DOI: 10.3897/jucs.140979

Authors: Kadir Ileri, M. Şamil Balcı, Adem Dalcalı

Abstract: Energy harvesting is an effective solution, especially in scenarios with low power requirements, using sources such as magnetic fields, vibrations, and wind. This study focuses on predicting harvested power of toroidal electromagnetic energy harvesters using various machine learning methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Adaptive Boosting (AdaBoost), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest, Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). To enhance the performance of these models, Artificial Bee Colony (ABC) optimization has been applied. The experiments were conducted using 1,300 trials across seven toroidal cores with varying sizes and magnetic permeabilities. During each experiment, the line current was varied between 0–100 A, and the resulting induced voltage and current were recorded. These measurements were used to create a comprehensive dataset named the Toroidal-Energy-Harvesting Dataset, enabling accurate power prediction. The performance of the machine learning models was assessed using statistical metrics, including R², MSE, MAE, and RMSE. Among the evaluated models, the ABC-optimized XGBoost (ABC-XGBoost) demonstrated the highest performance, achieving an R² value of 0.9993, an MSE of 247.1, an MAE of 9.8, and an RMSE of 15.7, indicating superior accuracy and minimal error. The comparative analysis clearly shows that proposed ABC-XGBoost outperformed the other models, making it the most effective solution for accurate power prediction in the Toroidal-Energy-Harvesting Dataset.

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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.141629

Authors: Samet Aymaz

Abstract: oronary artery disease (CAD) is a major global health issue. Early detection plays a crucial role in reducing risk and improving patient outcomes. This study proposes a novel, efficient approach to CAD diagnosis by integrating a histogram-based feature selection method with a specially designed long short-term memory (LSTM) classifier. The method is evaluated on two benchmark datasets: Z-Alizadeh Sani and Cleveland. Imbalanced class distribution, a common challenge in medical datasets, is addressed using the synthetic minority over-sampling technique (SMOTE). The proposed feature selection technique offers a fast and simple alternative to traditional optimization methods like particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), and the whale optimization algorithm (WOA), which typically require extensive parameter tuning and longer processing times. The histogram-based method selects features based on their distribution similarity to a Gaussian profile, aiming to enhance classification performance and computational efficiency. The selected features are then classified using a custom-designed LSTM architecture optimized through Grid Search and validated via k-fold cross-validation (k-fold). The effectiveness of the proposed method is demonstrated by comparing it with other feature selection approaches using metrics such as accuracy, precision, sensitivity, and the F1-score (f-score). Experimental results show that the histogram-based method significantly improves classification accuracy and reduces computational time. This approach offers a promising, low-cost, and scalable solution for CAD diagnosis, especially in resource-constrained settings, and provides valuable contributions to the field of medical data analysis.

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DOI: 10.3897/jucs.119828

Authors: Seyedeh Aridis Ahadi, Kian Jazayeri, Sahand Tebyani

Abstract: The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver algorithms are applied to the network. The mixture of these techniques forms 82 distinct methodologies employed, followed by comparing the results obtained. A collection of approximately 60,0000 user posts from 2018 to 2020 was compiled from Reddit for the study. It has resulted in the combination of TF-IDF (word embedding), RReLU (activation function), and Adam (solver algorithm) reaching the highest overall performance. The model achieved impressive accuracy, F1 Score, and AUC of 86%, with precision and recall score of 91% and 82% respectively. It was fitted in just 8.69 seconds, demonstrating its time efficiency as well. This approach has great potential for creating a platform in real life to not only reduce the social impacts of suicidality and mental illness, but also increase social access to mental health resources for all individuals.

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DOI: 10.3897/jucs.111879

Authors: Sahar Idwan, Junaid Ahmed Zubairi, Syed Ali Haider, Wael Etaiwi

Abstract: Traffic management is one of the major factors in growth strategy formulation in urban centers across the globe. The increasing population and, therefore, the increase in the number of vehicles on roads in urban centers cause congested traffic patterns. These patterns typically emerge on intersections in busy city roads at various times during the day, especially during peak hours. A direct consequence of congestion is the increase in commute time and pollution. This paper presents a hierarchical graph-based congestion control (HGCC) method. Congestion values are set and evaluated as a two-level hierarchical graph. The least congestion path algorithm (LCP)is integrated with the HGCC to compute the optimal route between source and destination. The experimental results for a Manhattan-like grid network, together with the paired-sample t-test, show that the proposed method is efficient in achieving good congestion-avoiding routes.

