Latest Articles from JUCS - Journal of Universal Computer Science Latest 100 Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Fri, 29 Mar 2024 08:27:11 +0200 Pensoft FeedCreator https://lib.jucs.org/i/logo.jpg Latest Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Image Filtering Techniques for Object Recognition in Autonomous Vehicles https://lib.jucs.org/article/102428/ JUCS - Journal of Universal Computer Science 30(1): 49-84

DOI: 10.3897/jucs.102428

Authors: Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, Jean-Philippe Georges

Abstract: The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.

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Research Article Sun, 28 Jan 2024 16:00:04 +0200
What is the Consumer Attitude toward Healthcare Services? A Transfer Learning Approach for Detecting Emotions from Consumer Feedback https://lib.jucs.org/article/104093/ JUCS - Journal of Universal Computer Science 30(1): 3-24

DOI: 10.3897/jucs.104093

Authors: Bashar Alshouha, Jesus Serrano-Guerrero, David Elizondo, Francisco P. Romero, Jose A. Olivas

Abstract: The capability of offering patient-centered healthcare services involves knowing the consumer needs. Many of these needs can be conveyed through opinions about services that can be found on social networks. The consumers/patients can express their complains, satisfaction, frustration, etc. in terms of feelings and emotions toward those services; for that reason, it is pivotal to accurately detect them. There are many recent techniques to detect sentiments or emotions, but one of the most promising is transfer learning. This allows adapting a model originally trained for a task to a different one by fine-tuning. Following this idea, the primary objective of this research is to study whether several pre-trained language models can be adapted to a task such as patient emotion detection in an efficient manner. For this purpose, seven clinical and biomedical pre-trained models and four domain-general models have been adapted to detect multiple emotions. These models have been tuned using a dataset consisting of real patient opinions which convey several emotions per opinion. The experiments carried out state the domain-specific pre-trained models outperform the domain-general ones. Particularly, Clinical-Longformer obtained the best scores, 98.18% and 95.82% in terms of accuracy and F1-score, respectively. Analyzing the patient feedback available on social networks may provide valuable knowledge about consumer sentiments and emotions, especially for healthcare managers. This information can be very interesting for purposes such as assessing the quality of healthcare services or designing patient-centered services.

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Research Article Sun, 28 Jan 2024 16:00:02 +0200
Wireless Sensor Network Coverage Optimization for Internet of Things https://lib.jucs.org/article/103738/ JUCS - Journal of Universal Computer Science 29(12): 1535-1553

DOI: 10.3897/jucs.103738

Authors: Yunwu Xu, Yan Li

Abstract: The objective of this work is to improve the existing Wireless Sensor Network coverage optimization method. The pigeon-inspired optimization algorithm was first evaluated, and its shortcomings were noted. The pigeon-inspired optimization method was then enhanced with the good point set, Yin-Yang optimization algorithm, and opposition-based learning. To test the improved algorithm, five representative standard functions were chosen: sphere function (f1), Rosenbrock function (f2), Levy function (f3), Schwefel function (f4), and Levy function N.13 (f5). The algorithm's speed of convergence may be determined by the first two functions, which are unimodal. The final three functions, which are multimodal, can extract several local optimal values from the local optimum. In comparison with other known algorithms, the improved Yin-Yang PIO algorithm showed the highest optimization accuracy and stability. Three sets of experiments were performed to optimize the WSN coverage with different parameters. The first series of experiments suggest that Yin–Yang PIO has the best optimization effect, with a coverage rate of 99.51% (10.22% higher with PIO and 6.41% higher compared with PSO). The second and third series of experiments show that Yin-Yang PIO significantly increased the WSN coverage ratio, up to 99.9%. The algorithm can be applied to optimize WSN coverage in various environments. Future research can extend the research scope to include other optimization problems in IoT.

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Research Article Thu, 28 Dec 2023 08:00:07 +0200
Artificial Intelligence as Catalyst for the Tourism Sector: A Literature Review https://lib.jucs.org/article/101550/ JUCS - Journal of Universal Computer Science 29(12): 1439-1460

DOI: 10.3897/jucs.101550

Authors: Anita Herrera, Ángel Arroyo, Alfredo Jiménez, Álvaro Herrero

Abstract: The analysis of Artificial Intelligence techniques and models used in the tourism sector provides insightful information for the management and innovation of this industry. In this paper, we conduct a comprehensive review of the different techniques and models, in regards to Artificial Intelligence when applied to the tourism industry. Specifically, we present a categorization of Artificial Intelligence applications used in different areas of tourism. The results allow to recognize valid studies and useful tools for the activation and growth of the tourism sector, an industry that represents a significant increase in the Gross Domestic Product of various economies and supports the development of life conditions for their inhabitants. Artificial Intelligence applications generate more personalized travel experiences, improve the efficiency of tourism services and strengthen the tourism competitiveness of the destination.

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Research Article Thu, 28 Dec 2023 08:00:03 +0200
Hybrid Classification Model for Emotion Prediction from EEG Signals: A Comparative Study https://lib.jucs.org/article/99542/ JUCS - Journal of Universal Computer Science 29(12): 1424-1438

DOI: 10.3897/jucs.99542

Authors: F. Kebire Bardak, M. Nuri Seyman, Feyzullah Temurtaş

Abstract: This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropriate features for classification. The three parallel ANFIS structures were trained using the selected features as input vectors, and the outputs of these models were combined to obtain a new feature vector. This feature vector was then used as the input to the adaptive network, which produced the output of emotion prediction. In addition, it is evaluated the accuracy of the network trained using only the first features of the dataset. The hybrid structure was designed to enhance the system's performance, and the best accuracy result of 96.51% was achieved using the ANFIS-ANFIS model. Overall, this study provides a promising approach for emotion classification based on EEG signals.

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Research Article Thu, 28 Dec 2023 08:00:02 +0200
Towards a Traceable Data Model Accommodating Bounded Uncertainty for DST Based Computation of BRCA1/2 Mutation Probability With Age https://lib.jucs.org/article/112797/ JUCS - Journal of Universal Computer Science 29(11): 1361-1384

DOI: 10.3897/jucs.112797

Authors: Lorenz Gillner, Ekaterina Auer

Abstract: In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

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Research Article Tue, 28 Nov 2023 18:00:07 +0200
Deep Random Forest and AraBert for Hate Speech Detection from Arabic Tweets https://lib.jucs.org/article/112604/ JUCS - Journal of Universal Computer Science 29(11): 1319-1335

DOI: 10.3897/jucs.112604

Authors: Kheir Eddine Daouadi, Yaakoub Boualleg, Oussama Guehairia

Abstract: Nowadays, hate speech detection from Arabic tweets attracts the attention of many researchers. Numerous systems and techniques have been proposed to address this classification challenge. Nonetheless, three major limits persist: the use of deep learning models with an excess of hyperparameters, the reliance on hand-crafted features, and the requirement for a huge amount of training data to achieve satisfactory performance. In this study, we propose Contextual Deep Random Forest (CDRF), a hate speech detection approach that combines contextual embedding and Deep Random Forest. From the experimental findings, the Arabic contextual embedding model proves to be highly effective in hate speech detection, outperforming the static embedding models. Additionally, we prove that the proposed CDRF significantly enhances the performance of Arabic hate speech classification.

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Research Article Tue, 28 Nov 2023 18:00:05 +0200
Control of a Spherical Robot Rolling Over Irregular Surfaces https://lib.jucs.org/article/89703/ JUCS - Journal of Universal Computer Science 29(10): 1198-1216

DOI: 10.3897/jucs.89703

Authors: Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza

Abstract: Pendulum-Driven Spherical Robots are a type of spherical robot whose motion is achieved by controlling two motors for longitudinal and lateral motion. This configuration makes the robot a non-holonomic system, which impedes it from navigating directly towards a target. In addition, controlling its motion on inclined irregular surfaces is also an issue that has not received much attention. In this work, we addressed these two issues by proposing a methodology to control both motors using PID controllers. However, we propose tuning the controller’s gains using stochastic signals for the longitudinal controller because by varying the motor’s torque, the robot is more susceptible to destabilization in combination with a classical gain tuning methodology for the second controller. Our results indicate that this enables the robot to perform motion on inclined irregular surfaces. We also propose using semicircular trajectories to plan the robot’s motion to reach a target successfully even when moving on inclined irregular surfaces. We have carried out experiments in the Webots simulator, showing that our approach does not overshoot while reaching a settling time of almost 0. These results outperform the Ziegler-Nichols PID controller.

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Research Article Sat, 28 Oct 2023 18:00:06 +0300
PlantKViT: A Combination Model of Vision Transformer and KNN for Forest Plants Classification https://lib.jucs.org/article/94657/ JUCS - Journal of Universal Computer Science 29(9): 1069-1089

DOI: 10.3897/jucs.94657

Authors: Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, Pham Thi Kim Thoa

Abstract: The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.

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Research Article Thu, 28 Sep 2023 08:00:06 +0300
Single-case learning analytics: Feasibility of a human-centered analytics approach to support doctoral education https://lib.jucs.org/article/94067/ JUCS - Journal of Universal Computer Science 29(9): 1033-1068

DOI: 10.3897/jucs.94067

Authors: Luis P. Prieto, Gerti Pishtari, Yannis Dimitriadis, María Jesús Rodríguez-Triana, Tobias Ley, Paula Odriozola-González

Abstract: Recent advances in machine learning and natural language processing have the potential to transform human activity in many domains. The field of learning analytics has applied these techniques successfully to many areas of education but has not been able to permeate others, such as doctoral education. Indeed, doctoral education remains an under-researched area with widespread problems (high dropout rates, low mental well-being) and lacks technological support beyond very specialized tasks. The inherent uniqueness of the doctoral journey may help explain the lack of generalized solutions (technological or otherwise) to these challenges. We propose a novel approach to apply the aforementioned advances in computation to support doctoral education. Single-case learning analytics defines a process in which doctoral students, researchers, and computational elements collaborate to extract insights about a single (doctoral) learner's experience and learning process. The feasibility and added value of this approach are demonstrated using an authentic dataset collected by nine doctoral students over a period of at least two months. The insights from this exploratory proof-of-concept serve to spark a research agenda for future technological support of doctoral education, which is aligned with recent calls for more human-centred approaches to designing and implementing learning analytics technologies.

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Research Article Thu, 28 Sep 2023 08:00:05 +0300
Robust Authentication Analysis of Copyright Images through Deep Hashing Models with Self-supervision https://lib.jucs.org/article/98824/ JUCS - Journal of Universal Computer Science 29(8): 938-958

DOI: 10.3897/jucs.98824

Authors: Jaeyoung Yang, Sooin Kim, Sangwoo Lee, Won-gyum Kim, Donghoon Kim, Doosung Hwang

Abstract: The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make image authentication extremely difficult. The aim of this paper is to establish a binary classification method for determining copyright images from copyright-free ones. A deep hashing model is introduced for an image authentication system, which uses deep learning-based perceptual hashing. Hash codes from a deep hashing model trained with a copyright image dataset are used to identify images. The deep learning model is able to learn features that represent the implicit meaning or structural information of an image. The copyright dataset, which lacks class labels, is trained with deep hashing models with self-supervision. The proposed model is based on an autoencoder or variational autoencoder model and is improved by including convolutional filters, residual blocks, and vision transformer blocks. Experimental results show that the proposed model performs a one-to-one mapping with most stored images and can retrieve related images using image features in hash collisions. The model can find the query image among the top 5 images with comparable hash codes. The results indicate that the proposed deep hashing approach is robust and applicable.

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Research Article Mon, 28 Aug 2023 18:00:06 +0300
Solving Restricted Preemptive Scheduling on Parallel Machines with SAT and PMS https://lib.jucs.org/article/97743/ JUCS - Journal of Universal Computer Science 29(8): 911-937

DOI: 10.3897/jucs.97743

Authors: Xiaojuan Liao, Hui Zhang, Miyuki Koshimura, Rong Huang, Fagen Li

Abstract: Restricted preemption plays a crucial role in reducing total completion time while controlling preemption overhead. A typical version of restricted preemptive models is k-restricted preemptive scheduling, where preemption is only allowed after a task has been continuously processed for at least k units of time. Though solving this problem of minimizing the makespan on parallel machines is NP-hard in general, it is of vital importance to obtain the optimal solution for small-sized problems, as well as for evaluation of heuristics. This paper proposes optimal strategies to the aforementioned problem. Motivated by the dramatic speed-up of Boolean Satisfiability (SAT) solvers, we make the first step towards a study of applying a SAT solver to the k-restricted scheduling problem. We set out to encode the scheduling problem into propositional Boolean logic and determine the optimal makespan by repeatedly calling an off-the-shelf SAT solver. Moreover, we move one step further by encoding the problem into Partial Maximum Satisfiability (PMS), which is an optimized version of SAT, so that the explicit successive calls of the solver can be eliminated. The optimal makespan of the problem and the performance of the proposed methods are studied experimentally. Furthermore, an existing heuristic algorithm is evaluated by the optimization methods.

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Research Article Mon, 28 Aug 2023 18:00:05 +0300
A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things   https://lib.jucs.org/article/96377/ JUCS - Journal of Universal Computer Science 29(8): 838-865

DOI: 10.3897/jucs.96377

Authors: Ahmad Abusukhon, Ala Al-Fuqaha, Belal Hawashin

Abstract: Water-pipeline leakage is one of the most common problems that depletes water supplies. Countries like Jordan, which are really experiencing a water deficit, are particularly concerned about this issue. The lack of monitoring tools makes the underground water-pipeline leakage a challenge since the pipelines are invisible. Besides, reducing the amount of time needed to precisely detect and locate the leak is another challenge. If not reduced, the aforementioned element has an effect on cost. A small broken water distribution line costs $64,000 per year. In Jordan, water leakage costs $1.7 million. This expense can be significantly decreased using an effective early water leak detection system. In this paper, we proposed an efficient internet of things system for detecting water-pipeline leakage based on a shielded pipeline, a NodeMCU, a soil moisture sensor, and the Firebase database. We created a baseline system, and then we tested and evaluated the proposed system when various types of soil are used. Furthermore, this paper compared several strategies offered for detecting water-pipeline leaking including the proposed system. The results showed that the proposed system reduced the time required for detecting water-pipeline leakage by 70% and the system hardware cost by 83% compared with the earlier work. It was difficult to compare the total cost of the proposed system with the total cost of previous works since the total cost is not calculated in their systems. Besides, in this paper, we proposed an IoT system for securing the underground water pipelines from adversaries.

