DOI: 10.3897/jucs.156553

Authors: Ozan Can Acar, Hürevren Kılıç

Abstract: High-quality random number generation is a need for many engineering application areas grounded on different theoretical bases, including probability theory & statistics, game theory, information theory, etc. Our solution to the problem is a Pseudo-Random Number Generation (PRNG) framework constituted by a Two-Dimensional Linear Cellular Automata (2D LCA) model and the Monte Carlo Tree Search method within an agent-based gamified environment. The framework employs 2D LCA as the foundation, utilizing the temporal evolution histories of cells to examine its impact on random sequence generation enabled through an intelligent agent-based gamification approach. Experimental results and evaluations showed that under the proposed framework, both agent-to-agent and agent-to-environment goal-driven game competitions facilitate high-quality random sequence generation while passing the NIST Statistical Test Suite tests, with success rates surpassing most of the existing PRNGs in the literature.

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DOI: 10.3897/jucs.166567

Authors: Sava Stanisic, Borislav Djordjevic, Branislav Belotic, Olga Ristic, Ivan Tot, Kristina Zivanovic, Dimitrije Kolasinac

Abstract: he orchestration of microservices in distributed cloud environments poses significant challenges due to the heterogeneous nature of cluster nodes and dynamic workload patterns. Traditional scheduling strategies in Kubernetes often fail to optimize latency-sensitive applications effectively. This paper proposes a latency-aware orchestration framework that integrates reinforcement learning techniques to dynamically schedule and migrate microservices across heterogeneous Kubernetes clusters. The proposed approach leverages a deep Q-network (DQN) agent trained to minimize end-to-end response times while balancing resource utilization and avoiding service-level objective (SLO) violations. Experiments conducted on a hybrid testbed comprising virtual and physical nodes demonstrate that the reinforcement learning-based scheduler reduces latency by up to 25% compared to default Kubernetes scheduling policies. The results highlight the potential of intelligent orchestration methods to enhance performance in complex cloud-native deployments.

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DOI: 10.3897/jucs.160204

Authors: Abdulhadi Altherwi, Md. Mottahir Alam, Mastoor M. Abushaega, Ahmed Hamzi, Abdulmajeed Azyabi, Shabbir Hassan, Asif Irshad Khan

Abstract: The integration of hybrid renewable energy systems (HRES) has introduced both opportunities and challenges in managing multisource power systems such as wind and solar. Accurate forecasting of HRES performance is critical to efficient planning and grid stability. This paper proposes a data efficient hybrid framework that combines Grey Wolf Optimization (GWO) for feature selection with Deep Belief Networks (DBN) for predictive modeling. GWO effectively selects relevant features from high dimensional environmental and system parameters, reducing computational burden and enhancing learning performance. The DBN is then trained on the optimized input set to forecast system performance. Two public datasets capturing wind and solar power production across distinct geographic conditions were used for validation. The proposed model significantly outperforms conventional methods, achieving a mean square error of 0.0207, RMSE of 0.144, and an energy efficiency of 98.32%. These results demonstrate the framework’s potential for deployment in smart grid forecasting environments.

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DOI: 10.3897/jucs.152114

Authors: Vahide Nida Kılıç, Esra Saraç Eşsiz

Abstract: In previous studies, the focus has predominantly been on anomaly detection, with minimal attention given to anomaly prevention. However, anomaly prevention holds greater significance than anomaly detection. Preventing anomalous behavior before it occurs and identifying potential anomalies in advance to enable timely intervention is both challenging and crucial. In this study, a Suspicious Nodes Anomaly Prevention framework for anomaly prevention has been developed. First, a novel K-medoid based Salp Swarm Anomaly Detection method is proposed within the framework. This method reveals unclustered data by applying clustering and determines the boundaries of clusters using a nature-inspired algorithm that optimizes the threshold. Since threshold determination is an optimization problem, it aligns well with nature-inspired algorithms. Additionally, the Enron email dataset was selected as it is a real-world dataset with accessible content information. Initially, content and node features were extracted from the Enron email dataset. The proposed anomaly detection method was then applied separately to each of these features. Nodes identified as anomalous by one feature but normal by others were of particular interest. These nodes were labeled as “suspicious nodes,” and their connections were analyzed to detect potentially harmful email content. This framework fills a significant gap in the anomaly detection literature by contributing an unprecedented approach to anomaly prevention, offering early intervention capabilities in various sectors by identifying risks in advance. In this study, the proposed framework demonstrates high efficacy in detecting anomalies, achieving a True Positive Rate of 94% in node-based anomaly detection and 78% in content-based anomaly detection, indicating a robust capability for early intervention and risk identification.

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DOI: 10.3897/jucs.135198

Authors: Juan Morales-García, Fernando Terroso-Sáenz, Andrés Bueno-Crespo, José M. Cecilia

Abstract: Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data. Specifically, the reinforcement learning with synthetic data benchmark, when compared to using only ground truth data, achieved a 1.22% improvement in R2, a 0.70% reduction in RMSE, a 2.97% decrease in MAE, a 27.07% reduction in MAPE, and an 18.18% improvement in CVRMSE, demonstrating its effectiveness in enhancing predictive accuracy.

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DOI: 10.3897/jucs.164692

Authors: Sergio Peñafiel, Esteban Ramírez, Nelson Baloian, Isabel Saffie, Paulo Luz, Inti Paredes

Abstract: Breast cancer is a significant global health problem, and HER2-positive breast cancer accounts for a substantial proportion of cases. The combination of Trastuzumab and Pertuzumab monoclonal antibodies with chemotherapy has demonstrated effectiveness in achieving pathologic complete response (pCR) among HER2-positive breast cancer patients. This study aims to develop an interpretable machine learning model to predict pCR in patients undergoing this neoadjuvant treatment. Previous studies have explored predictors of pCR and utilized statistical techniques, but no prior research has applied machine learning to this specific treatment. This work proposes a rule-based interpretable method based on Dempster-Shafer theory. The model is trained using a dataset of 390 patients, with 57% achieving pCR. The performance of the model is compared with other classification algorithms, demonstrating its moderate but promising results. This work highlights the importance of combining accuracy and interpretability in healthcare applications, providing insights into the factors influencing treatment response in HER2-positive breast cancer patients.