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DOI: 10.3897/jucs.104093

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

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

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DOI: 10.3897/jucs.103738

Authors: Yunwu Xu, Yan Li

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

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DOI: 10.3897/jucs.97743

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

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

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DOI: 10.3897/jucs.96377

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

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

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DOI: 10.3897/jucs.84647

Authors: Purvaja Balaji, Helena Merker, Amar Gupta

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

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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.72590

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

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

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DOI: 10.3897/jucs.77793

Authors: Igor Tomicic, Markus Schatten, Vadym Shkarupylo

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

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DOI: 10.3897/jucs.80750

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

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

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DOI: 10.3897/jucs.66979

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

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

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DOI: 10.3897/jucs.72314

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

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

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DOI: 10.3897/jucs.70325

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

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

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DOI: 10.3217/jucs-025-13-1687

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

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

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DOI: 10.3217/jucs-025-10-1353

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

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

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DOI: 10.3217/jucs-025-10-1279

Authors: Samet Dinçer, Yıltan Bitirim

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

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DOI: 10.3217/jucs-025-10-1240

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

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

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DOI: 10.3217/jucs-025-08-0988

Authors: Valeria Herskovic, Sergio Ochoa, José Pino

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

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DOI: 10.3217/jucs-024-10-1452

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

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

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DOI: 10.3217/jucs-022-06-0836

Authors: Kazimierz Choroś, Jacek Topolski

Abstract: The paper describes and extensively evaluates a new method for the parameterized and intelligent generation of an infinite environment including various biomes in a virtual 3D space defined by the user. The biomes might be generated with different set of textures and by using different formulas to form shape of the landscape. Despite different shapes they still blend smoothly between each other. To achieve this goal Gaussian blur and Voronoi diagram algorithms are used. To enable additional parameterization for biomes placement the standard Voronoi algorithm is extended by including cell change along an axis and dispersion of Voronoi cells. Applying latitude into terrain type produces more realistic results. An entire terrain is generated on the CPU using a separate thread to eliminate stuttering during calculations and then the data is sent to the GPU in order to draw it. It forces reducing amount of data as much as possible, because data must be sent through the graphical bus. The tests have been performed by setting up a sample terrain and performing basic actions on this terrain like moving or rotating to gather frame times. The results showed that although the method demands much memory it is efficient and suitable for the real-time processing.

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DOI: 10.3217/jucs-021-12-1654

Authors: Pengfei Sun, Philippe Bon, Simon Collart-Dutilleul

Abstract: Model transformation is an interesting task, which could take advantage of several modelling languages, and meanwhile should respect all the safety requirements. The presented work studies the translation from a valid design solution to a valid implementation, which is a mapping method from coloured Petri nets to abstract B machines. Both modelling languages are well known formal methods in the context of safety requirement engineering. The Petri nets are widely accepted by French railway engineers because of a fine graphic representation and their dynamic analysis properties. The B machine offers verified software development based on B language, which has already been applied in some safety-critical systems. The proposed model translation technique will help to bridge the gap between these two formal methods. This paper shows the systematic process of the translation, which is also illustrated by several case studies. The limitations and future efforts are discussed at the end of the paper.

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DOI: 10.3217/jucs-021-09-1143

Authors: Gerald De Grancy

Abstract: Distribution planning in urban areas faces a lack of available parking space at customer sites. One approach to mitigate the issue is to cluster nearby customers around known parking locations. Deliveries from each parking location to its assigned customers occur by a second mode of transport (for example by foot). These lead to long service times at each of the clusters. However, long service times in conjunction with time windows can lead to inefficient routes as nearby customer clusters with overlapping service times may not be connected. As a consequence, assigning additional service workers to each vehicle is a strategy to reduce service times. The additional workers can do the last mile deliveries in parallel to reduce the service time of a cluster and hence permit more efficient routing. The trade-off between paying additional workers to reduce costs for vehicles and driving creates a new decision problem called the vehicle routing problem with time windows and multiple service workers (VRPTWMS).