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Research Article Mon, 28 Aug 2023 18:00:02 +0300
Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images https://lib.jucs.org/article/93498/ JUCS - Journal of Universal Computer Science 29(7): 759-804

DOI: 10.3897/jucs.93498

Authors: Muath Sabha, Thaer Thaher, Marwa M. Emam

Abstract: The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds.

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Research Article Fri, 28 Jul 2023 16:00:06 +0300
Intelligent Vision Based Decision Making System for Aviation Accidents and Incidents https://lib.jucs.org/article/96013/ JUCS - Journal of Universal Computer Science 29(7): 718-737

DOI: 10.3897/jucs.96013

Authors: Monika Lamba, Seema Verma, Pardeep Kumar

Abstract: Safety has become the primary concern for the air transportation system nowadays primarily due to increasing air traffic throughout the world. Various regulatory bodies have been maintaining enormous amount of aviation accidental data repositories. This past data is highly complex because of its many temporal and geographical components along with multiple variables. To be able to analyze this past data, there is always a need of user friendly and GUI based System. This article has proposed an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents dataset. The proposed visual query-based model is capable to analyse the major factors like flight phases, human factors, weather conditions and faulty components in particular aircraft models which are responsible for those unsafe events and may claim life of many passengers who are traveling and crew personnels. This model enables the users to express “what” visuals should be created instead of “how” to create them. Various case studies conducted through visual queries have proved that the system will be highly able to improve situational awareness regarding flight conditions to the crew members and air traffic controllers along with aviation authorities so that they are able to take timely decisions and deciding on what kind of training staff members need to reduce the consequences of such accidents and incidents.

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Research Article Fri, 28 Jul 2023 16:00:04 +0300
OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems https://lib.jucs.org/article/96878/ JUCS - Journal of Universal Computer Science 29(6): 627-646

DOI: 10.3897/jucs.96878

Authors: José L. López Ruiz, Ángeles Verdejo Espinosa, Alicia Montoro Lendínez, Macarena Espinilla Estévez

Abstract: Nowadays, it is becoming increasingly important to understand the multiple configuration factors of BLE anchors in indoor location systems. This task becomes particularly crucial in the context of activity recognition in multi-occupancy smart environments. Knowing the impact of the configuration of BLE anchors in an indoor location system allows us to distinguish the interactions performed by each inhabitant in a smart environment according to their proximity to each sensor. This paper proposes a new methodology, OBLEA, that determines the optimisation of Bluetooth Low Energy (BLE) anchors in indoor location systems, considering multiple BLE variables to increase flexibility and facilitate transferability to other environments. Concretely, we present a model based on a data-driven approach that considers configurations to obtain the best performing configuration with a minimum number of anchors. This methodology includes a flexible framework for the indoor space, the architecture to be deployed, which considers the RSSI value of the BLE anchors, and finally, optimisation and inference for indoor location. As a case study, OBLEA is applied to determine the location of ageing inhabitants in a nursing home in Alcaudete, Jaén (Spain). Results show the extracted knowledge related to the optimisation of BLE anchors involved in the case study.

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Research Article Wed, 28 Jun 2023 12:00:06 +0300
Developed Models Based on Transfer Learning for Improving Fake News Predictions https://lib.jucs.org/article/94081/ JUCS - Journal of Universal Computer Science 29(5): 491-507

DOI: 10.3897/jucs.94081

Authors: Tahseen A. Wotaifi, Ban N. Dhannoon

Abstract: In conjunction with the global concern regarding the spread of fake news on social media, there is a large flow of research to address this phenomenon. The wide growth in social media and online forums has made it easy for legitimate news to merge with comprehensive misleading news, negatively affecting people’s perceptions and misleading them. As such, this study aims to use deep learning, pre-trained models, and machine learning to predict Arabic and English fake news based on three public and available datasets: the Fake-or-Real dataset, the AraNews dataset, and the Sentimental LIAR dataset. Based on GloVe (Global Vectors) and FastText pre-trained models, A hybrid network has been proposed to improve the prediction of fake news. In this proposed network, CNN (Convolution Neural Network) was used to identify the most important features. In contrast, BiGRU (Bidirectional Gated Recurrent Unit) was used to measure the long-term dependency of sequences. Finally, multi-layer perceptron (MLP) is applied to classify the article news as fake or real. On the other hand, an Improved Random Forest Model is built based on the embedding values extracted from BERT (Bidirectional Encoder Representations from Transformers) pre-trained model and the relevant speaker-based features. These relevant features are identified by a fuzzy model based on feature selection methods. Accuracy was used as a measure of the quality of our proposed models, whereby the prediction accuracy reached 0.9935, 0.9473, and 0.7481 for the Fake-or-Real dataset, AraNews dataset, and Sentimental LAIR dataset respectively. The proposed models showed a significant improvement in the accuracy of predicting Arabic and English fake news compared to previous studies that used the same datasets.

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Research Article Sun, 28 May 2023 18:00:06 +0300
Automated Classification of Cell Level of HEp-2 Microscopic Images Using Deep Convolutional Neural Networks-Based Diameter Distance Features https://lib.jucs.org/article/96293/ JUCS - Journal of Universal Computer Science 29(5): 432-445

DOI: 10.3897/jucs.96293

Authors: Mitchell Jensen, Khamael Al-Dulaimi, Khairiyah Saeed Abduljabbar, Jasmine Banks

Abstract: To identify autoimmune diseases in humans, analysis of HEp-2 staining patterns at cell level is the gold standard for clinical practice research communities. An automated procedure is a complicated task due to variations in cell densities, sizes, shapes and patterns, overfitting of features, large-scale data volume, stained cells and poor quality of images. Several machine learning methods that analyse and classify HEp-2 cell microscope images currently exist. However, accuracy is still not at the level required for medical applications and computer aided diagnosis due to those challenges. The purpose of this work to automate classification procedure of HEp-2 stained cells from microscopic images and improve the accuracy of computer aided diagnosis. This work proposes Deep Convolutional Neural Networks (DCNNs) technique to classify HEp-2 cell patterns at cell level into six classes based on employing the level-set method via edge detection technique to segment HEp-2 cell shape. The DCNNs are designed to identify cell-shape and fundamental distance features related with HEp-2 cell types. This paper is investigated the effectiveness of our proposed method over benchmarked dataset. The result shows that the proposed method is highly superior comparing with other methods in benchmarked dataset and state-of-the-art methods. The result demonstrates that the proposed method has an excellent adaptability across variations in cell densities, sizes, shapes and patterns, overfitting features, large-scale data volume, and stained cells under different lab environments. The accurate classification of HEp-2 staining pattern at cell level helps increasing the accuracy of computer aided diagnosis for diagnosis process in the future.

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Research Article Sun, 28 May 2023 18:00:03 +0300
Human Mobility Prediction with Region-based Flows and Road Traffic Data https://lib.jucs.org/article/94514/ JUCS - Journal of Universal Computer Science 29(4): 374-396

DOI: 10.3897/jucs.94514

Authors: Fernando Terroso-Saenz, Andres Muñoz

Abstract: Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems.

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Research Article Fri, 28 Apr 2023 12:00:05 +0300
Application of Electronic Nose and Eye Systems for Detection of Adulteration in Olive Oil based on Chemometrics and Optimization Approaches https://lib.jucs.org/article/90346/ JUCS - Journal of Universal Computer Science 29(4): 300-325

DOI: 10.3897/jucs.90346

Authors: Seyedeh Mahsa Mirhoseini-Moghaddam, Mohammad Reza Yamaghani, Adel Bakhshipour

Abstract: In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses.

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Research Article Fri, 28 Apr 2023 12:00:02 +0300
Behavioral and Psychophysiological Measures of Engagement During Dynamic Difficulty Adjustment in Immersive Virtual Reality https://lib.jucs.org/article/89412/ JUCS - Journal of Universal Computer Science 29(1): 16-33

DOI: 10.3897/jucs.89412

Authors: Oscar I Caldas, Mauricio Mauledoux, Oscar F Aviles, Carlos Rodriguez Guerrero

Abstract: Dynamically Difficulty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the effect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to find evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed significant changes over time during DDA and grew until hitting a plateau, i.e., at the subject's maximum performance. Moreover, some of the features extracted from the signals showed significant differences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between different experimental conditions in VR.

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Research Article Sat, 28 Jan 2023 10:30:00 +0200
EntailClass: A Classification Approach to EntailSum and End-to-End Document Extraction, Identification, and Evaluation https://lib.jucs.org/article/84647/ JUCS - Journal of Universal Computer Science 29(1): 3-15

DOI: 10.3897/jucs.84647

Authors: Purvaja Balaji, Helena Merker, Amar Gupta

Abstract: The novelty of zero-shot text classification can address the fundamental challenge of the lack of labeled training data. With the current plethora of multidisciplinary, unstandardized text data, scalable classification models favor unsupervised methods over their supervised counterparts. Overall, the aim is to automate the labelling of each sentence in an input document consisting of section titles and section text. We propose an end-to-end pipeline that includes a document parser, a text classification model called EntailClass, and finally an evaluator to determine balanced accuracy. The suggested pipeline employs a zero-shot approach to classify text within any desired set of aspects. Moreover, text sentences are paired with their section titles and chronological order is maintained within sentences of the same aspect. The proposed automated, three-step pipeline represents a step towards solving the challenge of text classification without the need for an individual dataset for each aspect. It also offers the potential for seamless integration into existing workflows. This zero-shot, generalizable pipeline has achieved 87.2% accuracy and outperformed other state-of-the-art models when applied to supervisory documents.

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Research Article Sat, 28 Jan 2023 10:30:00 +0200
Automated video game parameter tuning with XVGDL+ https://lib.jucs.org/article/75357/ JUCS - Journal of Universal Computer Science 28(12): 1282-1311

DOI: 10.3897/jucs.75357

Authors: Jorge Ruiz Quiñones, Antonio J. Fernández Leiva

Abstract: Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this is commonly an iterative process in which play-testing is required as well as human interaction for gathering all important information to improve and tune the game components’ specification. In this paper, a mechanism is proposed to accelerate this process and reduce significantly the costs of it, contributing with a solution to perform the game parameter tuning and game balancing using search algorithms and artificial intelligence (AI) techniques. The process is executed in a fully automated way, and just requires a game specification written in a particular video game description language. Automated play-testing, and game’s feedback information analysis, are related to perform game parameters’ tuning and balancing, leading to offer a solution for the problem of optimizing a video game specification. Recently, XVGDL, a new language for specifying video games which is based on the eXtensible Markup Language (XML), has been presented. This paper uses XVGDL+, an extension of this lan- guage that incorporates new components to specify, within the video game specification, desirable goals or requirements to be evaluated after each game execution. A prototypical implementation of a Game Engine (termed XGE+) was also presented. This game engine not only enables the execution of an XVGDL+ game specification but also provides feedback information once the game has finished.The paper demonstrates that the combination of XVGDL+ with XGE+ offers a powerful mechanism for helping solving game AI research problems, in this case, the game tuning of video game parameters, with respect to initial optimization goals. These goals, as one of the particularities of the proposal presented here, are included within the game specification, minimizing the input of the process.As a practical proof of it, two experiments have been conducted to optimize a game specification written in XVGDL via a hill climbing local search method, in a fully automated way.

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Research Article Wed, 28 Dec 2022 10:00:00 +0200
Challenges of ubiquitous and wearable solutions to address active ageing in the Andalusian community https://lib.jucs.org/article/86891/ JUCS - Journal of Universal Computer Science 28(11): 1221-1249

DOI: 10.3897/jucs.86891

Authors: Aurora Polo-Rodríguez, Pietro Dionisio, Francesco Agnoloni, Ana Perandrés Gómez, Cristiano Paggetti, Lucía González López, Alfonso Cruz Lendínez, Macarena Espinilla-Estévez, Javier Medina-Quero

Abstract: Active ageing is a multidimensional process for achieving the potential quality of life and meaning in the life cycle. In the context of the Andalusian region in Spain, where the majority of the population is over 60 years old and lives in rural areas, it has become a key challenge. That is why the European projects within the Framework Programme for Research and Innovation, such as Pharaon - Pilots for Active and Healthy Ageing, promote technologies adapted by and for our elders. In the case of the Andalusian pilot, part of this project, we have selected a social network adapted to them, enabling them to communicate with the community at home and share their experiences. In addition, to improve their physical fitness, a device to count active minutes and steps is included, which provides users and caregivers with a visible and objective metric of daily health status. The technology has been evaluated following a well-defined methodology, which is described in this work to promote the deployment of technology in large-scale pilots. A specific architecture (Information System for Active Ageing in Andalusia - ISA3) and the components evaluated within a common ecosystem (Pharaon Project) are presented.

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Research Article Mon, 28 Nov 2022 10:00:00 +0200
Learning Behavior Analysis to Identify Learner’s Learning Style based on Machine Learning Techniques https://lib.jucs.org/article/81518/ JUCS - Journal of Universal Computer Science 28(11): 1193-1220

DOI: 10.3897/jucs.81518

Authors: Zohra Mehenaoui, Yacine Lafifi, Layachi Zemmouri

Abstract: Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.

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Research Article Mon, 28 Nov 2022 10:00:00 +0200
Climatological parameters estimation based on artificial intelligence techniques with particle swarm optimization and deep neural networks https://lib.jucs.org/article/82370/ JUCS - Journal of Universal Computer Science 28(10): 1108-1133

DOI: 10.3897/jucs.82370

Authors: Sercan Yalçın, Musa Eşit, Mehmet İshak Yuce

Abstract: Climate forecasting plays an important role for human life in many areas such as water management, agriculture, natural hazards including drought and flood, tourism, business, and regional investment. Estimating these data is a difficult task as the time series climate parameter values vary monthly and seasonally. Therefore, predicting climate parameters based on learning and artificial intelligence is important to long-term efficient results in these fields. For this purpose, in this study, a time-series based Long Short-Term Memory (LSTM) deep neural network is proposed to predict future climate in Çankırı and Adıyaman cities in Turkey. With the help of this network, the average temperature, relative humidity, and precipitation values, which are known as the most effective climate parameters, have been estimated. An improved Particle Swarm Optimization (PSO) technique is also proposed to optimize input weight values of the LSTM deep network, and reduce the estimation errors. The proposed algorithm is compared with deep models of LSTM variants based on Root Mean Square Error (RMSE), Mean Absolute Deviation (MADE), and Mean Absolute Percentage Error (MAPE) metrics. The proposed adaptive LSTM-PSO and non-adaptive LSTM-PSO models achieved at RMSE 0.98 and 1.05 for temperature, 1.19 and 1.27 for relative humidity, and 4.21 and 4.67 for precipitation estimation, respectively. The RMSE is %7 lower with the proposed adaptive LSTM-PSO method than proposed non-adaptive LSTM-PSO method.