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DOI: 10.3897/jucs.150245

Authors: Fernando Ornelas, Alejandro H. García, Alejandro Santiago, Salvador Ibarra Martínez, José Antonio Castán Rocha, Fausto Balderas, Julio Laria-Menchaca, Mayra Guadalupe Treviño-Berrones

Abstract: The Internet Shopping Optimization Problem with multiple item Units (ISHOP-U) is a recently proven NP-Hard variant of the classical ISHOP, which considers buying more than one unit of the same product. In this work, we propose a new set of instances where the prices of the products follow a Gaussian distribution, which is more realistic in a competitive market than the original instances with random uniform prices. We compute the optimal values of the previous uniform and new Gaussian instances using an Integer Programming formulation in CPLEX. In addition, we also propose two new low-complexity heuristics, the first not metaheuristics approaches proposed for the ISHOP-U, which use a linear representation instead of the original matrix candidate solution, achieving better results than the previous Evolutionary Algorithms for the ISHOP-U from the state-of-the-art.

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DOI: 10.3897/jucs.114357

Authors: Samit Bhanja, Banani Ghose, Abhishek Das

Abstract: Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

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DOI: 10.3897/jucs.116916

Authors: Furkan Berk Seyrek, Halil Yiğit

Abstract: In recent years, rapid advancements in technology, particularly in the realm of artificial intelligence, have significantly transformed the landscape of lung cancer diagnosis. Early detection of lung cancer is pivotal in enhancing patient outcomes; however, traditional diagnostic methods are laborious and time-consuming. Leveraging the power of deep learning techniques, specifically utilizing established neural network architectures, offers a promising solution. This study focuses on the classification of lung images from computed tomography (CT) scans into cancerous and non-cancerous categories. By employing prevalent deep learning models, transfer learning, and rigorous evaluation metrics, this study aims to assess the efficiency of these models in accurately diagnosing lung cancer. The study uses a publicly available dataset and employs preprocessing and segmentation techniques to prepare the images for analysis. The performance of the deep learning models is evaluated on the basis of parameters such as accuracy, sensitivity, specificity, and F1 score. The results demonstrate remarkable accuracy rates, with specific architectures such as ResNet-152V2 and the proposed deep convolutional neural network architecture achieving a staggering 99.1% accuracy. These findings underscore the potential of deep learning techniques in revolutionizing lung cancer diagnosis, offering valuable support to healthcare professionals, and paving the way for more efficient and accurate diagnostic practices.

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DOI: 10.3897/jucs.98733

Authors: Sait Can Yucebas, Sukran Yalpir, Levent Genc, Melike Dogan

Abstract: The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study. This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%.

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DOI: 10.3897/jucs.97822

Authors: Herminia Beatriz Parra de Gallo, Marcela Vegetti

Abstract: This paper shows the research conducted to respond to a continuous requirement of justice regarding the application of scientifically supported forensic tools. Considering ontological engineering as the appropriate framework to respond to this requirement, the article presents OntoFoCE (Spanish abbreviation for Ontology for Electronic Mail Forensics), a specific ontology for the forensic analysis of emails. The purpose of this ontology is to help the computer expert in the validation of an email presented as judicial evidence. OntoFoCE is the fundamental component of the ObE Forensics (Ontology-based Email Forensics) tool. Although there are numerous forensic tools to analyze emails, the originality of the one proposed here lies in the implementation of semantic technologies to represent the traceability of the email transmission process. From that point on, it is possible to provide answers to the items of digital evidence subject to the expert examination. These answers make it possible to support these evidence items in the forensic analysis of an email and to guarantee the gathering of scientifically and technically accepted results that are valid for justice. Thus, the research question that is tried to be answered is: Is it possible to apply ontological engineering as a scientific support to design and develop a forensic tool that allows automatic answers to the evidence items subject to the expert examination in the forensic analysis of emails?

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DOI: 10.3897/jucs.94183

Authors: Burak Gülmez

Abstract: Plants are a big part of the ecosystem. Plants are also used by humans for various purposes. Cotton is one of these important plants and is very critical for humans. Cotton production is one of the most important sources of income for many countries and farmers in the world. Cotton can get diseases like other plants and living things. Detecting these diseases is critical. In this study, a model is developed for disease detection from leaves of cotton. This model determines whether the cotton is healthy or diseased through the photograph. It is a deep convolutional neural network model. While establishing the model, care is taken to ensure that it is a problem-specific model. The grey wolf optimization algorithm is used to ensure that the model architecture is optimal. So, this algorithm will find the most efficient architecture. The proposed model has been compared with the ResNet50, VGG19, and InceptionV3 models that are frequently used in the literature. According to the results obtained, the proposed model has an accuracy value of 1.0. Other models had accuracy values of 0.726, 0.934, and 0.943, respectively. The proposed model is more successful than other models.

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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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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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DOI: 10.3897/jucs.76528

Authors: Jing Qiu, Feng Dong, Guanglu Sun

Abstract: Disassembly is the basis of static analysis of binary code and is used in malicious code detection, vulnerability mining, software optimization, etc. Disassembly of arbitrary suspicious code blocks (e.g., for suspicious traffic packets intercepted by the network) is a difficult task. Traditional disassembly methods require manual specification of the starting address and cannot automate the disassembly of arbitrary code blocks. In this paper, we propose a disassembly method based on code extension selection network by combining traditional linear sweep and recursive traversal methods. First, each byte of a code block is used as the disassembly start address, and all disassembly results (control flow graphs) are combined into a single flow graph. Then a graph attention network is trained to pick the correct subgraph (control flow graph) as the final result. In the experiment, the compiler-generated executable file, as well as the executable file generated by hand-written assembly code, the data file and the byte sequence intercepted by the code segment were tested, and the disassembly accuracy was 93%, which can effectively distinguish the code from the data.

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DOI: 10.3897/jucs.73924

Authors: Nikola Zornić, Aleksandar Marković

Abstract: Traditionally, agent-based modelling and simulation relied on using utility function in agents’ decision-making process. Some drawbacks in this process are identified, and a potential remedy to the issue is proposed. This paper introduces a methodological framework for building a hybrid agent-based model that aims to overcome some of the elaborated problems related to the usage of a utility function. In the proposed approach, a machine learning algorithm substitutes the utility function, thus providing a possibility to use various algorithms. The proposed methodological framework has been applied to a case study of churn in a telecommunications company. Three models have been created and used for simulation experiments, two using the proposed methodology and one using utility function. The pros and cons of different approaches are identified and discussed.