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DOI: 10.3217/jucs-021-07-0912

Authors: Yasmín Hernández, Gustavo Arroyo-Figueroa, L. Sucar

Abstract: A model of affect and learning for intelligent tutoring systems is proposed. The model considers both how a student feels and what a student knows, and then customizes how instruction is presented and how learning and performance are reinforced. The model was designed based on teachers' expertise, which was obtained through interviews and interaction with an educational game on number factorization learning. The core of the model is a dynamic decision network, which generates tutorial actions balancing affect and knowledge. The student's affect representation relies on a Bayesian network and theoretical models of emotion and personality. A controlled user study to evaluate the impact of the model on learning was performed. Current results are encouraging since they show significant improvement in learning when the model of affect and learning is incorporated.

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DOI: 10.3217/jucs-021-06-0856

Authors: Alejandro Zambrano, Carlos Toro, Marcos Nieto, Ricardo Sotaquira, Cesar Sanín, Edward Szczerbicki

Abstract: This paper proposes a service-oriented architecture for video analysis which separates object detection from event recognition. Our aim is to introduce new tools to be considered in the pathway towards Cognitive Vision as a support for classical Computer Vision techniques that have been broadly used by the scientific community. In the article, we particularly focus in solving some of the reported scalability issues found in current Computer Vision approaches by introducing an experience based approximation based on the Set of Experience Knowledge Structure (SOEKS). In our proposal, object detection takes place client-side, while event recognition takes place server-side. In order to implement our approach, we introduce a novel architecture that aims at recognizing events defined by a user using production rules (a part of the SOEKS model) and the detections made by the client using their own algorithms for visual recognition. In order to test our methodology, we present a case study, showing the scalability enhancements provided.

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DOI: 10.3217/jucs-021-04-0587

Authors: Bojan Rupnik, Domen Mongus, Borut Žalik

Abstract: Light Detection And Ranging (LiDAR) technology provides the means for fast and accurate acquisition of geospatial data. Quality control of the derived data is an important process for verifying whether the requirements of the scanning mission have been met. Point density presents one of the most important factors for evaluating LiDAR data. This paper presents a new method for evaluating the point density of LiDAR data through by applying methods of computational geometry. This method treats the LiDAR scan with regard to terrain characteristics and divides it into those areas that can be scanned and those that prevent quality scanning and produce weak returns. Point density evaluation is performed using the Voronoi diagram, which allows efficient extraction of actual LiDAR point density.

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DOI: 10.3217/jucs-020-14-1903

Authors: Youngdong Kim, Ikhwan Kim, Inhye Kang, Taehyoun Kim, Minyoung Sung

Abstract: This paper presents a model-based approach to the design and verification of motordrive software for networked motion control systems. We develop a formal model for an Ethernetbased motion system, where, using timed automata, we describe the concurrent and synchronizedbehaviors of the components, i.e., motion controller, motor drives, and communication links. The drive, in particular, is modeled in enough detail to accurately reflect the software implementa-tion used in a real drive. We use the design of multitasked drive software with fixed-priority preemptive scheduling. With UPPAAL model checking, we verify the precision and accuracyof the rendered motion in terms of the requirements on the actuation delay at each drive and the actuation deviation between different drives, respectively. The analysis results demonstratethe benefits of our model-based approach in the safety verification and design space exploration of motor drive software. We show that it is possible to verify deadlock freeness and real-timeschedulability in an early design phase. And, for varying number of drives and size of messages, we can successfully determine the combination of task periods that leads to the best precision andaccuracy.

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DOI: 10.3217/jucs-020-11-1564

Authors: Mauricio Solar, Fernando Daniels, Roberto López, Luis Meijueiro

Abstract: This paper presents a model to diagnose maturity and capabilities of Public Agencies (PAs) in pursuing the open data principles and practices. The open data maturity model, called OD-MM, was piloted in ten PAs from three Latin American countries, validating in this way the web tool that operationalizes the model. This web tool is a valuable diagnostic tool for PA's, since it shows all weaknesses and provides the instrument (a roadmap) to progress in the implementation of open data. We also propose a guide to implement open data in PAs. This guide is the result of the OD-MM application in Latin American PAs. The guide is simple and orients decision makers so that PAs following the actions of the guide can see their improved capacities when facing a diagnosis of their institutional maturity in the implementation of open data.