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Research Article Fri, 28 Oct 2022 10:30:00 +0300
Improving Malaria Detection Using L1 Regularization Neural Network https://lib.jucs.org/article/81681/ JUCS - Journal of Universal Computer Science 28(10): 1087-1107

DOI: 10.3897/jucs.81681

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

Abstract: Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.

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Research Article Fri, 28 Oct 2022 10:30:00 +0300
Towards more trustworthy predictions: A hybrid evidential movie recommender system https://lib.jucs.org/article/79777/ JUCS - Journal of Universal Computer Science 28(10): 1003-1029

DOI: 10.3897/jucs.79777

Authors: Raoua Abdelkhalek, Imen Boukhris, Zied Elouedi

Abstract: Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.

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Research Article Fri, 28 Oct 2022 10:30:00 +0300
A Novel Image Super-Resolution Reconstruction Framework Using the AI Technique of Dual Generator Generative Adversarial Network (GAN) https://lib.jucs.org/article/94134/ JUCS - Journal of Universal Computer Science 28(9): 967-983

DOI: 10.3897/jucs.94134

Authors: Loveleen Kumar, Manish Jain

Abstract: Image superresolution (SR) is the process of enlarging and enhancing a low-resolution image. Image superresolution helps in industrial image enhancement, classification, detection, pattern recognition, surveillance, satellite imaging, medical diagnosis, image analytics, etc. It is of utmost importance to keep the features of the low-resolution image intact while enlarging and enhancing it. In this research paper, a framework is proposed that works in three phases and generates superresolution images while keeping low-resolution image features intact and reducing image blurring and artifacts. In the first phase, image enlargement is done, which enlarges the low-resolution image to the 2x/4x scale using two standard algorithms. The second phase enhances the image using an AI-empowered Generative adversarial network (GAN). We have used a GAN with dual generators and named it EffN-GAN (EfficientNet-GAN). Fusion is done in the last phase, wherein the final improved image is generated by fusing the enlarged image and GAN output image. The fusion phase helps in reducing the artifacts. We have used the DIV2K dataset to train the GAN and further tested the results on the images of Set5, Set14, B100, Urban100, Manga109 datasets with ground truth of size 224x224x3. The obtained results were compared with the state-of-the-art superresolution approach based on important image quality parameters, namely, Peak signal-to--to-noise ratio (PSNR), Structural similarity index (SSIM), Visual information fidelity (VIF) image quality parameters. The results show that the proposed framework for generating super-resolution images from 2x/4x resolution downgraded images improves the aforementioned mentioned image quality parameters significantly.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System https://lib.jucs.org/article/94132/ JUCS - Journal of Universal Computer Science 28(9): 916-929

DOI: 10.3897/jucs.94132

Authors: Vijay Krishna Pallaw, Kamred Udham Singh

Abstract: The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications https://lib.jucs.org/article/94127/ JUCS - Journal of Universal Computer Science 28(9): 882-897

DOI: 10.3897/jucs.94127

Authors: Lalan Kumar, Kamred Udham Singh

Abstract: Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
AI Empowered Big Data Analytics for Industrial Applications https://lib.jucs.org/article/94155/ JUCS - Journal of Universal Computer Science 28(9): 877-881

DOI: 10.3897/jucs.94155

Authors: V D Ambeth Kumar, Vijayakumar Varadarajan, Mukesh Kumar Gupta, Joel J. P. C Rodrigues, Neha Janu

Abstract: We proposed the idea of editing a special issue that would compile the fruitful research that resulted from the stimulating discussions that occurred during the workshop that was held during the 5th International Conference on Intelligent Computing, Chennai on 25th & 26th March 2022. The objective of this special issue is to call for high-quality papers covering the latest data analytic concepts and technologies of big data and artificial intelligence. This special issue serves as a forum for researchers across the globe to discuss their work and recent advances in this field. The best papers from Artificial intelligence and Big Data Analytics (BAM) in the domains of Product, Finance, Health, and Environment were invited, peer-reviewed. The best high-quality papers were selected based on the innovativeness and relevance of the theme. The amount of data being generated and stored in various fields such as education, energy, environment, healthcare, fraud detection, and traffic is increasing exponentially in the modern era of Big Data. Simultaneously, there is a significant paradigm shift in business and society worldwide due to rapid advancements in fields such as artificial intelligence, machine learning, deep learning, and data analytics. This creates significant challenges for decision-making and the potential for transformation in areas such as the economy, government, and industry. Artificial Intelligence tools, techniques, and technologies, in conjunction with Big Data, improve the predictive power of the systems created and allow the government, public, and private sectors to discover new patterns and trends, as well as improve public values such as accountability, safety, security, and transparency to enable better decision-making, policies, and governance. They also have a wide range of capabilities to perform complex tasks that humans cannot. They could be used to collect, organize, and analyze large, diverse data sets to discover patterns and trends that address a variety of problems related to the development of the economy, such as identifying new sources of revenue, expanding the customer base for business, product reviews, and promotion, disease prediction and prevention, climatic variation prediction, and the provision of energy solutions. The wide variety of subject areas discussed at the 5th International Conference on Intelligent Computing is reflected in the seven accepted papers presented in the following section.

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Editorial Wed, 28 Sep 2022 10:00:00 +0300
Affective Knowledge-enhanced Emotion Detection in Arabic Language: A Comparative Study https://lib.jucs.org/article/72590/ JUCS - Journal of Universal Computer Science 28(7): 733-757

DOI: 10.3897/jucs.72590

Authors: Jesus Serrano-Guerrero, Bashar Alshouha, Francisco P. Romero, Jose A. Olivas

Abstract: Online opinions/reviews contain a lot of sentiments and emotions that can be very useful, especially, for Internet suppliers which can know whether their services/products are meeting their customers’ expectations or not. To detect these sentiments and emotions, most applications resort to lexicon-based approaches. The major issue here is that most well-known emotion lexicons have been developed for English language; nevertheless, in other languages such as Arabic, there are fewer available tools, and many times, the quality of them is poor.The goal of this study is to compare the performance of two different types of algorithms, shallow machine learning-based and deep learning-based, when dealing with emotion detection in Arabic language. To improve the performance of the algorithms, two lexicons, which were originally developed in other languages and translated into Arabic language, have been used to add emotional features to different information models used to represent opinions. All approaches have been tested using the dataset SemEval 2018 Task 1: Affect in Tweets and the dataset LAMA+DINA. The semantic approaches outperform the classical algorithms, that is, the information provided by the lexicons clearly improves the results of the algorithms. Particularly, the BiLSTM algorithm outperforms the rest of the algorithms using word2vec. On the contrary to other languages, the best results were obtained using the NRC lexicon.

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Research Article Thu, 28 Jul 2022 10:00:00 +0300
Towards an Open Ontology for Renewable Resource Management in Smart Self-Sustainable Human Settlements https://lib.jucs.org/article/77793/ JUCS - Journal of Universal Computer Science 28(6): 620-647

DOI: 10.3897/jucs.77793

Authors: Igor Tomicic, Markus Schatten, Vadym Shkarupylo

Abstract: This paper proposes an open ontology for self-sustainable human settlements in an effort to set the common language for modelling self-sustainable systems and address the issues regarding heterogeneity of physical devices, protocols, software components, data and message formats and other relevant factors, which proved to be unavoidable in implementations of smart systems in the domain of self-sustainability, smart homes, Internet of things, smart energy management systems, demand side systems, and related areas of research and engineering. Although the existing body of research is showing significant results in related, specialized research areas, currently there is no common formal language available which would bring the diversity of such research efforts under a single umbrella and thus enhance and integrate such efforts, which is often pointed out by the researchers in related fields. This paper discuses self- sustainable systems and associated areas, argues the need for the ontology development, presents its scope, development methodology, domain’s architecture and metamodel, and finally the proposed ontology itself, implemented in an open OWL format.

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Research Article Tue, 28 Jun 2022 10:00:00 +0300
Supporting elderly’s independent living with a mobile robot platform https://lib.jucs.org/article/76579/ JUCS - Journal of Universal Computer Science 28(5): 475-498

DOI: 10.3897/jucs.76579

Authors: Natasa Koceska, Saso Koceski

Abstract: With the increased aging population, and declined support from the families, societies will need new tools to ensure the well-being of the elderly. Many of them would prefer living at home, but they will need help and assistance from someone. Technological innovations in the field of robotic systems can be used to enable independent living, to prolong the life of the elderly in their familiar home environments, to maintain the social connections by reducing social isolation and to improve the quality of life in general. In this paper, we present the design and validation of a low-cost mobile robot system that can assist elderly and professional caregivers in everyday activities. The robot structure and its control objectives are described in detail. The developed assistive telepresence robot was tested in simulation and experimentally. On field experiments were conducted in real environment, with potential end users, which is a major advantage of this study. The results of the evaluation were very satisfactory and have shown that participants can operate the robot safely and efficiently. The participants were very satisfied with the performance and features of the robot.

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Research Article Sat, 28 May 2022 10:00:00 +0300
The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping https://lib.jucs.org/article/69711/ JUCS - Journal of Universal Computer Science 28(4): 414-442

DOI: 10.3897/jucs.69711

Authors: Nahia Ugarte, Mikel Larrañaga, Ana Arruarte

Abstract: Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

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Research Article Thu, 28 Apr 2022 10:00:00 +0300
A Late Acceptance Hyper-Heuristic Approach for the Optimization Problem of Distributing Pilgrims over Mina Tents https://lib.jucs.org/article/72900/ JUCS - Journal of Universal Computer Science 28(4): 396-413

DOI: 10.3897/jucs.72900

Authors: Mohd Khaled Y. Shambour, Esam A. Khan

Abstract: About three million Muslims are traveling annually to Makkah in Saudi Arabia to perform the rituals of Hajj (i.e. the pilgrimage), the fifth pillar of Islam. It requires the pilgrims to move to several holy sites while performing the Hajj ritual, including Mina, Arafat, and Muzdalifah sites. However, pilgrims spend most of their time in prepared tent-camps at the Mina site during the days of Hajj. Among the challenges that the organizers face in the Hajj is the distribution of pilgrims over the camps of Mina while considering a range of constraints, which is considered a real-world optimization problem. This paper introduces a hyper-heuristic approach to optimize the distribution process of pilgrims over Mina tent-camps in an efficient manner, named the hyper-heuristic Mina tents distribution algorithm (HyMTDA). The proposed algorithm, iteratively, selects one heuristic among four predefined low-level heuristics to produce a new solution; thereafter the late move acceptance strategy is applied as a judgment to accept or reject the new solution. The performed simulations show that the proposed HyMTDA can effectively explore the search space and avoid falling into local minima during the iterations process. Moreover, comparisons show that HyMTDA outperforms other heuristic algorithms in the literature in terms of solution quality and convergence rate.

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Research Article Thu, 28 Apr 2022 10:00:00 +0300
TwitterBulletin: An Intelligent and Real-Time Automated News Categorization Tool for Twitter https://lib.jucs.org/article/69377/ JUCS - Journal of Universal Computer Science 28(4): 345-377

DOI: 10.3897/jucs.69377

Authors: Sedef Demirci, Seref Sagiroglu

Abstract: Social media platforms have become popular news sources thanks to their immense popularity and high speed of information dissemination. Using these platforms is essential for news organizations and journalists to track and discover news in digital journalism age. However, the abundance of meaningless data and the lack of organization on these platforms make it difficult to reach valuable news for journalists. In this paper, we create the first public dataset containing large number of real-world Turkish news tweets belonging to different news categories, to the best of our knowledge. We propose an artificial intelligence-based two-step approach to assist journalists for accessing the news shared by various sources on social media under the relevant categories like politics (elections, riots, etc.), health (pandemic, covid-19, etc.), etc. via a single platform by reducing the possibility of overlooking needed information. In the first step, we propose a machine learning based novel model for collecting and categorizing news posts on social media. We implement several traditional machine learning and deep learning based algorithms and evaluate their classification performance in terms of accuracy, precision, recall, and F1 score. In the second step, we develop a software tool, named TwitterBulletin, which automatically retrieves Turkish news tweets and groups them under news categories in real time by using the CNN classifier which achieves the best performance in the first step. The results show that the overall accuracy rate of TwitterBulletin is reasonably high and satisfactory despite the challenge of classifying short tweets.

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Research Article Thu, 28 Apr 2022 10:00:00 +0300
DeepFlood: A deep learning based flood detection framework using feature-level fusion of multi-sensor remote sensing images https://lib.jucs.org/article/80734/ JUCS - Journal of Universal Computer Science 28(3): 329-343

DOI: 10.3897/jucs.80734

Authors: A. Emily Jenifer, Sudha Natarajan

Abstract: Flooding is the most common natural disaster in many countries. Remote sensing images are very much useful in disaster monitoring. The different image modalities from different satellites provide varied information about the earth. The synergistic use of optical and radar data helps in precise flood detection. The central focus of this paper is to identify the flooded regions using a dual patch-based Fully Convolutional Network (FCN) for performing deep learning-based feature fusion. The learned features of FCNs trained independently with Synthetic Aperture Radar (SAR) and Multispectral (MS) images are concatenated to represent the flooding better. A random forest classifier is employed to identify the flood from the fused features. The information retrieved is very much valuable in undertaking necessary rescue efforts in flood-affected areas. The proposed network shows superior performance in flood detection on the images from the SEN12-FLOOD dataset with an accuracy as high as 94.17%.

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Research Article Mon, 28 Mar 2022 10:00:00 +0300
Water stress classification using Convolutional Deep Neural Networks https://lib.jucs.org/article/80733/ JUCS - Journal of Universal Computer Science 28(3): 311-328

DOI: 10.3897/jucs.80733

Authors: Lerina Aversano, Mario Luca Bernardi, Marta Cimitile

Abstract: In agriculture, given the global water scarcity, optimizing the irrigation system have become a key requisite of any semi-automatic irrigation scheduling system. Using efficient assessment methods for crop water stress allows reduced water consumption as well as improved quality and quantity of the production. The adoption of Neural Network can support the automatic in situ continuous monitoring and irrigation through the real-time classification of the plant water stress. This study proposes an end-to-end automatic irrigation system based on the adoption of Deep Neural Networks for the multinomial classification of tomato plants’ water stress based on thermal and optical aerial images. This paper proposes a novel approach that cover three important aspects: (i) joint usage of optical and thermal camera, captured by un-manned aerial vehicles (UAVs); (ii) strategies of image segmentation in both thermal imaging used to obtain images that can remove noise and parts not useful for classifying water stress; (iii) the adoption of deep pre-trained neural ensembles to perform effective classification of field water stress. Firstly, we used a multi-channel approach based on both thermal and optical images gathered by a drone to obtain a more robust and broad image extraction. Moreover, looking at the image processing, a segmentation and background removal step is performed to improve the image quality. Then, the proposed VGG-based architecture is designed as a combination of two different VGG instances (one for each channel). To validate the proposed approach a large real dataset is built. It is com- posed of 6000 images covering all the lifecycle of the tomato crops captured with a drone thermal and optical photocamera. Specifically, our approach, looking mainly at leafs and fruits status and patterns, is designed to be applied after the plants has been transplanted and have reached, at least, early growth stage (covering vegetative, flowering, friut-formation and mature fruiting stages).