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DOI: 10.3897/jucs.70484

Authors: Canan Tastimur, Erhan Akin

Abstract: Deep Learning has been widely used in image-based applications such as object classification, object detection, and object recognition in recent years. Classifying highly similar objects is a very difficult problem. It is difficult to classify datasets in this situation where object similarity between classes and differences between classes are high. In this study, Siamese Convolution Neural Network, which is a similarity measurement-based network, has been practiced to classify 6 types of screws, 5 types of nuts, and 7 types of bolts that are very similar to each other. In addition, this neural network formed with the One-Shot Learning technique is trained. Thanks to the OSL technique, there is no need to use large data sets. Also, there is no need to use large amounts of data from each class. Adding a new class to be classified is also made easier by the use of the OSL technique. The performance results of the proposed method are manifested in detail in the article.

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DOI: 10.3897/jucs.77046

Authors: Mohamed Talha, Anas Abou El Kalam

Abstract: Big Data often refers to a set of technologies dedicated to deal with large volumes of data. Data Quality and Data Security are two essential aspects for any Big Data project. While Data Quality Management Systems are about putting in place a set of processes to assess and improve certain characteristics of data such as Accuracy, Consistency, Completeness, Timeliness, etc., Security Systems are designed to protect the Confidentiality, Integrity and Availability of data. In a Big Data environment, data quality processes can be blocked by data security mechanisms. Indeed, data is often collected from external sources that could impose their own security policies. In many research works, it has been recognized that merging and integrating access control policies are real challenges for Big Data projects. To address this issue, we suggest in this paper a framework to secure data collection in collaborative platforms. Our framework extends and combines two existing frameworks namely: PolyOrBAC and SLA- Framework. PolyOrBAC is a framework intended for the protection of collaborative environments. SLA-Framework, for its part, is an implementation of the WS-Agreement Specification, the standard for managing bilaterally negotiable SLAs (Service Level Agreements) in distributed systems; its integration into PolyOrBAC will automate the implementation and application of security rules. The resulting framework will then be incorporated into a data quality assessment system to create a secure and dynamic collaborative activity in the Big Data context.

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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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DOI: 10.3897/jucs.2020.075

Authors: Vibekananda Dutta, Michał Choraś, Marek Pawlicki, Rafał Kozik

Abstract: Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

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DOI: 10.3897/jucs.2020.037

Authors: Oscar Camacho-Nieto, Cornelio Yáñez-Márquez, Yenny Villuendas-Rey

Abstract: This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.

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DOI: 10.3897/jucs.2020.014

Authors: Damir Krstinić, Maja Braović, Dunja Božić-Štulic

Abstract: Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm, but by a human as well. Since natural landscape video surveillance became relatively pervasive in recent years, in this paper we focus on the classification of natural landscape images taken mostly from forest fire monitoring towers. Since these images usually suffer from the lack of the usual low and middle level features (e.g. sharp edges and corners), and since their quality is degraded by atmospheric conditions, this makes the already difficult problem of natural landscape classification even more challenging. In this paper we tackle the problem of automatic natural landscape classiffication by proposing and evaluating a classifier based on a pretrained deep convolutional neural network and transfer learning.

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DOI: 10.3217/jucs-025-06-0611

Authors: Dominik Pieczyński, Marek Kraft, Michał Fularz

Abstract: In this paper, a method for improving the quality of person re-identification results is presented. The method is based on the assumption, that including segmentation information into re-identi_cation pipeline discards the automated detections that are of poor quality due to occlusions, misplaced regions of interest (ROI), multiple persons found within a single ROI, etc. using a simple segment number, bounding box fill rate and aspect ratio check. Assuming that a joint detector-segmented approach is used, the additional cost associated with the use of the proposed approach is very low.

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DOI: 10.3217/jucs-024-11-1582

Authors: Shadi Abudalfa, Moataz Ahmed

Abstract: Microblogging services have been significantly increased nowadays and enabled people to share conveniently their sentiments (opinions) with regard to matters of concerns. Such sentiments have shown an impact on many fields such as economics and politics. Different sentiment analysis approaches have been proposed in the literature to predict automatically sentiments shared in micro-blogs (e.g., tweets). A class of such approaches predicts opinion towards specific target (entity); this class is referred to as target-dependent sentiment classification. Another class, called open domain targeted sentiment classification, extracts targets from the micro-blog and predicts sentiment towards them. In this research work, we propose a new semi-supervised learning technique for developing open domain targeted sentiment classification by using fewer amounts of labelled data. To the best of our knowledge, our model represents the first semi-supervised technique that is proposed for open domain targeted sentiment classification. Additionally, we propose a new supervised learning model for improving accuracy of open domain targeted sentiment classification. Moreover, we show for the first time that SVM HMM is able to improve accuracy of open domain targeted sentiment classification. Experimental results show that our proposed technique outperforms other prominent techniques available in the literature.

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DOI: 10.3217/jucs-024-11-1496

Authors: Harry Luna-Aveiga, José Medina-Moreira, Oscar Apolinario-Arzube, Mario Paredes-Valverde, Katty Lagos-Ortiz, Rafael Valencia-García

Abstract: Asthma is a chronic lung disease of the airways that makes breathing difficult. Worldwide, asthma is a leading disease among children and adolescents and a leading cause of hospitalizations among adolescents. Asthma self-management is a systematic procedure that allows educating, training, and informing patients to control their disease and avoid it when it is possible and reduce it when it is necessary. Nowadays, there is a need for technological tools for supporting different tasks within the process of asthma self-management, such as education, control, and monitoring, that help patients and their families improve their quality of life and reduce the direct and indirect costs. This work proposes Astmapp, a platform that relies on semantic and mobile technologies and recommender systems to increase the patients' knowledge about asthma regarding topics such as triggers, symptoms, activity restrictions, medications, among others, and to promote the asthma control by means of the monitoring of symptoms and parameters such as physical activity, heart rate, blood pressure, temperature, among others. Likewise, Astmapp recommends educational resources based on the preferences of patients and generates medical recommendations based on the symptoms and health status of the patient aiming to prevent asthma and reduce its exacerbation. Astmapp was evaluated in terms of its ability to recommend asthma educational resources relevant for the patients as well as to provide health recommendations. The evaluation results suggest that Astmapp has the potential to effectively support the asthma self-management process.