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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-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.

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DOI: 10.3217/jucs-019-09-1295

Authors: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Reyes-Ortiz

Abstract: In this paper we propose a novel energy efficient approach for the recog-nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabledand the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre-serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experimentsshow comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of theproposed method.

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DOI: 10.3217/jucs-019-09-1199

Authors: Jaime Guixeres, Javier Saiz, Mariano Alcañiz, Ausias Cebolla, Patricia Escobar, Rosa Baños, Cristina Botella, Juan Lison, Julio Alvarez, Laura Cantero, Empar Lurbe

Abstract: Virtual Reality (VR) could be an interesting tool to combat obesity and sedentariness in children. During the last years a multidisciplinary research team comprised of engineers, psychologists, physiotherapists and paediatricians have been testing these technologies. Throughout the tests, physiological (cardiovascular and metabolic response with biomedical sensors (smart fabrics TIAS) and psychological responses have been collected. The results presented in this paper reflect two main aspects: 1) the feasibility of the monitoring techniques employed and 2) the validity of virtual reality and exergaming technologies as promoters of physical activity and their potential as tools in clinical intervention programs. In the first study (n=90) children, a commercial platform was tested as support tool to aerobic exercise in a treadmill. Results showed a more physiological effort by obese group and limitations to measure effort perception with Borg scale especially in obese group. In second study (n=126) a new VR platform was developed (VREP) and tested as support of aerobic activities, a difference of first study, all the boys completed both conditions (same Aerobic exercise with/without support VR). 59.5% felt that they had to exert more effort in the traditional condition. Regarding to acceptability in both studies the vast majority of the participants liked the idea of combining physical activity with the VR platform as a form of treatment to increase physical activity. The capacity of VR technology to create controllable, multisensory, interactive 3D stimulus environments within which children's performance can be motivated, recorded, and measured, has been tested in these studies, offering clinical assessment and intervention options which are not possible using traditional methods.

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DOI: 10.3217/jucs-019-01-0002

Authors: Philippe Bon, Simon Collart-Dutilleul

Abstract: In the context of safety requirement engineering, model transformation is a task of interest. Indeed, it allows us to keep all the requirements while switching from one point of view to another. The presented work assumes that a valid solution has been found and proposes an approach in order to build a valid implementation. As some fine dynamic properties are integrated into the specification, high-level Petri nets are used to specify and verify the solution. Then, considering an industrial railway context, the transformation of the Petri net model in order to provide an input to a B process is considered. This last consideration leads to a proposition of a systematic direct transformation of the Petri net model into abstract B machines. The approach is illustrated by a theoretical railway example. The limitations of this approach are discussed at the end of the paper and some prospects are detailed.

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DOI: 10.3217/jucs-018-17-2452

Authors: Ajantha Dahanayake, Bernhard Thalheim

Abstract: Although services are developed, used, applied and intensively discussed in nowadays IT practice, the concept of an IT service has not yet been introduced. Services are IT artifacts that can be used by many users in different context at different points of time in different locations and serve a certain purpose. They provide the data and functionality at the best point of time, in the agreed format and quality for the right user with the right location and context. We generalize some of the introduced notions such as the REA framework (resource-event-agent) and introduce a framework for conceptual modeling of IT service systems that is based on the classical rhetorical frame introduced by Hermagoras of Temnos (Quis, quid, quando, ubi, cur, quem ad modum, quibus adminiculis (W7: Who, what, when, where, why, in what way, by what means)). Services are primarily characterized by W4: wherefore (end), whereof (source), wherewith (supporting means), and worthiness ((surplus) value). Additionally, the purpose can be characterized by answering the why, whereto, when, and for which reason W4 questions. The secondary characterization W14H is given by characterizing user or stakeholder (by whom, to whom, whichever), the application domain (wherein, where, for what, wherefrom, whence, what), the solution they are providing (how, why, whereto, when, for which reason), and the additional context (whereat, whereabout, whither, when).