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Research Article Mon, 28 Mar 2022 10:00:00 +0300
Solving the problem of scheduling the production process based on heuristic algorithms https://lib.jucs.org/article/80750/ JUCS - Journal of Universal Computer Science 28(3): 292-310

DOI: 10.3897/jucs.80750

Authors: Dagmara Łapczyńska, Konrad Łapczyński, Anna Burduk, Jose Machado

Abstract: The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

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Research Article Mon, 28 Mar 2022 10:00:00 +0300
A Spark Parallel Betweenness Centrality Computation and its Application to Community Detection Problems https://lib.jucs.org/article/80688/ JUCS - Journal of Universal Computer Science 28(2): 160-180

DOI: 10.3897/jucs.80688

Authors: Daniel Gomez González, Luis Llana Díaz, Cristóbal Pareja

Abstract: The Brandes algorithm has the lowest computational complexity for computing the betweenness centrality measures of all nodes or edges in a given graph. Its numerous applications make it one of the most used algorithms in social network analysis. In this work, we provide a parallel version of the algorithm implemented in Spark. The experimental results show that the parallel algorithm scales as the number of cores increases. Finally, we provide a version of the well-known community detection Girvan-Newman algorithm, based on the Spark version of Brandes algorithm.

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Research Article Mon, 28 Feb 2022 11:00:00 +0200
Middleware for the Internet of Things: a systematic literature review https://lib.jucs.org/article/71693/ JUCS - Journal of Universal Computer Science 28(1): 54-79

DOI: 10.3897/jucs.71693

Authors: Rodolfo Medeiros, Sílvio Fernandes, Paulo G. G Queiroz

Abstract: The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.

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Research Article Fri, 28 Jan 2022 10:30:00 +0200
Combined Use of Virtual Reality and a Chatbot Reduces Emotional Stress More Than Using Them Separately https://lib.jucs.org/article/77237/ JUCS - Journal of Universal Computer Science 27(12): 1371-1389

DOI: 10.3897/jucs.77237

Authors: Atsuko Matsumoto, Takeshi Kamita, Yukari Tawaratsumida, Ayako Nakamura, Harumi Fukuchimoto, Yuko Mitamura, Hiroko Suzuki, Tsunetsugu Munakata, Tomoo Inoue

Abstract: In recent years, various organizations, such as companies and governments, have been required to take measures for the mental health of their employees, and the importance of self-care for mental health by employees themselves has been increasing, as well as being supported by administrators, such as doctors and workplace managers. As a means of self-care of mental health that can be implemented by busy professionals during their workdays and daily lives, the Digital-SAT method has been developed to implement the stress-care process of the SAT method, a psychological counseling technique for resolving psychological stress problems, in a self-guided manner using digital media. To realize the Digital-SAT method, two issues need to be addressed: first, to obtain the same emotional stress reduction effect as the SAT method and, second, to ensure the continuous implementation of the Digital-SAT method. Previous studies have shown that applications (apps) using virtual reality are effective in solving the former issue, and an app using a chatbot can be effective in solving the latter. In this research, an intervention study was conducted to verify the effectiveness of combined use of the two apps to encourage continuous use, resulting in increased emotional stress reduction, with the aim of making it feasible in actual work environments. An intervention of four weeks of app use was conducted with 70 nurses working in two hospitals where measures for mental health due to emotional labour and overwork were required. The emotional stress reduction effects of the intervention were evaluated using psychological scales and blood pressure levels, and it was confirmed that combined use of apps was more effective than using them separately to practice the Digital-SAT method in an actual work environment.

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Research Article Tue, 28 Dec 2021 10:00:00 +0200
An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things https://lib.jucs.org/article/76983/ JUCS - Journal of Universal Computer Science 27(12): 1325-1346

DOI: 10.3897/jucs.76983

Authors: Abdelhalim Hadjadj, Khaled Halimi

Abstract: The integration of the Internet of Things (IoT) technology and artificial intelligence has become essential in many aspects of daily life since the expansion of the communications and information field. Healthcare is one area that urgently needs to benefit from these technologies to keep up with the dramatic evolution of communications for contemporary human life. IoT, through wearable devices, provides real-time data related to the measurement of a person’s vital signs of health. However, for this data to become more relevant and valuable, it needs to be linked to other domains. Public transport is a domain related to the daily activity of people who take advantage of the IoT to provide exemplary transport services whose quality of service can greatly affect people’s health. The integration of these two domains offers many benefits, especially when providing services adapted to passengers’ health status, making them safer and healthier. This paper proposes an approach based on an IoT architecture using Semantic Web technologies; it aims to integrate health monitoring in public transport, provide passengers with quality transport services, and ensure continuous health monitoring. The use of Semantic Web technologies overcomes the lack of interoperability due to the heterogeneity of data collected by different devices and generated by two different domains. An experimental study was conducted, and the proposed approach’s results were compared with those obtained by the evaluation of a physician. The results show that the approach is effective and should allow passengers to benefit from appropriate transport services that better match their health status.

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Research Article Tue, 28 Dec 2021 10:00:00 +0200
Designing, Realizing, Running, and Evaluating Virtual Museum – a Survey on Innovative Concepts and Technologies https://lib.jucs.org/article/77153/ JUCS - Journal of Universal Computer Science 27(12): 1275-1299

DOI: 10.3897/jucs.77153

Authors: Nelson Baloian, Daniel Biella, Wolfram Luther, José Pino, Daniel Sacher

Abstract: This paper presents a survey of innovative concepts and technologies involved in virtual museums (ViM) that shows their advantages and disadvantages in comparison with physical museums. We describe important lessons learned during the creation of three major virtual museums between 2010 and 2020 with partners at universities from Armenia, Germany, and Chile. Based on their categories and features, we distinguish between content-, communication- and collaboration-centric museums with a special focus on learning and co-curation. We give an overview of a generative approach to ViMs using the ViMCOX metadata format, the curator software suite ViMEDEAS, and a comprehensive validation and verification management. Theoretical considerations include exhibition design and new room concepts, positioning objects in their context, artwork authenticity, digital instances and rights management, distributed items, private museum and universal access, immersion, and tour and interaction design for people of all ages. As a result, this survey identifies different approaches and advocates for stakeholders’ collaboration throughout the life cycle in determining the ViM's direction and evolution, its concepts, collection type, and the technologies used with their requirements and evaluation methods. The paper ends with a brief perspective on the use of artificial intelligence in ViMs.

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Research Article Tue, 28 Dec 2021 10:00:00 +0200
J.UCS special issue on Challenges for Smart Environments – Human-Centered Computing, Data Science, and Ambient Intelligence. Smart Human-Centered Computing (volume 2) https://lib.jucs.org/article/76822/ JUCS - Journal of Universal Computer Science 27(12): 1272-1274

DOI: 10.3897/jucs.76822

Authors: Ashot Harutyunyan, Gregor Schiele

Abstract: Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied.The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those devoted mainly to human-computer interaction problematics, which were rigorously reviewed in three rounds and 6 papers nominated to be published.

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Editorial Tue, 28 Dec 2021 10:00:00 +0200
Leveraging multifaceted proximity measures among developers in predicting future collaborations to improve the social capital of software projects https://lib.jucs.org/article/76602/ JUCS - Journal of Universal Computer Science 27(11): 1240-1271

DOI: 10.3897/jucs.76602

Authors: Amit Kumar, Sonali Agarwal

Abstract: Social capital is an asset earned by people through their social connections. One of the motivations among developers to contribute to open source development and maintenance tasks is to earn social capital. Recent studies suggest that the social capital of the project has an impact on the sustained participation of the developers in open source software (OSS). One way to improve the social capital of the project is to help the developers in connecting with their peers. However, to the best of our knowledge, there is no prior research which attempts to predict future collaborations among developers and establish the significance of these collaborations on improving the social capital at the project level. To address this research gap, in this paper, we model the past collaborations among developers on version control system (VCS) and issue tracking system (ITS) as homogeneous and heterogeneous developer social network (DSN). Along with the novel path count based features, defined on proposed heterogeneous DSN, multifaceted proximity features are used to generate a feature set for machine learning classifiers. Our experiments performed on 5 popular open source projects (Spark, Kafka, Flink, WildFly, Hibernate) indicate that the proposed approach can predict the future collaborations among developers on both the platforms i.e. VCS as well as ITS with a significant accuracy (AUROC up to 0.85 and 0.9 for VCS and ITS respectively). A generic metric- recall of gain in social capital is proposed to investigate the efficacy of these predicted collaborations in improving the social capital of the project. We also concretised this metric on various measures of social capital and found that collaborations predicted by our approach have significant potential to improve the social capital at project level (e.g. Recall of gain in cohesion index up to 0.98 and Recall of gain in average godfather index up to 0.99 for VCS). We also showed that structure of collaboration network has an impact on the accuracy and usefulness of predicted collaborations. Since the past research suggests that many newcomers abandon the open source project due to social barriers which they face after joining the project, our research outcomes can be used to build the recommendation systems which might help to retain such developers by improving their social ties based on similar skills/interests.

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Research Article Sun, 28 Nov 2021 10:00:00 +0200
Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers     https://lib.jucs.org/article/76608/ JUCS - Journal of Universal Computer Science 27(11): 1152-1173

DOI: 10.3897/jucs.76608

Authors: Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Nicholas Kushmerick

Abstract: Effective root cause analysis (RCA) of performance issues in modern cloud environ- ments remains a hard problem. Traditional RCA tracks complex issues by their signatures known as problem incidents. Common approaches to incident discovery rely mainly on expertise of users who define environment-specific set of alerts and >target detection of problems through their occurrence in the monitoring system. Adequately modeling of all possible problem patterns for nowadays extremely sophisticated data center applications is a very complex task. It may result in alert/event storms including large numbers of non-indicative precautions. Thus, the crucial task for the incident-based RCA is reduction of redundant recommendations by prioritizing those events subject to importance/impact criteria or by deriving their meaningful groupings into separable situations. In this paper, we consider automation of incident discovery based on rule induction algorithms that retrieve conditions directly from monitoring datasets without consuming the sys- tem events. Rule-learning algorithms are very flexible and powerful for many regression and classification problems, with high-level explainability. Since annotated or labeled data sets are mostly unavailable in this area of technology, we discuss data self-labelling principles which allow transforming originally unsupervised learning tasks into classification problems with further application of rule induction methods to incident detection.

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Research Article Sun, 28 Nov 2021 10:00:00 +0200
Validation of e-Government Information Delivery Attributes: The Adoption of the Focus Group Method https://lib.jucs.org/article/66979/ JUCS - Journal of Universal Computer Science 27(10): 1069-1095

DOI: 10.3897/jucs.66979

Authors: José Monteiro, Maria Bernando, Mafalda Ferreira, Tânia Rocha

Abstract: In democratic countries, government websites became an important channel for interaction with the public administration in the last few years. Nevertheless, several issues have an impact on the way users access to content and information. Lack of accessibility and usability or, in the broad sense, lack of concern with user needs, can still be found in many government websites. To address the problem, a previous literature review on e-government information delivery attributes was performed. Based on this review, a large set of attributes related to quality was obtained to evaluate these dimensions in the context of e-government. The purpose of this study is to better understand which of these attributes are the most valued, in the users’ perspective, for evaluating content delivered by government websites. A qualitative approach was adopted, using Focus Group interviews as a strategy to obtain data and Thematic Analysis to analyze such data. The main results highlighted the attributes related to content delivery, interaction, and emotional aspects. User Experience, accessibility, and usability were prioritized by Focus Group participants.

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Research Article Thu, 28 Oct 2021 10:30:00 +0300
Safety Design Strategies in Highly Autonomous Drive Level 2 – Lateral Control Decomposition Concept https://lib.jucs.org/article/72314/ JUCS - Journal of Universal Computer Science 27(8): 811-829

DOI: 10.3897/jucs.72314

Authors: Svatopluk Stolfa, Jakub Stolfa, Petr Simonik, Tomas Mrovec, Tomas Harach

Abstract: The paper is based on an experimental study at VSB TUO Ostrava with a DEMOCAR vehicle that simulates a real car with sensor fusion concept and a vehicle gateway to send and coordinate commands to ECUs to realize and manage autonomous driving. In this experimental study of autonomous driving vehicles control, a HARA (Hazard and Risk Analysis, ISO 26262:2018) has been done on vehicle level and strategies have been defined and implemented to manage safety situations where the car lateral control shall be hand over to a driver when in HAD 2 mode. The issue is that the switching to safe state shall not be done immediately but the vehicle has to stay in safe driving mode – fail-operational up to 4 seconds until a driver can take over. The UECE and other relevant studies show that it can take up to 6 seconds if driver/operator is not in the flow (HAD 3) and up to the 2 seconds when driver is in the flow (HAD 1). The paper makes assumptions and proposals about vehicle lateral control strategy to ensure the smooth take- over of the car by driver and its impact on control software development architectures.

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Research Article Sat, 28 Aug 2021 10:00:00 +0300
Assembling the Web of Things and Microservices for the Management of Cyber-Physical Systems https://lib.jucs.org/article/70325/ JUCS - Journal of Universal Computer Science 27(7): 734-754

DOI: 10.3897/jucs.70325

Authors: Manel Mena, Javier Criado, Luis Iribarne, Antonio Corral

Abstract: Cyber-Physical Systems (CPS) and Internet of Things (IoT) devices are handled by numerous different protocols. The management and connection to those devices tend to create usability and integrability issues. This brings about the need for a solution capable of facilitating the communication between different platforms and devices. The Web of Things (WoT) describes interfaces and interaction patterns among things, thereby abstracting itself from the underlying protocols used to manage those things and their implementation strategies. This paper describes the concept of Digital Dice, an abstraction of IoT devices and CPS capable of leveraging the advantages of microservices architectures and inspired by the concept of Digital Twins. A Digital Dice is a servient system of the WoT domain that represents a device by the features of the device, hence different WoT description models result in different microservices related to the particular thing. The paper explores the definition of Digital Dices and the conversion between WoT Thing Description Models and Digital Dices and the architecture that sustains the system.