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DOI: 10.3217/jucs-024-09-1306

Authors: Rūta Užupytė, Tomas Babarskis, Tomas Krilavičius

Abstract: Accurate information about the actual behavior of electricity users is essential to the electricity suppliers in order to ensure efficient decisions in planning pricing, e.g., designing tariffs and load planning. Load profiles of customers is a straightforward source for such data, however it should be analyzed to extract relevant information. Most of the existing techniques are tested with small data sets or over short periods, which does not allow to investigate seasonality influence. We present a new methodology for the grouping of electricity customers based on the similarities of their (hourly) consumption patterns. Approach is based on the periodicity analysis and well-known clustering technique - K-means, which is applied for identification for separate users load profiles and clustering of load profiles. Values of model parameter are selected using adequacy measures. Finally, the results obtained by this methodology with a data set of 3753 electricity customers are presented, and future plans discussed.

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DOI: 10.3217/jucs-024-06-0797

Authors: Jason Chen, Hsiao-Yen Kang, Mei-Chin Wang

Abstract: Postprandial blood glucose (PBG) elevation has been documented as a significant development of diabetes and cardiovascular diseases. Surprisingly, few studies have provided an effective model for predicting PBG elevation. This work presents the classification of PBG in a cohort study via integrating feature ranking with ensemble learning and logistic model trees. We used a cohort dataset that included 1,438 individuals from Landseed Hospital in Taiwan. Data from 2006 to 2013 were collected. To evaluate the performance of the proposed model, four well-known data mining classifiers (Naive Bayes tree algorithm, alternating decision tree, radial basis functions neural network, and Adaboost.M1) were employed in this study. The proposed model provided a reasonably accurate classification for predicting the PBG levels. Twenty-seven risk factors were identified as important risk factors for PBG elevation. The role of PBG should be emphasized and not that of PBG elevation. The predictive factors of PBG must be related to the development of certain diseases.

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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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DOI: 10.3217/jucs-024-06-0742

Authors: Chien-Sheng Cheng, Pei-Wei Shueng, Chi-Chang Chang, Chi-Wen Kuo

Abstract: Although the relationship between prognosis and oral cancer has been extensively investigated, its impact on recurrence and surgical margin has not been well studied. Clinical evaluation of a positive surgical margin in recurrent oral cancer is often challenging. The aim of this study was to propose an evidence-based diagnostic model using machine learning techniques for the prediction of risk factors of recurrent oral cancer. In addition, the performance of each technique was evaluated using accuracy, sensitivity, specificity, Fallout, F1 score, and Matthews correlation coefficient (MCC). An oral cancer dataset was provided by cancer registries of three hospitals in Taiwan. Of the 1,428 patients included in the current study, each patient in the dataset had 20 predictor variables. The results indicated that the KSTAR technique showed the best performance compared with other techniques. The GainRaito (RT) method was used in the screening to exclude five insignificant variables. The KSTAR technique also showed larger values for accuracy (77.04%), recall (77.98%), specificity (75.48%), Fallout (36.62%), F1 score (81.17%), and MCC (50.54%). Furthermore, the important risk factors for predicting recurrence in relation to the surgical margin in oral cancer were pathologic stage, behavior code, and lifestyle factors (smoking and betel nut chewing). Application of this proposed diagnostic model may facilitate targeted intervention to reduce the incidence of recurrence; however, our results suggest that adaptive machine learning techniques require incorporation of significant variables for optimal prediction.

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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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DOI: 10.3217/jucs-024-03-0302

Authors: Bruno Fernandes, Fábio Silva, Cesar Analide, José Neves

Abstract: Upcoming cities must undoubtedly reason upon the knowledge they have acquired through data gathered by sensorization. Those who do that will be at the forefront, closing to become Smart Cities. To achieve this goal, we must evolve from an Internet of Things to an Internet of People, defined as an ecosystem where everyone and everything can sense the other and the world, and act upon such data and knowledge, aiming to enhance people's quality of life. Considering the Vulnerable Road Users' (VRUs) problem, this work provides a proof of concept on crowd sensing for urban security in Smart Cities, confirming that our concept is viable and has practical potential. The goal is to sense the density of people at certain points of interest for VRUs, such as pedestrian crossings or busier roads, by detecting Wi-Fi probe requests with a Smart Scanner. Such information can be relevant to many applications, allowing, for example, the promotion of better safety measures on crowed spots, enhance crowd control and assemble interesting insights on traffic characteristics. To complement this work, and considering that smart clothing will certainly play an important role in promoting the citizen sensor and the smart cities' approach to VRUs' safety, a case study will be presented and discussed in which their clothing is equipped with a Bluetooth Low Energy transmitter. This allows such users to be recognized on the road, which may help avoid dangerous situations. The proof of concept was a success, with the developed software showing promising results at an extremely low price.

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DOI: 10.3217/jucs-022-06-0802

Authors: Bay Vo, Tam Tran, Tzung-Pei Hong, Nguyen Minh

Abstract: Using soft set theory for mining maximal association rules based on the concept of frequent maximal itemsets which appear maximally in many records has been developed in recent years. This method has been shown to be very effective for mining interesting association rules which are not obtained by using methods for regular association rule mining. There have been several algorithms developed to solve the problem, but overall, they retain weaknesses related to the use of memory as well as mining time. In this paper, we propose an effective strategy for maximal rules mining based on soft set theory that consists of the following steps: 1) Build tree Max_IT_Tree where each node contains maximal itemsets X, the category of X, the set of transactions in which X is maximal, and the support of the maximal itemsets X for each category. 2) From the tree Max_IT_Tree built in previous steps, build a tree Max_Item_IT_Tree so that each maximal itemset has child nodes where each node contains items with categories different from the category of maximal itemsets. 3) Generate maximal association rules which satisfy predefined minimum M-support (min M-sup) and minimum M-confidence (min M-conf) thresholds.

