DOI: 10.3897/jucs.207303

Authors: Xin Liu, Xiang Liu

Abstract: With the proliferation of dynamic network environments such as the Internet of Things (IoT) and Vehicle-to-Everything (V2X) networks, challenges related to data localization and privacy leakage have become increasingly prominent. Efficient and privacy-preserving real-time network attack detection has emerged as a critical issue in the field of cybersecurity. Existing methods struggle to balance handling dynamic topology changes, resisting privacy attacks, and maintaining high detection accuracy. To address this, this paper proposes an attack detection framework that integrates adaptive spatio-temporal graph generation, spatio-temporal Transformer, and privacy-enhanced federated learning. The approach designs a dynamic adjacency matrix optimization mechanism to capture real-time communication relationships, incorporates gradient compression and feature decoupling strategies to effectively mitigate model degradation caused by differential privacy noi se, and introduces a deep reinforcement learning module to dynamically adjust defense strategies based on real-time traffic feedback. This enables efficient collaborative training while ensuring data remains localized. Experimental results demonstrate that the proposed method achieves accuracy rates of 93.5% and 92.0% on the UNSW-NB15 and BoT-IoT datasets, respectively. Specifically, the proposed method achieves a 3.5 percentage point improvement over the strongest privacy-preserving baseline (DP-GNN) on the BoT-IoT dataset. Moreover, under the same privacy budget, it significantly reduces the success rate of membership inference attacks. This work provides novel insights into distributed secure learning in high-privacy-risk dynamic environments, with its low-latency and high-robustness characteristics supporting real-time threat perception and privacy-secure responses on edge devices.

HTML

PDF

DOI: 10.3897/jucs.160046

Authors: Sang Suh, Numery Zaber

Abstract: Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop oversight. Using a dataset of 962 resumes across 25 categories with an 80/20 stratified split, the system reports standard multi-class performance metrics (classification report and normalized confusion matrix) for the classical pipeline. At the same time, an evaluation harness enables the same artifacts for the LLM baseline on sample subsets. The interface further exposes dataset distribution, top predicted categories, and explanation widgets (SHAP for the classical pipeline; TF-IDF key-term highlighting for the LLM), while applying basic PII filtering. Considerations related to fairness, privacy, and deployment drift are discussed. Comparison with prior SBERT-only systems (e.g., Enhanced Resume Screening using S-BERT) highlights key contributions: (1) a reproducible SBERT→XGBoost→SHAP classification pipeline, (2) an LLM comparator with a standardized evaluation template, and (3) a practitioner-oriented user interface that operationalizes explainability and auditability.

HTML

PDF

DOI: 10.3897/jucs.135666

Authors: Davut Çulha

Abstract: Widespread adoption of blockchain technologies requires scalability. To achieve scalability, various methods are applied, including new consensus algorithms, directed acyclic graph solutions, sharding solutions, and off-chain solutions. Sharding solutions are particularly promising as they distribute workload across different parts of the blockchain network. Similarly, directed acyclic graphs use graph data structures to distribute workload effectively. In this work, a binary tree data structure is used to enhance blockchain scalability. Binary trees offer several advantages, such as the ability to address nodes with binary numbers, providing a straightforward and efficient method for identifying and locating nodes. Each node in the tree contains a block of transactions, which allows for transactions to be directed to specific paths within the tree. This directionality not only increases scalability by enabling parallel processing of transactions but also ensures that the blockchain can handle a higher volume of transactions without becoming congested. Moreover, transactions are decomposed into transaction elements, improving the immutability of the binary tree blockchain. This novel decomposition process helps to minimize the computational overhead required for calculating account balances, making the system more efficient. By breaking down transactions into their fundamental components, the system can process and verify transactions more rapidly and accurately. This approach effectively realizes implicit sharding using a binary tree structure, distributing the processing load more evenly and reducing bottlenecks. The proposed method is simulated to assess its performance. Experimental results demonstrate the method's scalability, showing that it can handle a significantly higher transaction throughput compared to traditional blockchain structures.