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Research Article Wed, 28 Jul 2021 10:00:00 +0300
Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods https://lib.jucs.org/article/68185/ JUCS - Journal of Universal Computer Science 27(6): 564-581

DOI: 10.3897/jucs.68185

Authors: Murat Firat, Derya Yiltas-Kaplan, Ruya Samli

Abstract: Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR.

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Research Article Mon, 28 Jun 2021 10:00:00 +0300
Emotional Aspects for Productive Dialogues in Computer-Supported Collaborative Learning: A Systematic Literature Review https://lib.jucs.org/article/66389/ JUCS - Journal of Universal Computer Science 27(3): 303-322

DOI: 10.3897/jucs.66389

Authors: Uyara Ferreira Silva, Deller James Ferreira

Abstract: This paper presents a systematic literature review of the literature on productive dialogues and emotional aspects in Computer-Supported Collaborative Learning (CSCL) and also presents emotional aspects used in debates with conflicting points of view in other contexts. Initially, more than 400 articles were catalogued, belonging mainly to the databases of Springer and Science Direct, not limited by years, because of very important works referenced until today. The findings reveal that in CSCL there is a neglect in relation to the emotional dimension, the results also show that there are negative emotional aspects that impair the motivation in the participation of students in collaborative activities. Empathy is seen as an alternative to conflict resolution in different contexts, in addition to collaborative learning, but it is rarely addressed in CSCL.

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Research Article Sun, 28 Mar 2021 17:00:00 +0300
Toward a Knowledge-based Personalised Recommender System for Mobile App Development https://lib.jucs.org/article/65096/ JUCS - Journal of Universal Computer Science 27(2): 208-229

DOI: 10.3897/jucs.65096

Authors: Bilal Abu-Salih, Hamad Alsawalqah, Basima Elshqeirat, Tomayess Issa, Pornpit Wongthongtham, Khadija Khalid Premi

Abstract: Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

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Research Article Sun, 28 Feb 2021 10:00:00 +0200
Communication architecture based on IoT technology to control and monitor pets feeding https://lib.jucs.org/article/65094/ JUCS - Journal of Universal Computer Science 27(2): 190-207

DOI: 10.3897/jucs.65094

Authors: Yadira Quiñonez, Carmen Lizarraga, Raquel Aguayo, David Arredondo

Abstract: Technology is currently a significant benchmark in any application area; science and technology have permitted the invention of tools and devices that simplify daily activities by developing software engineering applications that provide automated solutions. In this sense, this work proposes two architectures that allow communication between the electronic device and the mobile application remotely, using the GSM/GPRS communication services and the Twitter social network. This development aims to control dogs' feeding adequately and healthily, providing the ration of food a dog needs according to the daily energy requirements. A nutritional assessment has also been performed considering different factors such as the size, breed, and weight of the dog to calculate the daily ration of healthy and balanced food according to daily energy requirements. Essentially, the electronic device consists of two parts: on the one hand, the electronic design is formed with an Arduino board, a Sim900 module to send and receive text messages, and the ESP8266 Wi-Fi serial transceiver module, which allows establishing the internet connection to receive the tweet that users post, both modules permit remote communication with the device using the Arduino board. On the other hand, the mobile application developed on Android uses a standard design according to the Google material design guidelines, allowing the owner to feed, schedule the feeding, review the dog's food history, and receive alerts when the food is going to be finished.

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Research Article Sun, 28 Feb 2021 10:00:00 +0200
Knowledge Intensive Software Engineering Applications https://lib.jucs.org/article/65078/ JUCS - Journal of Universal Computer Science 27(2): 87-90

DOI: 10.3897/jucs.65078

Authors: Jezreel Mejía, Rafael Valencia-García, Giner Alor-Hernández, José A. Calvo-Manzano

Abstract: The use of Information and Communication Technologies (ICTs) has become a competitive strategy that allows organizations to position themselves within their market of action. In addition, the evolution, advancement and use of ICTs within any type of organization have created new domains of interest. In this context, Knowledge-intensive software engineering applications are becoming crucial in organizations to support their performance. Knowledge-based technologies provide a consistent and reliable basis to face the challenges for organization, manipulation and visualization of the data and knowledge, playing a crucial role as the technological basis of the development of a large number of information systems. In software engineering, it involves the integration of various knowledge sources that are in constant change.Knowledge-intensive software applications are becoming more significant because the domains of many software applications are inherently knowledge-intensive and this knowledge is often not explicitly dealt with in software development. This impedes maintenance and reuse. Moreover, it is generally known that developing software requires expertise and experience, which are currently also implicit and could be made more tangible and reusable using knowledge-based or related techniques. Furthermore, organizations have recognized that the software engineering applications are an optimal way for providing solutions, because it is a file that is constantly evolving due to the new challenges. Examples of approaches that are directly related to this tendency are data analysis, software architectures, knowledge engineering, ontologies, conceptual modelling, domain analysis and domain engineering, business rules, workflow management, human and cultural factors, to mention but a few. Therefore, tools and techniques are necessary to capture and process knowledge in order to facilitate subsequent development efforts, especially in the domain of software engineering.

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Editorial Sun, 28 Feb 2021 10:00:00 +0200
Weather Station IoT Educational Model Using Cloud Services https://lib.jucs.org/article/24151/ JUCS - Journal of Universal Computer Science 26(11): 1495-1512

DOI: 10.3897/jucs.2020.079

Authors: Ján Molnár, Simona Kirešová, Tibor Vince, Dobroslav Kováč, Patrik Jacko, Matej Bereš, Peter Hrabovský

Abstract: IoT technology is gaining more and more popularity in practice, as it collects, processes, evaluates and stores important measured data. The IoT is used every day in the work, in the home or smart houses or in public areas. It realizes the connectivity between real world and digital world which means, that it converts physical quantities of the real world in the form of analog signals into digital numbers stored in clauds. It is essential that students gain practical experience in the design and implementation of the IoT systems during their studies. The article first describes IoT issues and communication protocols used in IoT generally are closer described. Then the design and implementation of an educational model of IoT system - Weather station with the ThingSpeak cloud support is described. The created IoT model interconnects microcontroller programming, sensors and measuring, cloud API interfaces, MATLAB scripts which are useful to analyses the stored data, Windows and Android application developing.

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Research Article Sat, 28 Nov 2020 00:00:00 +0200
From Classical to Fuzzy Databases in a Production Enterprise https://lib.jucs.org/article/24135/ JUCS - Journal of Universal Computer Science 26(11): 1382-1401

DOI: 10.3897/jucs.2020.073

Authors: Izabela Rojek, Dariusz Mikołajewski, Piotr Kotlarz, Alžbeta Sapietová

Abstract: This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

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Research Article Sat, 28 Nov 2020 00:00:00 +0200
Reconstruction of Curve Networks from Unorganized Spatial Points https://lib.jucs.org/article/24120/ JUCS - Journal of Universal Computer Science 26(9): 1265-1280

DOI: 10.3897/jucs.2020.065

Authors: Shuangbu Wang, Yu Xia, Lihua You, Jianjun Zhang

Abstract: Curve network reconstruction from a set of unorganized points is an important problem in reverse engineering and computer graphics. In this paper, we propose an automatic method to extract curve segments and reconstruct curve networks from unorganized spatial points. Our proposed method divides reconstruction of curve networks into two steps: 1) detecting nodes of curve segments and 2) reconstructing curve segments. For detection of nodes of curve segments, we present a principal component analysis-based algorithm to obtain candidate nodes from unorganized spatial points and a Euclidean distance-based iterative algorithm to remove peripheral nodes and find the actual nodes. For reconstruction of curve segments, we propose an extraction algorithm to obtain the points on each of curve segments. We present quite a number of examples which use our proposed method to reconstruct curve networks from unorganized spatial points. The results demonstrate the effectiveness of our proposed method and its advantages of good automation and high reconstruction efficiency.

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Research Article Mon, 28 Sep 2020 00:00:00 +0300
Numerical Treatment of a Data Completion Problem in Heat Conduction Modelling https://lib.jucs.org/article/24112/ JUCS - Journal of Universal Computer Science 26(9): 1177-1188

DOI: 10.3897/jucs.2020.061

Authors: Augusto C. de Castro Barbosa, Carlos De Moura, Jhoab De Negreiros, J. Mesquita de Souza Aguiar

Abstract: This work deals with a question in the mathematical modelling for the temperature evolution in a bar, for a long time linked as an inverse problem. The onedimensional model is the parabolic partial differential equation ut = α uxx, known as the heat diffusion equation. The classic direct problem (DP) involves this equation coupled to a set of constraints: initial and boundary conditions, in such a way as to guarantee existence of a unique solution. The data completion (DC) problem hereby considered may be described as follows: the temperature at one of the bar extreme points is unknown but there is a fixed interior point where it may be measured, for all time. Finite difference algorithms (FDA) were tested to approximate the solution for such a problem. The important point to be emphasized is that FDA may show up distinct performances when applied to either DP or DC, which is due to the way the discrete variables follow up the mesh steps - advancing in time, for the first case, on the space direction, for the other.

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Research Article Mon, 28 Sep 2020 00:00:00 +0300
Evaluating Case Study and Action Research Reports: Real-world Research in Cybersecurity https://lib.jucs.org/article/24089/ JUCS - Journal of Universal Computer Science 26(7): 827-853

DOI: 10.3897/jucs.2020.045

Authors: Simon Vrhovec, Damjan Fujs, Luka Jelovčan, Anže Mihelič

Abstract: There is a growing number of scientific papers reporting on case studies and action research published each year. Consequently, evaluating the quality of pilling up research reports is becoming increasingly challenging. Several approaches for evaluation of quality of the scientific outputs exist however they appear to be fairly time-consuming and/or adapted for other research designs. In this paper, we propose a reasonably light-weight structure-based approach for evaluating case study and action research reports (SAE-CSAR) based on eight key parts of a real-world research report: research question, case description, data collection, data analysis, ethical considerations, results, discussion and limitations. To evaluate the feasibility of the proposed approach, we conducted a systematic literature survey of papers reporting on real-world cybersecurity research. A total of N = 102 research papers were evaluated. Results suggest that SAE-CSAR is useful and relatively efficient, and may offer a thought-provoking insight into the studied field. Although there is a positive trend for the inclusion of data collection, data analysis and research questions in papers, there is still room for improvement suggesting that the field of real-world cybersecurity research did not mature yet. The presence of a discussion in a paper appears to affect most its citation count. However, it seems that it is not uniformly accepted what a discussion should include. This paper explores this and other issues related to paper structure and provides guidance on how to improve the quality of research reports.

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Research Article Tue, 28 Jul 2020 00:00:00 +0300
Social Choice-based Explanations: An Approach to Enhancing Fairness and Consensus Aspects https://lib.jucs.org/article/24006/ JUCS - Journal of Universal Computer Science 26(3): 402-431

DOI: 10.3897/jucs.2020.021

Authors: Thi Ngoc Trang Tran, Muesluem Atas, Man Le, Ralph Samer, Martin Stettinger

Abstract: Explanations are integrated into recommender systems to give users an insight into the recommendation generation process. Compared to single-user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness, which helps to take into account as much as possible group members' preferences and consensus, which persuades group members to agree on a decision. In this paper, we proposed different types of explanations and found the most effective ones in terms of increasing the fairness perception, consensus perception and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which consider the preferences of all or the majority of group members achieve the best results in terms of the mentioned dimensions. Besides, we discovered positive correlations among these aspects. In the context of repeated decisions, group members' satisfaction from previous decisions are helpful to improve the fairness perception of users concerning group recommendations and speed up the group decision-making process. Furthermore, we found out that gender diversity does influence the perception of users regarding the mentioned dimensions of the explanations. Although the proposed explanations were analyzed in group decision scenarios for non-configurable (no-attribute) items, there exist potential possibilities to apply them to explanations for configurable items.

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Research Article Sat, 28 Mar 2020 00:00:00 +0200
Guidelines for Structuring Object-Oriented Product Configuration Models in Standard Configuration Software https://lib.jucs.org/article/24005/ JUCS - Journal of Universal Computer Science 26(3): 374-401

DOI: 10.3897/jucs.2020.020

Authors: Jeppe Rasmussen, Lars Hvam, Katrin Kristjansdottir, Niels Mortensen

Abstract: Product configuration systems (PCSs) are increasingly being used in various industries to manage product knowledge and create the required specifications of customized products. Companies applying PCS face significant challenges in modelling, structuring and documenting the systems. Some of the main challenges related to PCSs are formalising product knowledge conceptually and structuring the product features. The modelling techniques predominantly used to visualise and structure PCSs are the Unified Modelling Language (UML) notations, Generic Bill of Materials (GBOM) and Product Variant Master (PVM), associated with class collaboration cards (CRC-cards). These methods are used to both analyse and model the products and create a basis for implementation to a PCS by using an object-oriented approach. However, the modelling techniques do not consider that most commercial PCSs are not fully object-oriented, but rather, they are expert systems with an inference engine and a knowledge base; therefore, the constructed product models require modifications before implementation in the configuration software. The consequences are that what is supposedly a feasible structure of the product model is not always appropriate for the implementation in standard PCS software. To address this challenge, this paper investigates the best practice in modelling and implementation techniques for PCSs in standard software and alternative structuring methods used in object-oriented software design. The paper proposes a method for a modular design of a PCS in not fully object-oriented standard PCS software using design patterns. The proposed method was tested in a case company that suffered from a poorly structured product model in a not fully object-oriented PCS. The results show that its maintainability can be improved by using design patterns in combination with an agile documentation approach.

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Research Article Sat, 28 Mar 2020 00:00:00 +0200
Analysing Bias in Political News https://lib.jucs.org/article/23996/ JUCS - Journal of Universal Computer Science 26(2): 173-199

DOI: 10.3897/jucs.2020.011

Authors: Gabriel De Arruda, Norton Roman, Ana Monteiro

Abstract: Although of paramount importance to all societies, the fact that media can be biased is a troubling thought to many people. The problem, however, is by no means easy to solve, given its high subjectivity, thereby leading to a number of different approaches by researchers. In this work, we addressed media bias according to a tripartite model whereby news can suffer from a combination of selective coverage of issues (Selection Bias), disproportionate attention given to specific subjects (Coverage Bias), and the favouring of one side in a dispute (Statement Bias). To do so, we approached the problem within an outlier detection framework, defining bias as a noticeable deviation from some mainstream behaviour. Results show that, in following this methodology, one can not only identify bias in specific outlets, but also determine how that bias comes about, how strong it is, and the way it interacts with other dimensions, thereby rendering a more complete picture of the phenomenon under inspection.