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DOI: 10.3217/jucs-022-03-0416

Authors: Jarosław Jankowski, Kostas Kolomvatsos, Przemysław Kazienko, Jarosław Wątróbski

Abstract: An important aspect of managing social platforms, online games and virtual worlds is the analysis of user characteristics related to subscriptions and virtual goods purchases. The results of such a process could be adopted in decision support applications that build on top of users' behavior provide efficient strategies for the virtual world's management. One of the research questions in this area is related to the factors affecting purchases and their relation to the activity within social networks as well as the ability to use past data to make reasoning about future behaviors. Complex online systems are hard to analyze when adopting legacy methodologies due to the huge amount of data generated by users' activity and changes in their behavior over time. In this paper, we discuss an analysis of the characteristics of users performing purchases for virtual products. We adopt a Neuro-Fuzzy system which has the ability to process data under uncertainty towards better decisions related to parameterization of the virtual retail system. The proposed Fuzzy Logic (FL) inference model focuses on the analysis of purchases based on the types of past transactions and social activity as inputs. The proposed system results values for specific parameters affecting/depicting users’ behavior like own purchases, gifting and virtual products usage as output. Our results could be adopted for decision support of online platform operators and show the relations between less and more experienced users in terms of frequency and value of purchases, engagement with the use of virtual goods and gifting behaviors. Models based on the social activity with distinguished inbound and outbound social connections show increased interest in virtual goods among users with a higher number of inbound connections as a possible tool for building social position.

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DOI: 10.3217/jucs-020-11-1523

Authors: Luis M. Álvarez Sabucedo, Roberto Barreiros, Juan M. Santos Gago, Manuel J. Fernández Iglesias

Abstract: This paper addresses the recommendation of online services provided by public administrations taking into account both the specific characteristics of these services and the perception of other citizens. The solution discussed is based on an enhanced hybrid model that relies on content-based and collaborative strategies aimed to exploit the information shared by other users to validate the quality of the recommendations provided. As a relevant feature, the proposed schema takes advantage of an automatic compensation of the mentioned strategies. To make the most of theses two approaches, the use of semantics is introduced to describe knowledge and to make smart recommendation decisions. To facilitate the task of other researchers and practitioners, details about the actual development and validation of the proposed model are also included in the paper, making it possible its replication in other environmentsEurope is involved in a process of transition to digital terrestrial television that is aimed to replace all analog broadcasting infrastructures into digital ones by year 2012. Besides the substitution of all broadcasting networks scattered around Europe, this process includes the replacement of all household elements related to the reception of terrestrial television emissions, namely television appliances and antenna settings. As in any major change in the every-day life of citizens, public administrations must keep citizen informed and provide convenient support, specially when dealing with the a communication medium designated to be the carriers of services and information. This paper tackles how this situation has been faced in Galicia, a European region with special needs in this area, as shown in the paper. Through a successful use case based on Geographical Information Services and Web2.0 technologies, we illustrate some features not present in related initiatives in other areas.

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DOI: 10.3217/jucs-020-04-0507

Authors: Ingrid Christensen, Silvia Schiaffino

Abstract: Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.

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DOI: 10.3217/jucs-019-15-2292

Authors: Javier Fernández, Juan Augusto, Giuseppe Trombino, Ralf Seepold, Natividad Madrid

Abstract: Traders are required to work in the financial market with highly complex information and to perform efficiently under high levels of psychological pressure. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem not yet considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This paper takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision-making. The system has been designed considering both technical and physiological aspects to make information available in a suitable way. Biometric sensors are used to collect data associated with stress, a software platform then analyses this information and displays it to the trader. The resulting system is capable of making individual traders, as well as teams of traders, self-aware of their levels of stress. The system has been tested in real environments and the results provide evidence that self-aware traders benefit from the system by reducing risky decision-making.

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DOI: 10.3217/jucs-018-16-2319

Authors: María-Teresa Martín-Valdivia, Arturo Montejo-Ráez, Alfonso Ureña-López, Mohammed Saleh

Abstract: Sentiment analysis is a challenging research area due to the rapid increase of subjective texts populating the web. There are several studies which focus on classifying opinions into positive or negative. Corpora are usually labeled with a star-rating scale. However, most of the studies neglect to consider neutral examples. In this paper we study the effect of using neutral sample reviews found in an opinion corpus in order to improve a sentiment polarity classification system. We have performed different experiments using several machine learning algorithms in order to demonstrate the advantage of taking the neutral examples into account. In addition we propose a model to divide neutral samples into positive and negative ones, in order to incorporate this information into the construction of the final opinion polarity classification system. Moreover, we have generated a corpus from Amazon in order to prove the convenience of the system. The results obtained are very promising and encourage us to continue researching along this line and consider neutral examples as relevant information in opinion mining tasks.

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DOI: 10.3217/jucs-018-01-0005

Authors: Valentina Gatteschi, Fabrizio Lamberti, Claudio Demartini, Rob Van Wezel, Simonetta Bettiol

Abstract: In recent years, mobility of students and workers started to be considered as a way for answering skill shortages in the European labor world. However, in order to implement effective mobility practices, suitable instruments supporting transparency and readability of the outcomes of learning processes as well as of the needs of companies and employers in general have to be developed. In fact, though some instruments have been introduced (like, for instance, the European Qualification Framework, EQF, the European Credit system for Vocational Education and Training, ECVET, etc.), they are often seen as theoretical tools rather than a practical help for involved stakeholders. In this work, the results of the TIPTOE project, a transnational initiative funded by the European Commission under the Lifelong Learning Programme are illustrated. In particular, the semantic-based methodology for the construction of a European-wide profile mixing education and labor worlds perspectives in the trade sector is discussed. Furthermore, a number of tools allowing end-users to compare owned qualifications to the reference one and supporting stakeholders in the reading of national educational and occupational profiles in the EQF dimension are presented.

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DOI: 10.3217/jucs-017-01-0081

Authors: Rakesh Rampalli, Angarai Ramakrishnan

Abstract: This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

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DOI: 10.3217/jucs-016-11-1410

Authors: Lledó Museros, Luis González-Abril, Francisco Velasco, Zoe Falomir

Abstract: This paper presents a qualitative shape description scheme which has been defined in order to have a formal theory to allow the construction of new shapes from a set of given shapes by using a juxtaposition operation. Specifically, the qualitative shape description scheme defined is a pragmatic scheme since it has been defined in order to be applied in the automatic and intelligent assembling of trencadís mosaics.