HTML

PDF

DOI: 10.3897/jucs.121185

Authors: Emre Önal, Abdullah Bülbül

Abstract: Optimizing game elements through iterative human playtests can be time-consuming and insufficient for games with complex intransitive mechanics. Imbalances in games often require the release of numerous balance patches. We present a computational method for making each game unit equally preferable against a uniform play strategy. We leverage concepts from game theory to model intricate relationships among intransitive entities. Matching units against each other is modeled as a symmetric zero-sum game, where unit selection represents a strategy and the error is quantified using payoff values derived from unit parameters. The algorithm takes the initial unit parameters provided by the game designer and optimizes them with minimal changes using gradient descent. Consequently, the payoff matrix converges to a state where a uniform strategy is a near Nash equilibrium, ensuring that each unit is equally preferable under the optimized condition. We implemented a testing environment based on fictitious play and verified our results on different scenarios. While the majority of game theory research focuses on finding optimal strategies given specific environmental conditions, this paper takes a different perspective within the context of game design. We explore game theoretic concepts to address the goal of designing environments that lead to desired strategy choices.

HTML

PDF

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%.

HTML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

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.

HTML

XML

PDF

DOI: 10.3217/jucs-023-05-0505

Authors: Carlos Mello, Marília Saraiva, Diego Menor, Ricardo Nishihara

Abstract: This paper presents a comparison of several video quality metrics, analysing their performance against different types of distortions. Usually, comparisons are made considering a full dataset with few different degradations. We are presenting here a comparison using three very different datasets (VQEG Phase I, LIVE VQA and ReTRIeVED) and a fourth dataset which was generated in a mobile phone network simulator. This was done to check if the video quality metrics can correctly measure the degradations created by variations in the network, very close to real scenarios. The analysis was done with 13 full reference metrics (including Opticom's PEVQ commercial tool) and two no-reference metrics. We have concluded that NTIA's VQM achieved the best results, in most of the cases. It is an open source algorithm that outperformed most of the other techniques, including the licensed PEVQ.

HTML

XML

PDF

DOI: 10.3217/jucs-023-02-0208

Authors: Mariano Rico, Jaime Ramírez, Diego Riofrío-Luzcando, Marta Berrocal-Lobo

Abstract: Virtual worlds are one of the most promising and interesting lines of research and development in e-learning. Also, the recent maturity of 3D virtual world platforms like Second Life or OpenSimulator has made it possible to deploy such systems at a low cost in schools, colleges or companies. Therefore, these virtual world platforms have enabled the development of cost-effective virtual laboratories. However, there are few empirical studies to support the educational value of Virtual Worlds for Procedural Training (VWPT), especially when they are equipped with automatic tutors. In this paper we present an empirical study that provides evidence in favor of the educational value of virtual worlds. To be precise, we describe the evaluation of a biotechnology VWPT implemented on top of OpenSimulator. We have implemented an automatic tutor within this virtual laboratory in order to supervise and guide students during task practice. This tutor is configurable and can be reused in different VWPTs. This facilitates the development of VWPTs. This virtual laboratory has been evaluated by domain experts (university professors) and students over a three-year period as part of a university course. The analysis of this evaluation has shown good results for usability, user satisfaction and perceived educational utility. It has also served to demonstrate that the virtual laboratory is resilient and reliable. This supports the applied technical approach.

HTML

XML

PDF

DOI: 10.3217/jucs-022-06-0874

Authors: Piotr Jędrzejowicz, Aleksander Skakovski

Abstract: Differential evolution (DE) is a stochastic global optimization method, that has been under continuous development during the past two decades. It has been recognized that preserving the diversification of population can significantly improve the performance of DE. Although, several results and approaches to population diversification have been proposed, it seems that this issue still has a potential for development. In this paper we have studied experimentally the possibility of increasing the performance of DE. Our investigation aims at identifying how the performance of DE depends on such factors as population diversity, size and number of fitness function evaluations carried out by DE to yield a solution. In our experiments we diversified the population in an intensive manner using the proposed decloning procedure carried out in cycles, and also through increasing the population size. The choice of how to preserve the diversification may depend on restrictions imposed on the population size, response time, and the quality of solutions that should be met by a specific implementation of the algorithm. The obtained results allowed us to propose a performance improvement policy that might noteworthy improve both the efficacy and response time of the algorithm. The discrete-continuous scheduling with continuous resource discretisation was used as the test problem.