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Research Article Fri, 28 Feb 2020 00:00:00 +0200
Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets https://lib.jucs.org/article/23989/ JUCS - Journal of Universal Computer Science 26(1): 50-70

DOI: 10.3897/jucs.2020.004

Authors: Qanita Baker, Farah Shatnawi, Saif Rawashdeh, Mohammad Al-Smadi, Yaser Jararweh

Abstract: Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered and analyzed the influenza-related tweets written in the Arabic language. Several classifiers were used to measure the quality and the performance of the approach, which are: Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbor. The classifiers which achieved the best accuracy results for the three experiments were: Naïve Bayes with 89.06%, and K-Nearest Neighbor with 86.43%, respectively.

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Research Article Tue, 28 Jan 2020 00:00:00 +0200
Pompilos, a Model for Augmenting Health Assistant Applications with Social Media Content https://lib.jucs.org/article/23987/ JUCS - Journal of Universal Computer Science 26(1): 4-32

DOI: 10.3897/jucs.2020.002

Authors: Henrique Vianna, Jorge Luis Victória Barbosa

Abstract: Caused by habits such as poor diets, lack of physical activity practice or smoking, non-communicable diseases were elected by the World Health Organization as one of the greatest challenges of the twenty-first century, despite a lot of information produced in social media focused on preventing this type of disease. This paper presents the Pompilos Model, which aims at improving computer-aided social support by suggesting beneficial health resources and revealing what inuences other people's health, so to foster better health behaviors in social relations. In order to evaluate the model's feasibility, we performed a random experiment during one month and half with two groups to assess the influence of messages related to the prevention of chronic diseases. Those messages presented information on a healthier diet, the practice of physical activities, and ways to lose weight, from monitored Twitter profiles on the habits of health assistant web application's users. So it would be possible to manage food intake, the practice of physical activities, and weight control. Messages related to the prevention of chronic diseases, such as a healthier diet, the practice of physical activities, and weight loss from monitored Twitter profiles were directed to an intervention group as a way to re-engage users in their care activities. With this information, we found a correlation between message reading and the access to the application history feature among intervention users.

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Research Article Tue, 28 Jan 2020 00:00:00 +0200
A Revised Framework for the Governance and Management of Green IT https://lib.jucs.org/article/22695/ JUCS - Journal of Universal Computer Science 25(13): 1736-1760

DOI: 10.3217/jucs-025-13-1736

Authors: J. Patón-Romero, Maria Baldassarre, Moisés Rodríguez, Mario Piattini

Abstract: Sustainability is not an option; it has become a primordial necessity in our nearest future and in the base of the growth of our society in all aspects and areas. Information Technology (IT) is playing a leading role in the field of sustainability. Organizations around the world realize the importance of Green IT and the great benefits it generates at an ecological, social, and economic level. That is why more and more organizations advocate for a sustainable environment in and by IT and demand standards and guidelines in this regard. However, this transformation towards the Green IT is not simple, it is a profound change that must be approached in stages, and the first one is the level of governance and management. For this reason, after developing, applying, and validating a first version (obtaining a series of lessons learned and points of improvement), we have carried out the development of the second version of a "Governance and Management Framework for Green IT". With this revised framework, we intend to offer a more complete and solid guide that helps organizations to gradually implement, evaluate, and improve all those aspects and characteristics of governance and management that are the basis of the processes, practices, and activities of Green IT. The results obtained after validating the revised framework demonstrate a stronger validity, usefulness, and applicability, offering a solid guide to organizations in their efforts to gradually implement, evaluate, and improve Green IT.

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Research Article Sat, 28 Dec 2019 00:00:00 +0200
Improving Multi-Label Classification for Learning Objects Categorization by Taking into Consideration United States of Americage Information https://lib.jucs.org/article/22692/ JUCS - Journal of Universal Computer Science 25(13): 1687-1716

DOI: 10.3217/jucs-025-13-1687

Authors: Pedro Espejo, Eva Gibaja, Victor Menéndez, Alfredo Zapata, Cristobal Romero

Abstract: Learning objects are digital resources that can be deployed by means of a web system for supporting teaching. A key advantage is reuse, and this is possible thanks to learning objects repositories that allow learning object search, management and categorization. In this work, we propose a novel approach towards automatically learning object categorization taking into consideration learning object United States of Americage information. We use a multi-label learning approach since each learning object might be associated with multiple categories. We have developed a methodology with three main stages allowing us to firstly select the most suitable set of text features from learning objects metadata, secondly selecting how much historical learning object United States of Americage information can enhance classification performance, and finally selecting the best multi-label classification algorithms with our data. We have carried out an experimental work using 519 learning objects gathered from the AGORA repository for 8 years. We have compared 13 multi-label classification algorithms over 16 evaluation measures. The results obtained show that United States of Americage information about the learning object can improve the classification.

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Research Article Sat, 28 Dec 2019 00:00:00 +0200
Overcoming the Silver Generation Digital Gap https://lib.jucs.org/article/22687/ JUCS - Journal of Universal Computer Science 25(12): 1625-1543

DOI: 10.3217/jucs-025-12-1625

Authors: Carlos De Carvalho, Pedro Cano, José Roa, Anna Wanka, Franz Kolland

Abstract: Being able to effectively use online tools has become a fundamental competence in our Society. Therefore, it is important to tackle the age digital divide as there is a rapidly growing number of elderly people. Like everyone else, older adults (senior citizens or the silver generation) must be equipped with the necessary skills to be able to be connected and integrated in the online world to prevent their social isolation and to foster their inclusion. As a contribution to that effort, a European-wide digital literacy development initiative for senior citizens was setup and this article presents the analysis of the achieved results which shows a very positive perception of the seniors on the developed digital abilities.

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Research Article Sun, 1 Dec 2019 00:00:00 +0200
Implementing Flipped Classroom that Used a Context Aware Mobile Learning System into Learning Process https://lib.jucs.org/article/22681/ JUCS - Journal of Universal Computer Science 25(12): 1531-1553

DOI: 10.3217/jucs-025-12-1531

Authors: Mahnane Lamia, Mohamed Hafidi, André Tricot, Ouissem Benmesbah

Abstract: While some studies indicate that flipped classrooms offer many positive educational outcomes, other studies draw attention to limitations associated with flipped classroom (students' limited preparation prior to class, students' need for guidance at home, students' inability to get immediate feedback while they study at home, and little research has focused on students' learning outcomes, such as: satisfaction and motivation). This paper attempts to address several of these limitations through exploratory studies conducted in an Algerian University. The approach proposed in this paper called Flipped classroom based on Context-Aware mobile learning system (FC-CAMLS) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The system was implemented in an English Language course. It was expected that the FC-CAMLS increased the management of students' heterogeneity. A quantitative analysis by means of structural equation modeling was performed to analyze the caUnited States of Americal relationships between knowledge, skills, motivation and students' satisfaction. The results show that the system has positive effects on students' knowledge, skills, and motivation. Finally, our research provides useful results that the use of the context dimensions and learner's feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

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Research Article Sun, 1 Dec 2019 00:00:00 +0200
An Intelligent Data Analytics based Model Driven Recommendation System https://lib.jucs.org/article/22665/ JUCS - Journal of Universal Computer Science 25(10): 1353-1372

DOI: 10.3217/jucs-025-10-1353

Authors: Bushra Ramzan, Imran Bajwa, Rafaqut Kazmi, Shabana Ramzan

Abstract: The recommendation systems are getting important due to their significance in decision making, social and economic impact on customers and getting detailed information relevant to a required product or a service. A challenge in getting true recommendations in terms of relevance is the heterogenous nature of data (likes, ratings, reviews, etc.) that a recommendation engine has to cope with. This paper presents an intelligent approach to handle heterogeneous and large-sized data of user reviews and generate true recommendations for the future customers. The proposed approach makes use of Apache Cassandra to efficiently store data (such as customer reviews, feedback of hotel customers) having context properties such as awareness and knowledge of the tourists, personal preferences (such as ratings, likes, etc.) and location of the users. This system consists of three main components: the web front-end, the data storage and the recommendation engine to gain recommendations efficiently. The recommendation engine is relying on Euclidean distance and Collaborative Filtering (CF) to measure similarities in users' review or items' features. Our hotel recommender approach has bifold contribution as it has ability to handle heterogeneous data with the help of big data platform and it also provides accurate and true recommendations.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
A Smart Hydroponics-Based System for Child Education https://lib.jucs.org/article/22659/ JUCS - Journal of Universal Computer Science 25(10): 1279-1300

DOI: 10.3217/jucs-025-10-1279

Authors: Samet Dinçer, Yıltan Bitirim

Abstract: In this paper, a novel smart system based on hydroponics is proposed. It is aimed to help educate children by contributing to their improvement on cognitive domain, affective domain and psychomotor domain. This hydroponics-based smart education system is task oriented, does not interfere the child's daily needs such as studying and sleeping and includes instant child control. It is an interdisciplinary system which consists of Android application, Raspberry Pi, Web server, MySQL server and hydroponics system components. Improvement of children in terms of cognitive, affective and psychomotor could be contributed with this system's various features.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
Research on Fair Trading Mechanism of Surplus Power Based on Blockchain https://lib.jucs.org/article/22656/ JUCS - Journal of Universal Computer Science 25(10): 1240-1260

DOI: 10.3217/jucs-025-10-1240

Authors: Zhuoqun Xia, Jingjing Tan, Jin Wang, Runnong Zhu, Hongguang Xiao, Arun Sangaiah

Abstract: The development of blockchain technology is very rapidly. As a decentralized distributed technology, the blockchain has become one of the most promising Internet applications, and its application in the power balance trading platform has also received extensive attention. In view of the information asymmetry between the trading center and the margin trading users in the power balance trading platform, it is difficult to guarantee the fairness of the transaction and affect the actual income of the production consumers. First, we analyze the trading mechanism of the power surplus market.Then we designed a smart contract for multi-party bidding power resources based on blockchain technology, and achieved the decentralized power trading decision to ensure the information is symmetric and fair.At the same time, the credibility model is established by analyzing the user's recent transaction records, and we design a corresponding punishment mechanism to strengthen the constraint on the execution of offline point-to-point power transactions.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
Trust Based Cluster Head Election of Secure Message Transmission in MANET Using Multi Secure Protocol with TDES https://lib.jucs.org/article/22655/ JUCS - Journal of Universal Computer Science 25(10): 1221-1239

DOI: 10.3217/jucs-025-10-1221

Authors: K. Shankar, Mohamed Elhoseny

Abstract: In wireless communication, Mobile Ad Hoc Network (MANET) consists of a number of mobile nodes which are communicated with each other without any base station. One of the security attacks in MANETs is Packet forwarding misbehaviour attack; this makes MANETs weak by showing message loss behavior. For securing message transmission in MANET, the work proposes Energy Efficient Clustering Protocol (EECP) with Radial Basis Function (RBF) based CH is elected for formed Clusters. Moreover, here some Network measures are considered to detect the malicious nodes and CH model that is speed, mobility, trust and so on. The trust value of the node is computed from the neighbor node which helps in further location to find a malicious node in the network to avail message drop and energy consumption (EC). After detecting malicious nodes, Multi secure Protocols that is Secure Efficient Distance Vector Routing (SEDV) and Secure Link State Routing Protocol (SLSP) with encryption technique used for message security. If the" HELLO" message sending by the sender, its encrypted and decrypted triples in receiver end to get the plain message, this technique is Triple Data Encryption Standard (TDES). Finally, the implementation results are evaluated to analyze the message security level of the proposed system in MANET in terms, of Packet to Delivery Ratio (PDR, Network Life Time (NLT) and some other important Measures.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
Identifying Groupware Requirements in People-Driven Mobile Collaborative Processes https://lib.jucs.org/article/22642/ JUCS - Journal of Universal Computer Science 25(8): 988-1017

DOI: 10.3217/jucs-025-08-0988

Authors: Valeria Herskovic, Sergio Ochoa, José Pino

Abstract: People-driven mobile collaborative processes are increasingly mediated by technology due to the ubiquity, efficiency and flexibility that modern groupware systems provide their users. However, identifying groupware requirements to be considered in their development is a challenging task, since the processes being supported by them do not have a clear workflow coordinating the activities performed by the participants. Thus, software developers must usually guess these requirements based on their own experience, and so the elicitation process becomes a creative activity instead of an engineering process. Trying to reduce this uncertainty about groupware requirements identification, and thus helping developers improve their capability to predict the suitability of a collaborative system, this paper presents a visual notation to represent user interaction scenarios through models. These models are processed to automatically determine a set of potentially required groupware services. Thus, this proposal reduces the uncertainty about the groupware requirements to be considered in the development of a system supporting a particular people-driven mobile collaborative process. The United States of Americability and usefulness of the visual notation and the method to derive the groupware requirements are illustrated with a running example, and also through its application to a case study. The results are encouraging and consistent, allowing us to augur potential adoption in research and industrial settings.

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Research Article Wed, 28 Aug 2019 00:00:00 +0300
Planning of Urban Public Transportation Networks in a Smart City https://lib.jucs.org/article/22640/ JUCS - Journal of Universal Computer Science 25(8): 946-966

DOI: 10.3217/jucs-025-08-0946

Authors: Jonathan Frez, Nelson Baloian, José Pino, Gustavo Zurita, Franco Basso

Abstract: Planning efficient public transport is a key issue in modern cities. When planning a route for a bus or a line for a tram or subway, it is necessary to consider people's demand for this service. In this work we present a method to use existing crowdsourced data (like Waze and OpenStreetMap) and cloud services (like Google Maps) to support a transportation network decision making process. The method is based on the Dempster-Shafer Theory to model transportation demand. It uses data from Waze to provide a congestion probability and data from OpenStreetMap to provide information about location of facilities such as shops, in order to predict where people may need to start or end their trips using public transportation vehicles. The paper also presents an example using this method with real data. The example shows an analysis of the current availability of public transportation stops in order to discover its weak points.