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DOI: 10.3217/jucs-016-09-1234

Authors: Andres Ortega, Jose M. Alcaraz Calero, Juan Botía, Gregorio Pérez, Felix J. Garcia Clemente

Abstract: Ontology rule editing, testing, debugging and validation are still handcrafted and painful tasks. Nowadays, there is a lack of tools that take these tasks into consideration in order to ease the work of the developer. This paper is devoted to explain how we have come to a new tool, ORE (Ontology Rule Editor), which significantly eases these tasks. It rests on a Semantic Web framework together with reasoning engines, which operate with semantic representations. Its design maintains a loosely coupling from the framework and from rule engines. Collaborative functionalities have been tackled in order to enable a real integration of the rule authoring across different tools and/or users. A practical validation of the approach by instantiating our tool with Jena and Pellet reasoning engines is presented here. In order to demonstrate its use, the tool is applied to the task of rule-based management in a ubiquitous computing scenario.

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DOI: 10.3217/jucs-016-01-0121

Authors: Özgür Kabak, Füsun Ülengin

Abstract: Several supply chain and production planning models in the literature assume the demands are fuzzy but most of them do not offer a specific technique to derive the fuzzy demands. In this study, we propose a methodology to obtain a fuzzy-demand forecast that is represented by a possibilistic distribution. The fuzzy-demand forecast is found by aggregating forecasts based on different sources; namely statistical forecasting methods and experts’ judgments. In the methodology, initially, the forecast derived from the statistical forecasting techniques and experts’ judgments are represented by triangular possibilistic distributions. Subsequently, those results are combined by using weights assigned to each of them. A new objective weighting approach is used to find the weights. The proposed methodology is illustrated by an example and a sensitivity analysis is provided.

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DOI: 10.3217/jucs-015-09-1907

Authors: Weidong Li, Liang Gao, Xinyu Li

Abstract: Manufacturing planning is crucial for the quality and efficiency of product development. Process planning and scheduling are the most important and challenging tasks in manufacturing planning. These two processes are usually arranged in a sequential way. Recently, a significant trend is to make the processes to work more concurrently and cooperatively to achieve a globally optimal result. In this paper, several intelligent strategies have been developed to build up Cooperative Process Planning and Scheduling (CPPS). Three Game Theory-based strategies, i.e., Pareto strategy, Nash strategy and Stackelberg strategy, have been introduced to analyze the cooperative integration of the two processes in a systematic way. To address the multiple constraints in CPPS, a fuzzy logic-based Analytical Hierarchical Process (AHP) technique has been applied. Modern heuristic algorithms, including Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GAs), have been developed and applied to CPPS to identify optimal or near-optimal solutions from the vast search space efficiently. Experiments have been conducted and results show the objectives of the research have been achieved.

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DOI: 10.3217/jucs-015-09-1852

Authors: Kelvin Leong, Junco Li, Stephen Chan, Vincent Ng

Abstract: Dynamic pattern analysis refers to analyzing the relationship of spatial patterns at different time points. Traditional spatial pattern analysis such as data clustering can find the spatial patterns extant at a geographical location at a particular time point but failing to identify spatial dynamics, or changes that occur over time in a particular place. In this paper, we present a dynamic pattern analysis framework, the DPA framework. This framework allows user to identify three types of dynamic patterns in spatial-temporal data: 1) similar spatial patterns at different time points, 2) interactive relationship between two geographical locations as a result of a specific reason and 3) frequent association rules related to particular types of events, geographical locations, and time points. To evaluate the proposed framework, we used it to analyze a set of reported crime data for a district of Hong Kong and compared the identified patterns with some expectations of field experts and prior empirical studies for this kind of data and patterns. In line with expert predictions, we found strong correlations between school holidays and crime clusters. On the contrary, in our data set, we could not find obvious seasonal dependency. These findings are corroborated by related empirical crime studies.

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DOI: 10.3217/jucs-015-04-0877

Authors: Trong Duong, Geun-Sik Jo, Jason Jung, Ngoc Nguyen

Abstract: Most previous research on ontology integration has focused on similarity measure-ments between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs of considering all possible pairs between two given ontologies. In this paper, we propose a novel approach to reducing computational complexity in ontology integration. Thereby, we address the importance and types of concepts, for priority matching anddirect matching between concepts, respectively. Identity-based similarity is computed, to avoid comparisons of all properties related to each concept, while matching between concepts. Theproblem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.

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DOI: 10.3217/jucs-014-16-2720

Authors: Gitte Lindgaard, Catherine Pyper, Monique Frize, Robin Walker, Craig Boutilier, Bowen Hui, Sheila Narasimhan, Janette Folkens, Bill Winogron, Peter Egan, Colin Jones

Abstract: Decision Support Systems are proliferating rapidly in many areas of human endeavour including clinical medicine and psychology. While these are typically based on rule-based systems, decision trees, or Artificial Neural Networks, this paper argues that Bayes’ Theorem can be applied fruitfully to support expert decisions both in dynamically changing situations requiring the system progressively to adapt, and when this is not the case. One example of each of these two types is given. One provides diagnostic support for human decision makers; the other, an e-health mental intervention system provides decision rules enabling it to respond and provide the most appropriate training modules to input from clients with changing needs. The contributions of psychological research underlying both systems is summarized.

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DOI: 10.3217/jucs-014-07-1080

Authors: Vedran Podobnik, Ana Petric, Gordan Jezic

Abstract: Supply chain management (SCM) deals with planning and coordinating activities such as material procurement, product assembly, and the distribution of manufactured products. This paper offers an agent-based solution as a potentially adequate approach for the automation of supply chain management. The greatest obstacle in SCM research is obtaining benchmark designed solutions since it is difficult to simulate real business environments, while live testing in real-world systems is not an option. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed for studying and prototyping SCM agents by providing a challenging game environment where competing agents engage in complex decision-making activities with the purpose of maximizing their profit. In this paper, we describe the TAC SCM environment and present the main features of the CrocodileAgent, our TAC SCM 2007 entry. Additionally, the CrocodileAgent’s performance in the competition, as well as in a series of controlled experiments, is discussed.