HTML

XML

PDF

DOI: 10.3217/jucs-021-06-0757

Authors: Ana Torre-Bastida, Esther Villar-Rodriguez, Sergio Gil-Lopez, Javier Ser

Abstract: The amount of open information available on-line from heterogeneous sources anddomains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can behighlighted: the "Linked Open Data" (LOD) and "Social Media" (SM) paradigms. The fusion of these data sources - structured the former, and raw data the latter -, along with the informationcontained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand andleverage their value. In this paper, we present two complementary use cases, illustrating the potential of using the open data in the business domain. The first represents the creation of an existingand potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. Thesecond focuses on the classification of organizations and enterprises aiming at detecting potential competitors and/or allies via the analysis of the conceptual similarity between their participatedprojects. To this end, a solution based on the synergy of Big Data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection, data fusionand classification supported by natural language processing (NLP) techniques, whereas the latter will deal with semantic aggregation, persistence, reasoning and information retrieval, as well aswith the triggering of alerts based on the semantized information.

HTML

XML

PDF

DOI: 10.3217/jucs-019-15-2207

Authors: Juan Frausto-Solís, Mishael Sánchez-Pérez, Ernesto Liñan-García, Juan Sánchez-Hernández, Manoj Ramachandran

Abstract: In this paper, an improved Simulated Annealing algorithm for Protein Folding Problem (PFP) is presented. This algorithm called Cluster Perturbation Simulated Annealing (CPSA) is based on a brand new scheme to generate new solutions using a cluster perturbation. The algorithm is divided into two phases: Cluster Perturbation Phase and the Reheat Phase. The first phase obtains a good solution in a small amount of time, and it is applied at very high temperatures. The second phase starts with a threshold temperature and reheats the system for a better exploration. CPSA reduces the execution time of the Simulated Annealing Algorithm without sacrificing quality to find a native structure in PFP in Ab-Initio approaches.

HTML

XML

PDF

DOI: 10.3217/jucs-019-07-0873

Authors: Diogo Azevedo, Benjamim Fonseca, Hugo Paredes, Stephan Lukosch, Jordan Janeiro, Robert Briggs

Abstract: The need for computer-supported collaboration has grown over the last years and made collaboration an important factor within organizations. This trend has resulted in the development of a variety of tools and technologies to support the various forms of collaboration. Many collaborative processes, e.g. strategy building, scenario analysis, root cause analysis and requirements engineering, require various collaboration support tools. Data flow, fishbone and brainstorming diagrams, play an important role within these synchronous collaborative applications to create, evaluate, elaborate, discuss, and revise graphical models. Currently, the necessary tools are not integrated and flexible enough to support such processes. In this paper, a synchronous collaborative brainstorming diagram editor integrated in a flexible group support system is described. This approach goes beyond the current state of the art as it can be seamlessly integrated with other collaboration support tools such as text-based brainstorming or voting. The usability of the taken approach is evaluated within a case study on collaborative learning.

HTML

XML

PDF

DOI: 10.3217/jucs-018-20-2832

Authors: Daewon Kim, Doosung Hwang

Abstract: As the augmented reality technology has become more pervasive and applicable, it is easily seen in our daily lives regardless of fields and scopes. Existing camera vision based augmented reality techniques depend on marker based approaches rather than real world information. The augmented reality technology using marker recognition has limitations in its applicability and provision of proper environment to guarantee user's immersiveness to relevant service application programs. This study aims to implement a smart mobile terminal based augmented reality technology by using a camera built in a terminal device and image and video processing technology without any markers so that users can recognize multimedia objects from real world images and build an augmented reality service, where 3D content connected to objects and relevant information are added to the real world image. Object recognition from a real world image is involved in a process of comparison against preregistered reference information, where operation to measure similarity is reduced for faster running of the application, considering the characteristics of smart mobile devices. Furthermore, the design allows users to interact through touch events on the smart device after 3D content is output onto the terminal screen. Afterward, users can browse object related information on the web. The augmented reality technology appropriate for the smart mobile environment is proposed and tested through several experiments and showed reliable performances in the results.