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DOI: 10.3217/jucs-018-01-0062

Authors: Jesus Boticario, Alejandro Rodriguez-Ascaso, Olga Santos, Emmanuelle Raffenne, Lydia Montandon, David Roldán, Félix Buendía

Abstract: The EU4ALL project (IST-FP6-034778) has developed a general framework to address the needs of accessible lifelong learning at Higher Education level consisting of several standards-based interoperable components integrated into an open web service architecture aimed at supporting adapted interaction to guarantee students' accessibility needs. Its flexibility has supported the project implementation at several sites with different settings and various learning management systems. Large-scale evaluations involving hundreds of users, considering diverse disability types, and key staff roles have allowed obtaining valuable lessons with respect to "how to adopt or enhance eLearning accessibility" at university. The project was evaluated at four higher education institutions, two of the largest in Europe and two medium-sized. In this paper, we focus on describing the implementation and main conclusions at the largest project evaluation site (UNED), which was involved in the project from the beginning, and thus, in the design process, and a medium-sized university that adopted the EU4ALL approach (UPV). This implies dealing with two well-known open source learning environments (i.e. dotLRN and Sakai), and considering a wide variety of stakeholders and requirements. Thus the results of this evaluation serve to illustrate the coverage of both the approach and developments.

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DOI: 10.3217/jucs-017-13-1854

Authors: Eugene Levner, Amir Elalouf, T. C. Cheng

Abstract: Camponogara and Shima (2010) developed an ε-approximation algorithm (FPTAS) for the mobile agent routing problem in which a benefit function determines how visits to different sites contribute to the agent's mission. The benefit is to be maximized under a time constraint. They reduced the problem to the constrained longest-path problem in a graph. In this note we present a modified FPTAS that improves on their result by a factor of , where and are an upper bound and a lower bound on the maximum benefit, respectively, n is the number of nodes, and h is the length of the longest path (in hops) in the graph.

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DOI: 10.3217/jucs-017-02-0296

Authors: Jianming Yong

Abstract: This paper systematically discusses the security and privacy concerns for e-learning systems. A five-layer architecture of e-learning system is proposed. The security and privacy concerns are addressed respectively for five layers. This paper further examines the relationship among the security and privacy policy, the available security and privacy technology, and the degree of e-learning privacy and security. The digital identity attributes are introduced to e-learning portable devices to enhance the security and privacy of e-learning systems. This will provide significant contributions to the knowledge of e-learning security and privacy research communities and will generate more research interests.

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DOI: 10.3217/jucs-016-19-2780

Authors: Conor Gaffney, Declan Dagger, Vincent Wade

Abstract: This paper examines the personalization of online training simulations which are a key modern approach in computer aided education. More specifically it focuses on the difficulties involved in authoring personalized training simulations. The composition of such systems is very difficult which has hampered their wide spread adoption [Joolingen, 03]. Presently adaptive training simulations can only be authored by programmers working closely with subject matter experts. One of the key ways for adaptive simulations to increase their popularity in online eLearning [Wade, 09] is to reduce the effort and technical skills required by authors in their development. We argue that personalized online simulations need to be composed by subject matter experts, inexpensively and quickly. This paper details the twin challenges in composing content for both educational simulations and personalization. It also describes ACTSim, a new and unique composition tool that supports the rapid development of personalized training simulations. In particular ACTSim focuses on situational simulations for inter personal dialogue, so called soft skills. This paper concludes with a series of evaluations of the composition tool and of courses developed using the composition tool.

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DOI: 10.3217/jucs-016-15-1992

Authors: Kris Mcglinn, Eleanor Neill, Alan Gibney, Declan Sullivan, Dave Lewis

Abstract: The promise of smart buildings (SBs) is a safer more productive environment for users and a more operationally efficient building for owners. The automation of building function is highly dependent on sensing devices and Smart Building Applications(SBAs), which are often only evaluated in situ post deployment, making re-development costly. In this paper we explore our experiences developing a Simulated Context (SimCon) Model which currently supports taking information from a Virtual Reality (VR) SB and converting it into three types of location context to conduct early rapid evaluation of location based SBAs. This model is expressed using the Sensor Modelling Language (sensorML). It also explores the integration of this model into the Industry Foundation Classes (IFC) for modelling and simulating SBs. It also details usability evaluations of the SimConfig and SimConViz Tool for improving evaluation during the design phase of smart building development life cycle.