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DOI: 10.3217/jucs-014-07-1048

Authors: Grażyna Brzykcy, Jerzy Bartoszek, Tadeusz Pankowski

Abstract: We propose specification of schema mappings and agents' actions in XML data integration task. We discuss the problem in a highly-dynamic environment consisting of a community of peer-to-peer cooperating partners (agents). Peers decide how to describe their local data, when to join and when to leave the system, how to communicate and share their information with partners. An agent responds to the query by asking its partners (friends), which are able to partly answer the query. All the answers are merged and final result is constructed. A peer propagates a query along semantic paths existing in the system. Semantic paths are determined by schema mappings defined between partners. We propose a method for specifying schema mappings and to translate them to XQuery expressions. Mappings are represented by means of logical formulas. We also propose a declarative specification of semantic-driven communication in the system. The specification is made in a peer-oriented extension of Prolog.

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DOI: 10.3217/jucs-013-09-1184

Authors: Simona Colucci, Tommaso Noia, Eugenio Di Sciascio, Francesco Donini, Azzurra Ragone

Abstract: Knowledge management is characterized by many different activities ranging from the elicitation of knowledge to its storing, sharing, maintenance, usage and creation. Skill management is one of such activities, with its own peculiarities, as it focuses on full exploitation of knowledge individuals in an organization have, in order to carry out at best given tasks. In this paper a semantic-based automated Skill Management System is proposed, which supports competences search and creation. The system implements an approach exploiting the formalism and the reasoning services provided by Description Logics. The approach embeds also non standard Description Logics reasoning services to extend the set of provided features. Here we present main characteristics of our system and focus on a novel algorithm exploiting advanced inference services for the one-to-one assignment of a set of individuals to a set of tasks, endowed of logical explanation features for missing/conflicting skills.

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DOI: 10.3217/jucs-012-11-1500

Authors: Maryam Bavarian, Veronica Dahl

Abstract: The need for processing biological information is rapidly growing, owing to the masses of new information in digital form being produced at this time. Old methodologies for processing it can no longer keep up with this rate of growth. The methods of Artificial Intelligence (AI) in general and of language processing in particular can offer much towards solving this problem. However, interdisciplinary research between language processing and molecular biology is not yet widespread, partly because of the effort needed for each specialist to understand the other one's jargon. We argue that by looking at the problems of molecular biology from a language processing perspective, and using constraint based logic methodologies we can shorten the gap and make interdisciplinary collaborations more effective. We shall discuss several sequence analysis problems in terms of constraint based formalisms such Concept Formation Rules, Constraint Handling Rules (CHR) and their grammatical counterpart, CHRG. We postulate that genetic structure analysis can also benefit from these methods, for instance to reconstruct from a given RNA secondary structure, a nucleotide sequence that folds into it. Our proposed methodologies lend direct executability to high level descriptions of the problems at hand and thus contribute to rapid while efficient prototyping.

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DOI: 10.3217/jucs-012-06-0581

Authors: Benjamin Blankertz, Guido Dornhege, Steven Lemm, Matthias Krauledat, Gabriel Curio, Klaus-Robert Müller

Abstract: We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main contribution of the BBCI, which is a non-invasive EEG-based BCI system, is the use of advanced machine learning techniques that allow to adapt to the specific brain signatures of each user with literally no training. In BBCI a calibration session of about 20min is necessary to provide a data basis from which the individualized brain signatures are inferred. This is very much in contrast to conventional BCI approaches that rely on operand conditioning and need extensive subject training of the order 50-100 hours. Our machine learning concept thus allows to achieve high quality feedback already after the very first session. This work reviews a broad range of investigations and experiments that have been performed within the BBCI project. In addition to these general paradigmatic BCI results, this work provides a condensed outline of the underlying machine learning and signal processing techniques that make the BBCI succeed. In the first experimental paradgm we analyze the predictability of limb movement long before the actual movement takes place using only the movement intention measured from the pre-movement (readiness) EEG potentials. The experiments include both off-line studies and an online feedback paradigm. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of left vs. right hand rsp. foot. In a second conplementary paradigm voluntary modulations of sensorimotor rhythms caused by motor imagery (left hand vs. right hand vs. foot) are translated into a continuous feedback signal. Here we report results of a recent feedback study with 6 healthy subjects with no or very little experience with BCI control: half of the subjects achieved an information transfer rate above 35 bits per minute (bmp). Furthermore one subject used the BBCI to operate a mental typewriter in free spelling mode. The overall spelling speed was 4.5-8 letters per minute including the time needed for the correction errors.

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DOI: 10.3217/jucs-012-02-0127

Authors: Ahmad Almonayyes

Abstract: Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the first phase, a discrimination network of multiple questions is used to retrieve explanatory knowledge structures each of which gives an interpretation of a text according to a particular aspect of fanaticism. Explanation structures organize past documents of fanatic content. Similar documents are retrieved to generate additional valuable information about the new document. In the second phase, the document classification process based on Naïve Bayes is used to classify documents into their fanatic class. The results show that the classification accuracy is improved by incorporating the explanation patterns with the Naïve Bayes classifier.

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DOI: 10.3217/jucs-012-01-0045

Authors: Timothy Sullivan, John Donoghue, John Herbert, Richard Studdert

Abstract: Telemedicine applications on a medical practitioner's mobile device should be context-aware. This can vastly improve the effectiveness of mobile applications and is a step towards realising the vision of a ubiquitous telemedicine environment. The nomadic nature of a medical practitioner emphasises location, activity and time as key context-aware elements. An intelligent middleware is needed to effectively interpret and exploit these contextual elements. This paper proposes an agent-based architectural solution called Context-Aware Mobile Medical Devices (CAMMD). This framework can proactively communicate patient records to a portable device based upon the active context of its medical practitioner. An expert system is utilised to cross-reference the context-aware data of location and time against a practitioner's work schedule. This proactive distribution of medical data enhances the usability and portability of mobile medical devices. The proposed methodology alleviates constraints on memory storage and enhances user interaction with the handheld device. The framework also improves utilisation of network bandwidth resources. An experimental prototype is presented highlighting the potential of this approach.

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DOI: 10.3217/jucs-011-11-1752

Authors: Martin Atzmueller, Frank Puppe

Abstract: Visual mining methods enable the direct integration of the user to overcome major problems of automatic data mining methods, e.g., the presentation of uninteresting results, lack of acceptance of the discovered findings, or limited confidence in these. We present a novel subgroup mining approach for explorative and descriptive data mining implemented in the VIKAMINE system. We propose several integrated visualization methods to support subgroup mining. Furthermore, we describe three case studies using data from fielded systems in the medical domain.