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Research Article Wed, 28 Aug 2019 00:00:00 +0300
Mobile Applications for People with Parkinson's Disease: A Systematic Search in App Stores and Content Review https://lib.jucs.org/article/22627/ JUCS - Journal of Universal Computer Science 25(7): 740-763

DOI: 10.3217/jucs-025-07-0740

Authors: Sonia Estévez, M. Cambronero, Yolanda García-Ruiz, Luis Llana Díaz

Abstract: Parkinson's disease (PD) is the most common age-related neurodegenerative motor disease. People with Parkinson's have different motor symptoms related to movement, the most common of which are tremor, muscle rigidity and slowness of movement. In addition, there are other problems that are unrelated to motor symptoms, such as sleep behavior disorders, personality changes, pain and depression. Numerous apps designed for people with this disease have been developed in recent years. Due to the diversity of symptoms, there are very many different apps. Our goal is to carry out a systematic review of available apps related to PD for the operating systems iOS and Android and to assess their features. In addition, we are interested in the United States of Americability of the apps. A search for the representative terms "Parkinson" and "Parkinson's Disease", together with the descriptors of the symptoms, was conducted in the Google Play and Apple App stores. Next, we screened the PD-related apps. Finally, we assessed the apps with respect to symptoms, users, purpose and features. In addition, a United States of Americability evaluation was carried out.

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Research Article Sun, 28 Jul 2019 00:00:00 +0300
An Educational Glance into the Future: Holodeck as a Future Enacted Narrative Learning Technology https://lib.jucs.org/article/22607/ JUCS - Journal of Universal Computer Science 25(5): 446-464

DOI: 10.3217/jucs-025-05-0446

Authors: Tiina Kymäläinen

Abstract: his article describes how the fictional concept of Holodeck can be seen as a future immersive learning technology, as well as a new medium for future enacted, narrative experiences. In essence, the article illustrates how this fictional media has been recently studied within the art education context; particularly with the emphasis on how the medium has been considered through the holonovel writing activity. At first, the literature review presents earlier Holodeck-related research, innovation and applications, which, subsequently, provide important terminology for the holonovel writing process. The terminology includes the setup, important stakeholders and those critical units and entities that are needed for defining the pre-conditions for the Holodeck. Thereafter, the article introduces content of the holonovel course and the creation process for such holonovels that take the form of science fiction prototypes. Finally, the article presents some student-created examples of holonovels that employ the Holodeck for educational and pedagogical purposes.

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Research Article Tue, 28 May 2019 00:00:00 +0300
Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics https://lib.jucs.org/article/22604/ JUCS - Journal of Universal Computer Science 25(4): 390-417

DOI: 10.3217/jucs-025-04-0390

Authors: Matias Micheletto, Rodrigo Santos, Javier Orozco

Abstract: In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.

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Research Article Sun, 28 Apr 2019 00:00:00 +0300
Ontology and Weighted D-S Evidence Theory-Based Vulnerability Data Fusion Method https://lib.jucs.org/article/22592/ JUCS - Journal of Universal Computer Science 25(3): 203-221

DOI: 10.3217/jucs-025-03-0203

Authors: Xiaoling Tao, Liyan Liu, Feng Zhao, Yan Huang, Saide Zhu

Abstract: With the rapid development of high-speed and large-scale complex network, network vulnerability data presents the characteristics of massive, multi-source and heterogeneous, which makes data fusion become more complex. Although existing data fusion methods can fuse multi-source data, they do not consider that the multisource data may affect the accuracy of fusion result. To solve this problem, we propose an ontology and weighted D-S evidence theory-based vulnerability data fusion method. In our method, we utilize ontology to describe the network vulnerability semantically and construct the network vulnerability ontology hierarchically. Then we use weighted D-S evidence theory to perform the operation of probability distribution and fusion processing. Besides, we simulate our method on MapReduce parallel computing platform. The experiment results show that our method is more effective and accurate compared with existing fusion approaches using single detection tool and traditional D-S evidence theory.

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Research Article Thu, 28 Mar 2019 00:00:00 +0200
Designing a Human Computation Framework to Enhance Citizen-Government Interaction https://lib.jucs.org/article/22582/ JUCS - Journal of Universal Computer Science 25(2): 122-153

DOI: 10.3217/jucs-025-02-0122

Authors: Koldo Zabaleta, Unai Lopez-Novoa, Ivan Pretel, Diego López-De-Ipiña, Vincenzo Cartelli, Giuseppe Modica, Orazio Tomarchio

Abstract: Human computation or Human-based computation (HBC) is a paradigm that considers the design and analysis of information processing systems in which humans participate as computational agents performing small tasks and being orchestrated by a computer system. In particular, humans perform small pieces of work and a computer system is in charge of orchestrating their results. In this work, we want to exploit this potential to improve the take-up of e-service United States of Americage by citizens interacting with governments. To that end, we propose Citizenpedia, a human computation framework aimed at fostering citizen's involvement in the public administration. Citizenpedia is presented as a web application with two main components: the Question Answering Engine, where citizens and civil servants can post and solve doubts about e-services and public administration, and the Collaborative Procedure Designer, where citizens can collaborate with civil servants in the definition and improvement of new administrative procedures and e-services. In this work, we present the design and prototype of Citizenpedia and two evaluation studies conducted: the first one, a set of on-line surveys about the component's design, and the second one, a face-to-face user evaluation of the prototype. These evaluations showed us that the participants of the tests found the platform attractive, and pointed out several improvement suggestions regarding user experience of e-services.

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Research Article Thu, 28 Feb 2019 00:00:00 +0200
Dynamic Estimation of Temporary Failure in SoC FPGAs for Heterogeneous Applications https://lib.jucs.org/article/23789/ JUCS - Journal of Universal Computer Science 24(12): 1776-1799

DOI: 10.3217/jucs-024-12-1776

Authors: J. Kokila, N. Ramasubramanian, Ravindra Thamma

Abstract: Recent processors are shrinking in size due to the advancement of technology. Reliability is an important design parameter along with power, cost, and performance. The processors need to be fault tolerant to counter reliability challenges. This work proposes a dynamic thermal and voltage management (DTVM) system which ensures a reasonable level of fault tolerance. The fault tolerance system (FTS) identifies and subsequently can forecast temporary failures at run-time. The temporary failures are dynamically estimated on SoC FPGAs for a class of heterogeneous applications. The dynamic priority scheduling based on absolute deadline is adopted to improve the nature of FTS. Experimental results indicate that the failure rate reduces by 7.2% with the variation of 2% and 12% in temperature and voltage respectively.

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Research Article Fri, 28 Dec 2018 00:00:00 +0200
An Architecture for IoT Management Targeted to Context Awareness of Ubiquitous Applications https://lib.jucs.org/article/23610/ JUCS - Journal of Universal Computer Science 24(10): 1452-1471

DOI: 10.3217/jucs-024-10-1452

Authors: Rodrigo Souza, João Lopes, Cláudio Geyer, Anderson Cardozo, Adenauer Yamin, Jorge Luis Victória Barbosa

Abstract: The recent advances in the Internet of Things (IoT), which has provided increasing availability of networked sensors and actuators, have given context awareness research in the UbiComp area a new perspective. In this sense, the main contribution of this paper is the proposition of a distributed architecture for IoT, called CoIoT (Context awareness in the Internet of Things). This architecture is designed to provide proactive management of the interactions with the physical environment. To evaluate the functionalities of the proposed architecture we implemented a case study in the agricultural area, specifically in the monitoring of seed analysis laboratory.

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Research Article Sun, 28 Oct 2018 00:00:00 +0300
Building an Educational Platform Using NLP: A Case Study in Teaching Finance https://lib.jucs.org/article/23607/ JUCS - Journal of Universal Computer Science 24(10): 1403-1423

DOI: 10.3217/jucs-024-10-1403

Authors: Soto Montalvo, Jesus Palomo, Carmen Orden

Abstract: Information overload is one of the main challenges in the current educational context, where the Internet has become a major source of information. According to the European Space for Higher Education, students must now be more autonomous and creative, with lecturers being required to provide guidance and supervision. Guiding students to search and read news related to subjects that are being studied in class has proven to be an effective technique in improving motivation, because students appreciate the relevance of the topics being studied in real world examples. However, one of the main drawbacks of this teaching practice is the amount of time that lecturers and students need for searching relevant and useful information on different subjects. The objective of our research is to demonstrate the usefulness of a complementary teaching tool in the traditional educational classroom. It is a new educational platform that combines Artificial Intelligence techniques with the expertise provided by lecturers. It automatically compiles information from different sources and presents only relevant breaking news classified into different subjects and topics. It has been tested on a Finance course, where being continually informed about the latest economic and financial news is an important part of the teaching process, specially for certain key financial concepts. The utility of the platform has been studied by conducting student surveys. The results confirm that using the platform had a positive impact on improving students' motivation and boost the learning processes. This research provides evidence about effectiveness of the new educational complement to traditional teaching methods in classrooms. Also, it demonstrates the improvement on the knowledge transfer within an environment of information overload.

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Research Article Sun, 28 Oct 2018 00:00:00 +0300
Target Selection in Head-Mounted Display Virtual Reality Environments https://lib.jucs.org/article/23528/ JUCS - Journal of Universal Computer Science 24(9): 1217-1243

DOI: 10.3217/jucs-024-09-1217

Authors: Difeng Yu, Hai-Ning Liang, Feiyu Lu, Vijayakumar Nanjappan, Konstantinos Papangelis, Wei Wang

Abstract: Target selection is one of the most common and important tasks in interactive systems. Within virtual reality environments, target selection can pose extra challenges to users because targets can be located far away, clustered together, and occluded from view. Although selection techniques have been explored, it is often unclear which techniques perform better across different environmental target density levels and which have higher levels of usability especially for recently released commercial head-mounted display (HMD) virtual reality systems and input devices. In this paper, we first review previous studies on target selection in HMD VR environments. We then compare the performances of three main techniques or metaphors (RayCasting, Virtual Hand, and Hand-Extension) using recently marketed VR headsets and input devices under different density conditions and selection areas. After, we select the best two techniques (RayCasting and Virtual Hand) for the second experiment to explore their relative performance and usability by adding different feedback to these two techniques. In the third experiment, we implemented three techniques with pointing facilitators and compared them against the best techniques from the second experiment, RayCasting with visual feedback, to assess their performance, error rates, learning effects, and usability. The three studies altogether suggest the best target selection features, based on techniques, feedback, and pointing facilitators for target density conditions in HMD VR environments.

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Research Article Fri, 28 Sep 2018 00:00:00 +0300
An Effective Risk Factor Detection and Disease Prediction (RFD-DP) Model Applied to Hypertension https://lib.jucs.org/article/23527/ JUCS - Journal of Universal Computer Science 24(9): 1192-1216

DOI: 10.3217/jucs-024-09-1192

Authors: Dingkun Li, Yaning Li, Zhou Ye, Musa Ibrahim, Keun Ryu, Seon Jeong

Abstract: Never before in history is the data growing at such a high volume, variety and velocity. It not only provides multi-sources of information for people to discover useful, important and valuable nuggets of information, but also increases the difficulty in finding such nuggets in almost all fields. Particularly, the field of healthcare is known for its dominical or ontological complexity and variety of clinical data or medical data regarding its variable data standards and data quality and so as the high data dimensionality. In order to effectively use the data at the hand to improve healthcare outcomes and processes, this paper illustrates a model called Risk Factor Detection and Disease Prediction (RFD-DP) model. The model incorporates statistics, data mining and MapReduce techniques on high dimensional clinical data to detect risk factors and generate predicator for a specified disease, hypertension disease. The experimental results indicate that the proposed model outperforms traditional feature selection and classification methods in terms of accuracy, F-score, and AUC. Consequently, the proposed model is promising to be applied to healthcare system.

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Research Article Fri, 28 Sep 2018 00:00:00 +0300
Service-Driven Iterative Software Project Management with I-Tropos https://lib.jucs.org/article/23384/ JUCS - Journal of Universal Computer Science 24(7): 975-1011

DOI: 10.3217/jucs-024-07-0975

Authors: Yves Wautelet, Manuel Kolp, Loris Penserini

Abstract: The increased symbiotic relationships between society and Information and Communication Technology (ICT) pave the ways for a substantial alignment and rethinking of current software development methodologies. This paper presents the use and validation of a software analysis and project management (PM) framework for iterative software development within the Tropos method. This methodology is servicedriven, its requirements models are founded on social-based modeling elements. The PM framework includes risk and quality management; it has been applied on multiple case studies and this paper presents a full experience report. The proposed methodology is aimed to provide a reference for practitioners willing to develop iteratively using Tropos.

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Research Article Sat, 28 Jul 2018 00:00:00 +0300
Lightweight Adaptive E-Advertising Model https://lib.jucs.org/article/23383/ JUCS - Journal of Universal Computer Science 24(7): 935-974

DOI: 10.3217/jucs-024-07-0935

Authors: Alaa Qaffas, Alexandra Cristea, Mohamed Mead

Abstract: Adaptive online advertising is a rapidly expanding marketing tool that delivers personalised messages and adverts to Internet users. At a time when the Internet is burgeoning, many websites use an adaptation process to tailor their advertisements, however, often in an ad-hoc manner. Thus, a new model that guarantees a systematic integration of adaptive features on existing business websites has become an urgent requirement to satisfy customers. This paper aims to solve this issue, by presenting an innovative model for e-advertising adaptation: the Layered Adaptive Advertising Integration (LAAI). LAAI is building upon previous models and frameworks from different domains, by selecting and adding novel features appropriate for e-advertising. Based on this model, a new adaptation system -AEADS - is developed, to test and evaluate the LAAI model. This research also reports on the perception on the methods towards obtaining generalisation, portability and efficiency, as proposed by the LAAI model, by evaluating how a range of businesses are enabled to adapt their advertisements based on user profiles and behaviours.

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Research Article Sat, 28 Jul 2018 00:00:00 +0300
Medical Diagnosis of Chronic Diseases Based on a Novel Computational Intelligence Algorithm https://lib.jucs.org/article/23304/ JUCS - Journal of Universal Computer Science 24(6): 775-796

DOI: 10.3217/jucs-024-06-0775

Authors: Yenny Villuendas-Rey, Mariana-D. Alanis-Tamez, Carmen-F. Benguría, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto

Abstract: Computational Intelligence techniques in medicine have become an increasing area of research worldwide. Among them, the application and development of new models and algorithms for disease diagnosis and prediction have been an active research topic. The research contribution of the current paper is the proposal of a novel classification model, and its application to the diagnosis of chronic diseases. One of the main characteristics of the new model is that it is designed to deal with imbalanced data. With the purpose of making experimental comparisons to demonstrate the benefits of the proposed model, we tested five classification models, over medical data. The application of the supervised classification algorithms is done over the Knowledge Extraction based on Evolutionary Learning (KEEL) environment, using a distributed optimally balanced stratified 5-fold cross validation scheme. In addition, the experimental results obtained were validated in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that the proposed model outperforms other supervised classifiers for medical diagnosis.