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DOI: 10.3217/jucs-015-12-2429

Authors: Chun Ruan, Sang-Soo Yeo

Abstract: Due to rapid advances of computing power and communications, healthcare services are increasingly rely on the electronic processing and transmission of confidential patient data to reduce the costs and improve the quality. It is becoming more and more important that accessing the health information should be both secure and privacy preserving. Therefore access control becomes an important integral part of any secure healthcare computer software systems. Specification of access control requirements at early steps of the software life cycle can provide stakeholders rapid feedback and protect the system in a best possible way. On the other hand, intelligent systems are widely used in various computing areas ranging from medicine to manufacturing industries to financial markets. This paper studies how to model an intelligent e-Consent system about the security requirements regarding healthcare information protection. In this paper, we use UML to specify and visualize the access control policies in a health application domain. These policies are represented in logic based e-Consent rules, and the patient’s consents about their information access can be derived from these rules. We first identify various parts necessary to specify the e-Consent rules about patient record protection requirements, and then propose UML models to demonstrate these requirements.

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DOI: 10.3217/jucs-015-08-1735

Authors: Ulrica Pettersson

Abstract: Developing successful knowledge management (KM) processes is extremely difficult. In general, a large number of all KM projects end unsuccessfully. The aim of this paper is to summarize and study the attempts to take advantage of Lessons Learned in the Swedish Armed Forces (SwAF), focusing on international missions. Relevant reports, articles and literature have been studied. With the purpose of understanding the reasons for failure and the failure factors in SwAF’s attempts at KM, Chua and Lam’s model for unsuccessful KM implementation has been applied to four cases from the organization. The results show that SwAF are aware of the importance of knowledge and have attempted to implement KM on several occasions. In most cases, however, the KM projects do not achieve widespread use and eventually end unsuccessfully. Furthermore, many of the KM tools that have been developed are no longer in use. The Swedish Explosive Ordnance Disposal and Demining Centre (SWEDEC) and the Swedish Air Force are notable exceptions.

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DOI: 10.3217/jucs-015-05-1065

Authors: Jong Park

Abstract: Ubi-Com has emerged as an exciting new paradigm to provide intelligent computig and communications at anytime and anywhere. But, In order to take the advantages of such services, it is important that intelligent security framework be suitable for Ubi-Com. In this paper, we propose privacy and access control scheme by surveillance which is one of core security technologies for ubiquitous hybrid intelligent security framework. In this scheme, the device information and the signature information can be added to the image data obtained by the image capturing device to maintain security of the image data and use the image data as digital proof when a specific event is generated.

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DOI: 10.3217/jucs-014-22-3627

Authors: Adriana Berlanga, Francisco García-Peñalvo

Abstract: After more than ten years of research aiming at defining methods and techniques to deliver personalized instruction, Adaptive Educational Hypermedia Systems have not made the jump into real practice systems. Reasons for this include the complexity of their development, their use of exclusive methods for defining adaptivity and educational elements, and their lack of interoperation amongst courses and applications. A possible alternative to cope with these issues is using as a common notational method the IMS Learning Design specification. This paper attempts to bring AEHS and IMS LD closer to each other in order to define adaptivity behaviour. To this end, it outlines how IMS LD could be used to define personalization properties and adaptive techniques and, based on that, it proposes a component called Adaptive Learning Designs, and an authoring tool to create these components. Furthermore, the paper discusses the benefits and limitations of IMS LD to define adaptivity behaviour, and ends suggesting additional research lines.

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DOI: 10.3217/jucs-014-17-2859

Authors: Jesus Boticario, Olga Santos

Abstract: One of the key problems in developing standard based adaptive courses is the complexity involved in the design phase, especially when establishing the hooks for the dynamic modelling to be performed at runtime. This is particularly critical when the courses are based on adaptation-oriented learning scenarios, where the full eLearning cycle (design, publication, use and auditing) is considered. Based on the problems we experienced in developing such scenarios with a reusable, platform independent, objective-based approach in the aLFanet project we have established an alternative framework in the ADAPTAPlan project, which focuses on dynamically generating learning design templates with the support of user modelling, planning and machine learning techniques. In particular, in this paper we describe the problems we are tackling and how we are relaxing the design work by automatically building the IMS learning design of the course from a simplified set of data required from the course authors.