HTML

XML

PDF

DOI: 10.3217/jucs-018-16-2334

Authors: Javier Melero, Davinia Hernández-Leo, Josep Blat

Abstract: Information and Communication Technology (ICT) engineering education is facing a decreasing interest by students. To deal with this issue, a need for shifting from traditional learning approaches to constructivist methods has been identified. Several pedagogical methodologies based on social and constructivist theories are being applied to engage students in ICT education. This paper presents a literature review of studies carried out from 2000 to 2010 that have applied constructivist learning methods with supportive tools to specific ICT areas. From the analysis of the literature review this paper identifies the most representative constructivist learning methods within the field of ICT education. In particular, we pay attention to the educational tooling used to support the learning process and the learning benefits of applying such methods. The analysis also reveals that different combinations of guidance approaches and tooling implementations are often adopted to scaffold the learning process. With the aim of understanding to what extent and how scaffolding is present in the studied learning scenarios, this document proposes a definition of different types of scaffolding techniques. Namely: social-guidance and system-guidance scaffolding, depending on whether an individual or a tool is the responsible for providing support to students; macro-scaffolding when pedagogical methods define activity flows, or micro-scaffolding when the support is provided to perform specific actions within activities; and tool-enveloped scaffolding, when a generic tool such a learning management system scaffolds the learning process by the integration of different supportive tools, and tool-embedded scaffolding when the scaffolding is applied within a specific-purpose tooling.

HTML

XML

PDF

DOI: 10.3217/jucs-018-13-1853

Authors: Musrrat Ali, Millie Pant, Atulya Nagar, Chang Ahn

Abstract: Insertion of a local search technique is often considered an effective mechanism to increase the efficiency of a global optimization algorithm. In this paper we propose and analyze the effect of two local searches namely; Trigonometric Local Search (TLS) and Interpolated Local Search (ILS) on the working of basic Differential Evolution (DE). The corresponding algorithms are named as DETLS and DEILS. The performances of proposed algorithms are investigated and compared with basic DE, modified versions of DE and some other evolutionary algorithms. It is found that the proposed schemes improve the performance of DE in terms of quality of solution without compromising with the convergence rate.

HTML

XML

PDF

DOI: 10.3217/jucs-018-06-0750

Authors: Ickin Vural, Hein Venter

Abstract: Malicious software (malware) infects large numbers of mobile devices. Once infected these mobile devices may be involved in many kinds of online criminal activity, including identity theft, unsolicited commercial SMS messages, scams and massive coordinated attacks. Until recently, mobile networks have been relatively isolated from the Internet, so there has been little need to protect them against Botnets. Mobile networks are now well integrated with the internet, so threats on the internet, such as Botnets, have started to migrate to mobile networks. This paper studies the potential threat of Botnets based on mobile networks, and proposes the use of computational intelligence techniques to detect Botnets. We then simulate mobile Bot detection by detecting anomalies using an artificial immune system implementation on an Android device.

HTML

XML

PDF

DOI: 10.3217/jucs-017-14-2009

Authors: Roberto Konow, Wayman Tan, Luis Loyola, Javier Pereira, Nelson Baloian

Abstract: There has been a continuous development of new clustering and prediction techniques that help customers select products that meet their preferences and/or needs from an overwhelming amount of available choices. Because of the possible huge amount of available data, existing Recommender Systems showing good results might be difficult to implement and may require a lot of computational resources to perform in this scenario. In this paper, we present a more simple recommender system than the traditional ones, easy to implement, and requiring a reasonable amount of resources to perform. This system clusters users according to the frequency an item has been visited by users belonging to the same cluster, performing a collaborative filtering scheme. Experiments were conducted to evaluate the accuracy of this method using the Movielens dataset. Results obtained, as measured by the F-measure value, are comparable to other approaches found in the literature which are far more complex to implement. Following this, we explain the application of this system to an e-content site scenario for advertising. In this context, a filtering tool is shown which has been developed to filter and contextualize recommended items.

HTML

XML

PDF

DOI: 10.3217/jucs-016-12-1650

Authors: Yang Cai

Abstract: Analyzing human motion in a building has been an active subject in Ambient Intelligence and Universal Design for many years. In this study, we present a rapid prototype of a mobile and interactive sensing platform for smart buildings. The biologically inspired robot can follow the moving person around, memorize the motion patterns in form of sequences of symbols, and detect surprising events, based on similarity between the priori and posteriori probability distributions. The key modules in this study have been prototyped and tested with real-world data, such as the two–month sensory data in a building. Furthermore, the author believes that simple and recursive algorithms would enable mobile robots to simulate natural ethological intelligence.

HTML

XML

PDF

DOI: 10.3217/jucs-016-05-0852

Authors: Jessie Zou, Xiaotie Deng, Ming Ii

Abstract: The last k days of trading together tell the financial market trends. It may be inconceivable if we are told to ignore the 3rd, 6th, and 8th day, a priori. We introduce a novel approach to show exactly that - it pays to ignore some fixed days among the recent k days, fixed a priori,in order to minimize risk and maximize profit simultaneously. The theory developed here has direct implications to our common senses on how we should look at the financial market trends.