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DOI: 10.3217/jucs-010-01-0014

Authors: Maja Pivec, Olga Dziabenko

Abstract: How to design effective learning opportunities? Why is learning by experience often more efficient than learning by studying? How to provide the learning experiences needed to respond to current challenges? Using computer games and games in general for educational purposes offers a variety of knowledge presentations and creates opportunities to apply the knowledge within a virtual world, thus supporting and facilitating learning processes. An innovative educational paradigm such as game-based learning, which is considered suitable for the given purpose, is described in this article. The connection of the collaborative social context of education with game-based learning is discussed in the first part of the paper. The second part of the paper introduces the game concept of "UniGame: Social Skills and Knowledge Training". Game ideas along the educational background of the UniGame game concept are outlined. UniGame scenarios presented and possible use cases should stimulate users to apply game-based learning approach in the future for their classes.

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DOI: 10.3217/jucs-009-09-1073

Authors: Florence Ber, Amedeo Napoli, Jean-Luc Metzger, Sylvie Lardon

Abstract: In this paper, we present the knowledge­based system rosa working on spatial and functional organizations in agriculture. The reasoning in rosa combines hierarchical classification, case-based reasoning, and qualitative spatial reasoning. The goal of the system is twofold: formalizing and building a case base holding on farm spatial and functional organizations, and helping the analysis of new cases. Domain knowledge and cases are modeled with the help of the so-called spatial organization graphs (sogs), and represented within a description logic system. Hierarchical case-based reasoning, involving classification and qualitative spatial reasoning, is used to compare and explain farm spatial structures modeled by sogs. An example of case retrieval is proposed, followed by a global discussion on case­based reasoning in the rosa system and related work.

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DOI: 10.3217/jucs-009-07-0682

Authors: Eddie Moench, Mike Ullrich, Hans-Peter Schnurr, Jürgen Angele

Abstract: During the analysis of knowledge processes in enterprises it often turns out that simple access to existing enterprise knowledge which is covered in documents is not possible. To enable access to a companys document and data stocks Information Retrieval (IR) technologies play a central role. In the following we describe the underlying theory of the SemanticMiner system, including methods and technologies as well as continuing approaches to obtain Knowledge Retrieval (KR) by dint of semantic technologies.

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DOI: 10.3217/jucs-009-07-0608

Authors: Ralph Bergmann, Martin Schaaf

Abstract: This article addresses the relations between ontology-based knowledge management implemented by logic-oriented knowledge representation/retrieval approaches and knowledge management using case-based reasoning. We argue that knowledge management with CBR does not only very much resemble but indeed is a kind of ontology-based knowledge management since it is based on closely related ideas and a similar development methodology, although the reasoning paradigms are different. Therefore, we conclude by proposing to merge logic-oriented and case-based retrieval and also to extend the current view of the semantic web architecture respectively.

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DOI: 10.3217/jucs-008-08-0751

Authors: Florence Ber, Amedeo Napoli

Abstract: Our work is concerned with the design of a knowledge-based system for recognizing agricultural landscape models on land-use maps. Landscape models are defined as sets of spatial structures and spatial relations. This paper focuses on the representation of topological relations inside an object-based representation system. In this system, relations are represented by objects with their own properties. We propose to define two types of properties: the first ones are concerned with relations as concepts while the second are concerned with relations as links between concepts. In order to represent the second type of properties, we have defined facets that are inspired from the constructors of description logics. We describe these facets and how they are used for classifying spatial structures and relations on land-use maps. The paper ends with a discussion on the present work and related work in qualitative spatial reasoning.

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DOI: 10.3217/jucs-008-05-0491

Authors: Mark Maybury

Abstract: This article outlines new technologies in the areas of automated expertise finding, expert network discover, virtual place-based collaboration, and automated question answering. We illustrate each of these areas with implemented and in some cases empirically evaluated systems. Collectively, these illustrate new methods for automatic discovery of knowledge, experts, and communities in an effective and efficient manner.

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DOI: 10.3217/jucs-003-08-0929

Authors: Otto Kühn, Andreas Abecker

Abstract: A core concept in discussions about technological support for knowledge management is the Corporate Memory. A Corporate or Organizational Memory can be characterized as a comprehensive computer system which captures a company's accumulated know-how and other knowledge assets and makes them available to enhance the efficiency and effectiveness of knowledge-intensive work processes. The successful development of such a system requires a careful analysis of established work practices and available information-technology (IT) infrastructure. This is essential for providing a cost-effective solution which will be accepted by the users and can be evolved in the future. The current paper compares and summarizes our experiences from three case studies on Corporate Memories for supporting various aspects in the product life-cycles of three European corporations. Based on the conducted analyses and prototypical implementations, we sketch a general framework for the development methodology, architecture, and technical realization of a Corporate Memory.

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DOI: 10.3217/jucs-003-08-0835

Authors: Uwe Borghoff, Remo Pareschi

Abstract: Knowledge has been lately recognized as one of the most important assets of organizations. Can information technology help the growth and the sustainment of organizational knowledge? The answer is yes, if care is taken to remember that IT here is just a part of the story (corporate culture and work practices being equally relevant) and that the information technologies best suited for this purpose should be expressly designed with knowledge management in view. This special issue of the Journal of Universal Computer Science contains a selection of papers from the First Conference on Practical Applications of Knowledge Management. Each paper describes a specific type of information technology suitable for the support of different aspects of knowledge management.

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DOI: 10.3217/jucs-002-02-0070

Authors: Plamena Andreeva

Abstract: The inexact information system is based on linguistic terms which have values lying in the interval [0,1]. Imprecision has advantages, because fuzzy sets avoid the rigidity of conventional mathematical reasoning and computer programming. Fuzzy quantifiers are made explicit by means of fuzzy logic. Many systems, for example, complex biological processes, cannot be programmed in a precise way. With fuzzy sets the implicit quantifiers can be easily translated into machine usable form. This paper discusses a method for the description of fuzzy quantifiers in formal languages. A comparison between approximate reasoning and the method of linear interpolation is made. Inexact information in biological and medical expert systems, and the reliability inferences based on it, are also discussed.

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