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DOI: 10.3217/jucs-014-17-2777

Authors: César Vialardi, Javier Bravo, Alvaro Ortigosa

Abstract: Authoring in adaptive educational hypermedia environment is complex activity. In order to promote a wider application of this technology, the teachers and course designers need specific methods and tools for supporting their work. In that sense, data mining is a promising technology. In fact, data mining techniques have already been used in E-learning systems, but most of the times their application is oriented to provide better support to students; little work has been done for assisting adaptive hypermedia authors through data mining. In this paper we present a proposal for using data mining for improving an adaptive hypermedia system. A tool implementing the proposed approach is also presented, along with examples of how data mining technology can assist teachers.

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DOI: 10.3217/jucs-014-16-2737

Authors: Ana-Belén Gil, Francisco García-Peñalvo

Abstract: This paper analyses aspects about the recommendation process in distributed information systems. It extracts similarities and differences between recommendations in e-stores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a Multi-Agent System architecture.

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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-014-01-0088

Authors: Andrés Neyem, Sergio Ochoa, José Pino

Abstract: Advances in wireless communication and mobile computing extend collaboration scenarios. Mobile workers using computing devices are currently able to collaborate in order to carry out productive, educational or social activities. Typically, collaborative applications intended to support mobile workers involve some type of centralized data or services, because they are designed to work on infrastructure supported wireless networks. This centralization constrains the collaboration capabilities in ad-hoc communication cases. This paper introduces the concept of Service-Oriented Mobile Unit (SOMU) in order to reduce such limitation. SOMU is an autonomous software infrastructure running on a computing device; it is able to be integrated to ad-hoc networks and it can interoperate with other mobile units in ad-hoc collaboration scenarios. In addition, the paper presents the challenges faced when designing and implementing the SOMU platform. It also describes an application developed on SOMU.

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DOI: 10.3217/jucs-014-01-0059

Authors: Stephan Lukosch

Abstract: Nowadays, more and more users make use of web-based collaborative systems. Users participate in communities or search for and provide information in webbased systems. They access shared resources which they need for their professional life or for learning. One of the major prerequisites of such web-based systems is that users have to be connected to the network. But life has become much more mobile over the last years. While traveling, e.g. to the office or the university, users often are disconnected from the network. This makes it difficult to interact with other users or to access shared resources. An application supporting a seamless transition between connected and disconnected phases would allow users to work at any time and place while maintaining the advantages of a web-based collaborative system once they are online again. In this article, we describe the requirements that a web-based collaborative system has to fulfill to enable a nomadic use. We show how we extended the web-based collaborative system CURE to fulfill these requirements and how our approach can be transferred to other web-based collaborative systems.

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DOI: 10.3217/jucs-013-02-0224

Authors: Iwan Tabakow

Abstract: This paper describes a method of using Petri net P-invariants in system diagnosis. To model this process a net oriented fault classification is presented. Hence, the considered discrete event system is modelled by a live, bounded, and reversible place-transition Petri net. The notions of D-partition of the set of places P of a given place-transition net N and net k-distinguishability are first introduced. Next these two notions are extended to the set of all vertices, i.e. places and transitions of N. So the problem of fault identification of the vertices of N is transformed as a problem of fault identification of the places of a new net N´ called a net simulator of N. Any transition in N´ is assumed to be fault-free. Then the corresponding net place invariants are computed. The system k-distinguishability measure is obtained in a unique way from the place-invariant matrix. For a large value of k, the system model is extended by using some set of additional places called test points and at the same time preserving the original net properties. To obtain a 1-distinguishable net the notion of a marked graph component is used. It is shown a sufficient condition for 1-distinguishability of an arbitrary place-transition net and a corresponding algorithm is presented. Next two different diagnosis test strategies are discussed, i.e. combinational and sequential fault diagnosis. Corresponding (single) place and transition fault models are introduced. The complexity of the proposed method depends on the effectivity of the existing algorithms for computation of the P-cover, i.e. the set of P-invariants covering N. The proposed approach can be extended for higher level Petri nets, e.g such as coloured nets or also to design self-diagnosable circuit realisations of Boolean interpreted Petri nets. Several examples are given.