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Research Article Thu, 28 Jun 2018 00:00:00 +0300
Identifying Cleavage Sites of Gelatinases A and B by Integrating Feature Computing Models https://lib.jucs.org/article/23294/ JUCS - Journal of Universal Computer Science 24(6): 711-724

DOI: 10.3217/jucs-024-06-0711

Authors: Quan Zou, Chi-Wei Chen, Hao-Chen Chang, Yen-Wei Chu

Abstract: Gelatinases proteases with the ability to cleave the extracellular matrix (ECM). Two types of gelatinases exist: Gelatinase A, also referred to as matrix metalloproteinase-2 (MMP-2), and gelatinase B, also referred to as matrix metalloproteinase-9 (MMP-9). MMP-2 and MMP-9 degrade ECM, which is highly expressed during tumor metastasis. The poor therapeutic effects of inhibitors can be attributed to the high structural homology shared by members of the matrix metalloproteinase family. The highly similar structures of these proteases preclude the specific binding of inhibitor drugs. Moreover, the regulatory pathways of MMP-2 and MMP-9 remain poorly understood. An accurate model for the prediction of substrates and the cleavage sites of gelatinases should be developed to enable screening and exploring the physiological and pathological mechanisms of these enzymes. Prediction is based on various types of information on binary integration, physical-chemical properties, protein stability, solvent accessibility, and protein secondary structure. In this study, the first level of the prediction model was constructed on the basis of intergroup differences and support vector machine. Predictive probability was then taken as the characteristic of the second level of the prediction model, which was constructed using different machine-learning methods. The Mathews correlation coefficients of the MMP-2 and MMP-9 prediction models were 89.4% and 64.4%, respectively. The physical-chemical properties of the active sites of MMP-2 and MMP-4 were selected for analysis. The completion of this prediction system will aid the discovery of regulatory paths and novel applications of MMP-2 and MMP-9, as well as provide references for drug design.

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Research Article Thu, 28 Jun 2018 00:00:00 +0300
A Hybrid Machine Learning Scheme to Analyze the Risk Factors of Breast Cancer Outcome in Patients with Diabetes Mellitus https://lib.jucs.org/article/23291/ JUCS - Journal of Universal Computer Science 24(6): 665-681

DOI: 10.3217/jucs-024-06-0665

Authors: Linglong Ye, Tian-Shyug Lee, Robert Chi

Abstract: Along with the worldwide trend of rapidly aging populations, diabetes mellitus and its comprehensive complications have become major public health issues. Considerable evidence suggests patients with diabetes mellitus have a higher risk of breast cancer. However, the relationships between the complications of diabetes mellitus and occurrence of breast cancer have not been well characterized. Despite the higher risk of breast cancer among patients with diabetes mellitus, patients with breast cancer constitute only a relatively small proportion of the diabetes mellitus data, leading to an imbalanced data set. This study proposes a hybrid machine learning scheme to cope with imbalanced data in the analysis of risk factors of breast cancer in patients with diabetes mellitus. The scheme combines the undersampling based on the clustering algorithm, the k-means algorithm, and the extreme gradient boosting algorithm. The results identify that occlusion stroke, diabetes with peripheral circulatory disorders, peripheral angiopathy in diseases classified elsewhere, and other forms of chronic ischemic heart disease are risk factors. This study provides an application of advanced methods in health care and shows the epidemiologic and informatics value of the proposed hybrid machine learning scheme.

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Research Article Thu, 28 Jun 2018 00:00:00 +0300
Relating Mobile Device Use and Adherence to Information Security Policy with Data Breach Consequences in Hospitals https://lib.jucs.org/article/23224/ JUCS - Journal of Universal Computer Science 24(5): 634-645

DOI: 10.3217/jucs-024-05-0634

Authors: Simon Vrhovec, Blaž Markelj

Abstract: Critical infrastructure is a high value target in the real world and cyberspace. A failure to protect the critical infrastructure in the cyberspace could lead to serious financial and material losses and violate the effective functioning of a country. In this paper, we will focus on healthcare as an important part of the critical infrastructure. An important part of the healthcare infrastructure are hospitals. Hospital personnel is increasingly using mobile devices in their everyday work to improve patient care. Hospitals may however fail to adequately address the use of mobile devices and adapt their information security policies in time. Hospital personnel may use both their personal and work mobile devices for everyday work. Sometimes they do it without adhering to an adequate hospital information security policy. The objective of this paper is to study the relation between the use of mobile devices, adhering to hospital information security policy and perceived consequences of data breaches. An exploratory survey (N = 95) has been conducted in a Slovenian hospital. Respondents were asked about the use of their personal and work mobile devices for accessing medical data, adhering to the hospital information security policy, and the perceived consequences of data breaches for themselves, the hospital and the patients. The results show that perceived personal consequences are negatively correlated with personal and work mobile device use for work. Also, adhering to information security policy is positively correlated with perceived data breach consequences for both the patients and the hospital.

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Research Article Mon, 28 May 2018 00:00:00 +0300
Large Scale Mobility-based Behavioral Biometrics on the Example of the Trajectory-based Model for Anomaly Detection https://lib.jucs.org/article/23147/ JUCS - Journal of Universal Computer Science 24(4): 417-443

DOI: 10.3217/jucs-024-04-0417

Authors: Piotr Kałużny, Agata Filipowska

Abstract: The paper describes an implementation of a behavioral authentication system, working on sparse geographical data generated by mobile devices in the form of CDR logs. While providing a review of state of the art w.r.t. sensors and measures that can be used when creating a system detecting anomalies in the user behavior, it also describes domain specific authorization methods focusing on the user mobility. The trajectory based stay-extraction model is utilized to build user mobility patterns, upon which the anomaly detection model measures the repeatability of human behavior in dimensions of: geography, time and sequentiality. The goal is to measure the extent to which the geographical aspect of the human mobility can be used in behavioral biometrics' systems i.e. in which scenarios geography may enable to describe (and differentiate between) user patterns - based on anomaly detection in cases resembling real life scenarios (phone theft or sharing between users). The research methods developed may be implemented on mobile devices to benefit from multiple sensors data in the authentication processes. The model is evaluated on a large telecom dataset, with the use of similarity classes, what allows measuring the accuracy of the model in real-life scenarios and provides benchmarking guidelines for the future work on the topic.

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Research Article Sat, 28 Apr 2018 00:00:00 +0300
Multi-scaled Spatial Analytics on Discovering Latent Social Events for Smart Urban Services https://lib.jucs.org/article/23075/ JUCS - Journal of Universal Computer Science 24(3): 322-337

DOI: 10.3217/jucs-024-03-0322

Authors: O-Joun Lee, Yunhu Kim, Hoang Nguyen, Jai Jung

Abstract: The goal of this paper is to discover latent social events from social media for sensitively understanding social opinions that appeared within a city. The latent social event indicates a regional and inconspicuous social event which is mostly buried under macroscopic trends or issues. To detect the latent social event, we propose three methods: i) discovering areas-ofinterest (AOIs), ii) allocating social texts to the AOIs, and iii) detecting social events in each AOI. The AOIs can be composed by grouping social texts which are topically and spatially homogeneous. To make the AOIs dynamic and incremental, we use windows for allocating a social text to an adequate AOI. Lastly, the latent social events are detected from the AOI on the basis of keywords and temporal distribution of the social texts. Although, in this study, we limited the proposed method into analyzing social media, it could be extended to detecting events among agents/things/sensors.

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Research Article Wed, 28 Mar 2018 00:00:00 +0300
Do you Want to be a Superhero? Boosting Emotional States with the Booth https://lib.jucs.org/article/22990/ JUCS - Journal of Universal Computer Science 24(2): 85-107

DOI: 10.3217/jucs-024-02-0085

Authors: Jan Schneider, Dirk Börner, Peter Van Rosmalen, Marcus Specht

Abstract: Educational practitioners have stressed the relevance of providing learners with a set of 21th century skills that will allow them to face current life challenges. Among others this includes communication and social skills such as public speaking, argumentation, negotiation, etc. Besides the acquisition of knowledge and techniques, these skills have the special characteristic that their performance is usually conducted under emotionally charged and stressful situations. Recent advances in technology have allowed the creation of digital applications to support learners with the development of techniques for this type of skills. However, supporting learners on the preparation of a mindset that allows them to perform well under emotionally charged circumstances is an area that technology enhanced learning has practically overlooked. To examine this gap, we developed the Booth, an application designed to get learners into a powerful and resourceful emotional state. In this article we present a two-step user study. Results of the first evaluation show that the use of the Booth induced a positive emotional state on users. Results from the second step suggest that using the Booth helps learners to emotionally prepare for public speaking.

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Research Article Wed, 28 Feb 2018 00:00:00 +0200
A New m-Learning Scenario for a Listening Comprehension Assessment Test in Second Language Acquisition [SLA] https://lib.jucs.org/article/23773/ JUCS - Journal of Universal Computer Science 23(12): 1200-1214

DOI: 10.3217/jucs-023-12-1200

Authors: Teresa Magal-Royo, Jesus Garcia Laborda, Sara Price

Abstract: Computer adaptive language testing offers the possibility to research and practice m-learning using ubiquitous technology. Virtual education in m-learning uses conventional Learning Objects (LO) to enable the possibility of development of several tasks oriented towards language learning including the assessment and verification of skill improvements in second language acquisition. So far, there are few research papers on the impact of using new multimodal digital resources as LO in the design process of foreign language assessment tests through mobile devices because many English certification tests still continue to use traditional testing techniques [face-to-face and pen-and-paper assessments] combined with conventional digital environments oriented to virtual education. Learning languages requires not only new m-learning scenarios for assessment but also multimodal interactive environments to improve the user's experience during proficiency tests or language certification. Multimodal object learning such as augmented environments, learning games, spatial sound, etc. can be integrated into an assessment process to enhance the user´s experience by simulating natural communicative scenarios. The present article defines an innovative new m-learning scenario for listening comprehension assessment in an on-line test by implementing a multimodal audio learning source named binaural sound. Use of this technology will enable demonstration of other possibilities of human interaction to improve the user's experience in language learning through sound perception and its cognition from the user in an especific learning task.

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Research Article Thu, 28 Dec 2017 00:00:00 +0200
Developing a BYOD Scale to Measure the Readiness Level: Validity and Reliability Analyses https://lib.jucs.org/article/23769/ JUCS - Journal of Universal Computer Science 23(12): 1113-1131

DOI: 10.3217/jucs-023-12-1113

Authors: Murat Topaloglu, Dilek Kırar

Abstract: The BYOD programme is a trend that aims to provide companies and workers with the next generation of security methods and flexible business models. These have been developed recently as a result of technological developments, especially in smart devices. Individuals from the "Y generation", who are also called the millennials, have a significant influence on shaping the present and future technology. Y generation employees want to use their own devices, including their own personal applications. Allowing employees to use their own devices does not mean that you will lose anything or have no control. For this reason, the BYOD policy, when implemented at a good level, significantly increases business performance and increases the productivity with the benefits provided by mobility. The BYOD tendency, which is difficult to avoid, increases the productivity of employees and the flexibility of the company in the eyes of the employees, by letting them use their own devices in the business environment. Moreover, it reflects positively on the employees' morale, with a subsequent increase in company loyalty. The aim of this study is to evaluate the validity and reliability analyses done during the development of the scale which aims to measure the effects of BYOD on workers and to assess its security components, benefits, applicability and sustainability. Our goal is to revise the previous research done and present objective values and findings obtained from the analyses. These values were based on the demographic information and the answers given by participants about to what extent BYOD is known and legal, its vulnerabilities in infrastructure and data security, the way it affects workers' perceptions individually and in general, and the benefits it provides. SPSS 20 program was used for descriptive statistics, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), item analyses and correlation coefficients.

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Research Article Thu, 28 Dec 2017 00:00:00 +0200
Prospects and Challenges for the Computational Social Sciences https://lib.jucs.org/article/23687/ JUCS - Journal of Universal Computer Science 23(11): 1057-1069

DOI: 10.3217/jucs-023-11-1057

Authors: Giangiacomo Bravo, Mike Farjam

Abstract: Computational social sciences (CSS) refer to computer-enabled investigations of human behaviour and social interaction. They include three main components - (i) computational modelling and social simulation, (ii) the analysis of digital traces of online interactions, (iii) virtual labs and online experiments - and allow researchers to perform studies that were even hard to imagine a few decades ago. Moreover, CSS favour a more systematic test of theories and increase the possibility of study replication, two factors holding the potential to help social sciences reach a higher scientific status. Despite the huge potential of CSS, we follow previous works in identifying several impediments to a larger adoption of computational methods in social sciences. Most of them are linked with the humanistic attitude and a lack of technical skills of many social scientist. Significant changes in the basic training of social scientist and in the relation patterns with other disciplines and departments are needed before the potential of CSS can be fully exploited.

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Research Article Tue, 28 Nov 2017 00:00:00 +0200
Utilizing Multilingual Language Data in (Nearly) Real Time: The Case of the Nordic Tweet Stream https://lib.jucs.org/article/23684/ JUCS - Journal of Universal Computer Science 23(11): 1038-1056

DOI: 10.3217/jucs-023-11-1038

Authors: Mikko Laitinen, Jonas Lundberg, Magnus Levin, Alexander Lakaw

Abstract: This paper presents the Nordic Tweet Stream, a cross-disciplinary digital humanities project that downloads Twitter messages from Denmark, Finland, Iceland, Norway and Sweden. The paper first introduces some of the technical aspects in creating a real-time monitor corpus that grows every day, and then two case studies illustrate how the corpus could be used as empirical evidence in studies focusing on the global spread of English. Our approach in the case studies is sociolinguistic, and we are interested in how widespread multilingualism which involves English is in the region, and what happens to ongoing grammatical change in digital environments. The results are based on 6.6 million tweets collected during the first four months of data streaming. They show that English was the most frequently used language, accounting for almost a third. This indicates that Nordic Twitter users choose English as a means of reaching wider audiences. The preference for English is the strongest in Denmark and the weakest in Finland. Tweeting mostly occurs late in the evening, and high-profile media events such as the Eurovision Song Contest produce considerable peaks in Twitter activity. The prevalent use of informal features such as univerbated verb forms (e.g., gotta for (HAVE) got to) supports previous findings of the speech-like nature of written Twitter data, but the results indicate that tweeters are pushing the limits even further.

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Research Article Tue, 28 Nov 2017 00:00:00 +0200