HTML

XML

PDF

DOI: 10.3217/jucs-016-01-0198

Authors: Pierre Châtel, Isis Truck, Jacques Malenfant

Abstract: This paper addresses the problem of expressing preferences among nonfunctional properties of services in a Web service architecture. In such a context, semantic and non-functional annotations are required on service declarations and business process calls to services in order to select the best available service for each invocation. To cope with these multi-criteria decision problems, conditional and unconditional preferences are managed using a new variant of conditional preference networks (CPnets), taking into account uncertainty related to the preferences to achieve a better satisfaction rate. This variant, called LCP-nets, uses fuzzy linguistic information inside the whole process, from preference elicitation to outcome query computation, a qualitative approach that is more suitable to business process programmers. Indeed, in LCP-nets, preference variables and utilities take linguistic values while conditional preference tables are considered as fuzzy rules which interdependencies may be complex. The expressiveness of the graphical model underlying CP-nets provides for solutions to gather all the preferences under uncertainty and to tackle interdependency problems. LCP-nets are applied to the problem of selecting the best service among a set of offers, given their dynamic non-functional properties. The implementation of LCP-nets is presented step-by-step through a real world example.

HTML

XML

PDF

DOI: 10.3217/jucs-015-12-2373

Authors: Hai-Cheng Chu, Der-Jiunn Deng, Han-Chieh Chao, Yueh-Min Huang

Abstract: It is an urgent, imminent and present danger that we have to focus on the traditional terrorists, who are transforming ICT into the modern attacking tools that can devastate the metropolitan areas with the deconstruction of critical infrastructures via the computer network using state-of-the-art hacking and cracking technologies. The cyber terrorists could inflict catastrophic loss or damage on civilians, corporations or the governments physically thousands of miles away and accomplish severe death polls than the traditional one. The government in the public sector or the private critical infrastructure administrators should not underestimate these potential cyber attacks. In this paper, we presented the cyber terrorism, the next generation of terrorism, to be a forthcoming and unavoidable threat to the global community as well as providing a potential rational cyber terrorist scenario, which could be the global cyber terrorism phenomena. This paper explicitly demonstrates the feasibility of launching cyber attacks toward critical infrastructures that might cause severe casualties.

HTML

XML

PDF

DOI: 10.3217/jucs-015-12-2353

Authors: Ioannis Nikolakopoulos, Charalampos Patrikakis, Antonio Cimmino, Martin Bauer, Henning Olesen

Abstract: In this paper, we present a user profile definition scheme featuring context awareness. Though the scheme has been designed to meet the needs of web applications deployed over heterogeneous devices, emphasis is given in the deployment of the profile scheme over Personal Networks (PNs), as the personalization of the deployed applications and services in PN environments is of great importance. The proposed scheme is presented as part of an integrated framework for user profile management that takes into account (and is therefore compliant to) the existing standardization attempts. The overall architecture and description of the profile management framework, taking into account security issues inside Personal Networks, is presented. The paper concludes by showcasing how user profiles have been incorporated in a selected pilot service of the EU IST research project MAGNET Beyond.

HTML

XML

PDF

DOI: 10.3217/jucs-012-09-1393

Authors: Tibor Tóth, Ferenc Erdélyi

Abstract: Introduction of the multi-degree linear education system consisting of BSc, MSc and PhD programs in the Hungarian higher education, according to the goals and requirements of the Bologna Process, necessitates reviewing, enhancing and changing the three-level subject-structures consisting of fundamental, foundation enlarging and special subjects. In the course of the last 50 years the quantity of the number of subject areas and the number of engineering branches have been increasing in technological faculties around the world and they have already reached the limits of rationality. Recently there is an increasing demand for integrated and interdisciplinary special branches such as Systems Engineering. After having surveyed the paradigm changes in the progress of engineering sciences, the paper gives a brief summary on the concept, formation and significance of Systems Engineering. The paper also deals with Production Information Engineering as a characteristic field of Systems Engineering, which offers important application possibilities for IT-based system integration.

HTML

XML

PDF

DOI: 10.3217/jucs-009-04-0300

Authors: Hermann Maurer, Ron Oliver

Abstract: In this paper we argue that in about ten years time PCs as we now know them, will no longer exist. Their functionality will be totally integrated into mobile telephony devices, or putting it differently, in ten years time mobile phones will incorporate all functions one would expect from a powerful PC. These new devices, let us call them eAssistants, will be with us all the time and will change our lives enormously. In this paper we take a first look at both the technological and applied aspects of this prediction.

HTML

XML

PDF

DOI: 10.3217/jucs-008-12-1047

Authors: Florentina Hristea

Abstract: WordNet (WN) is a lexical knowledge base, first developed for English and then adopted for several Western European languages, which was created as a machine-readable dictionary based on psycholinguistic principles. Our paper is an attempt to discuss the semiautomatic generation of WNs for languages other than English, a topic of great interest since the existence of such WNs will create the appropriate infrastructure for advanced Information Technology systems. Extending the algorithmic approach proposed in [Nikolov and Petrova, 01] we introduce a semiautomatic method based on heuristics for generating noun and adjective synsets and clusters. This choice of involved parts of speech is determined by the fact that nouns and adjectives have completely different organizations in WN: the hierarchy and the N-dimensional hyper-space respectively. Our approach to WN generation relies on so-called "class methods", namely it uses as knowledge sources individual entries coming from bilingual dictio� naries and WN synsets, but at the same time demonstrates the need to combine such methods with structural ones.

HTML

XML

PDF

DOI: 10.3217/jucs-008-05-0456

Authors: Johann Günther

Abstract: People have always been mobile. In the Middle Ages, masses moved because of pilgrimages. Today, these masses are called holiday travellers. People have never been more mobile than today. Because of increasing prosperity and the abolishment of many borders in Europe, "freedom" has become a new symbol. Freedom causes movement. We have yet to overcome the borders of the countries behind the former Iron Curtain. Our economy needed the division of labour, and therefore more mobility for goods and managers. Moreover, liberalised markets brought about the "global village". In the 21st century, the global economy will be dominated by three key industries: Telecommunications Information Technology and Tourism Changes in technologies and generations are common developments. Old styles are replaced by new ones. New technologies are replacing old ones. Telecommunications and Information Technology have propelled us into what we call the Information Society. More than 50 percent of employees in developed countries are working with information. The Information Society did not only bring about change. Its instruments also helped us to become more mobile. Mobility is nothing new for people. We can now lead nomadic lives without being accountable to the state.

HTML

XML

PDF

DOI: 10.3217/jucs-008-04-0409

Authors: Jennifer Lennon, H. Liu, Hermann Maurer

Abstract: In this paper we propose several significant advances in online employment services. We address issues such as privacy, interaction, and scalability to worldwide services. The details of each user are managed in completely relevant-to-service formats and both jobseekers & employers have considerable control over just how many of their own personal details are visible at any time. Perhaps most interestingly, virtual connections are maintained that support the various stages of negotiation in iterative, computer-supported cycles.

HTML

XML

PDF

DOI: 10.3217/jucs-007-01-0037

Authors: Heinz Treseler, Olaf Stursberg, Paul W. H. Chung, Shuanghua Yang

Abstract: The paper presents a tool architecture which supports the formal verification of logic controllers for processing systems. The tool's main intention is to provide a front-end for modelling the controller as well as the processing systems. The models are automatically transformed into representations which can be analysed by existing model checking algorithms. While the first part of the paper gives an overview of the complete architecture, the second part introduces a newly developed modelling interface: Process Control Event Diagrams (PCEDs) are formally defined as a suitable means to represent the flow of information in controlled processes. The transformation of PCEDs into verifiable code is described, and the whole procedure of modelling, model transformation and verification is illustrated with a simple processing system.

HTML

XML

PDF

DOI: 10.3217/jucs-006-03-0345

Authors: Rita Hessley, Daniel L. Morris Jr., Michael R. R. Mueller

Abstract: This paper describes the integrated use of electronic structure computations in the undergraduate chemistry curriculum including organic, physical, and analytical chemistry courses. This computational tool is used to enhance student learning and understanding of chemical principles along with exposing students to a modern research tool in chemistry. The electronic structure computations are used for homework, classroom activities (including examinations), and laboratory experiments (both computational and wet-lab). Some examples of these uses of electronic structure computations in organic, analytical, and physical chemistry courses are discussed.

HTML

XML

PDF

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.

HTML

XML

PDF