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DOI: 10.3217/jucs-012-09-1345

Authors: Peter Nagy, Istvan Pinter, Mihely Bagany

Abstract:

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DOI: 10.3217/jucs-012-09-1254

Authors: László Horváth, Imre Rudas

Abstract: Higher education courses are increasingly created as student organized collections of interrelated modules. At the same time, frequent change of subject matter and knowledge in its background must be handled. Above and other factors created and recognized a need for efficient computer based course management. Conventional computer aided teaching methods are not suitable to organize, manage, and communicate the comprehensive course information any more. The authors considered an analogy with well- organized computer descriptions of interrelated objects in the form of comprehensive integrated models in product engineering. Modeling and management of information serve engineering activities during lifecycle of product. Relevant advanced characteristics of integrated product descriptions are process orientation, definition of application oriented building elements called as features, and assistance of decisions by knowledge representations. The authors considered higher education course as one kind of product and proposed a course model. They focused to integrating student, teacher, and institute demand driven characteristics of modeling. Model is developed for application by course procedures. While conventional virtual education systems concentrate to computer mediated distance education, the authors considered arbitrary mix of campus and distance styles of education. In this paper, the authors first give an introduction in their approach to classroom modeling by a comparison of conventional distance education, conventional virtual classroom, and the proposed model based virtual classroom. Next, functional elements of the proposed course modeling and components of virtual classroom are explained. Conflicts as consequences of inappropriate capability or breaking of human intent are analyzed. Following this, initial conditions for definition of course module and construction of course module using modification by features are detailed. Finally, future work for implementation of the modeling in an experimental system composed by professional product lifecycle management (PLM) system, configurable virtual teaching environment, its virtual classroom extension and virtual classroom extension to the engineering modeling system is concluded.

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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-012-01-0099

Authors: Zhenjiang Miao, Baozong Yuan, Mengsun Yu

Abstract: This paper proposes a Human-centered Pervasive Computing System Model (HPC), a Layered Architectural Analysis and Design Method (LAAD) and a Waterfall Prototyping Process Model (WPP). Based on the HPC model and the LAAD method, a pervasive computing based multimodal tele-home healthcare system is designed and partly implemented using the Waterfall Prototyping Process. The design and implementation issues are discussed in more detail. Some testing results are presented.

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DOI: 10.3217/jucs-006-12-1197

Authors: Eva Heinrich, Hermann Maurer

Abstract: In this paper we present the notion of active documents. The basic idea is that in the future, users of documents in any networked system should not just be able to communicate with other users, but also with documents. To put it differently, we believe that communication in networks should be understood in a more general sense than it usually is. Although our notion will, at first glance, almost look like science fiction, we will show that good approximations can indeed be implemented. We conclude this short paper by pointing out a number of important applications of our new concept and mention cases where it has been applied already, successfully.

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DOI: 10.3217/jucs-006-07-0704

Authors: Graeme Smith, Colin Fidge

Abstract: System requirements frequently change while the system is still under development. Usually this means going back and revising the requirements specification and redoing those development steps already completed. In this article we show how formal requirements can be allowed to evolve while system development is in progress, without the need for costly redevelopment. This is done via a formalism which allows requirements engineering steps to be interleaved with formal development steps in a manageable way. The approach is demonstrated by a significant case study, the Light Control System.

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DOI: 10.3217/jucs-005-12-0833

Authors: Wonsik Lee, Sunghan Lee, Beom Lee, Young Lee

Abstract: In this paper, a genetic algorithm based method is proposed to solve the problem of minimizing the PCB assembly time for multi-head surface mounting machines. By grouping the reels and by clustering the components the multi-head problem is transformed into a single_head one and then the single-head method is simply applied to the multi-head case. To implement the genetic algorithm, a partial link concept is proposed for genetic operations. Computer simulation results show that the proposed algorithm is superior to the heuristic algorithm that is currently used in industry.

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DOI: 10.3217/jucs-005-09-0574

Authors: Kahn Mason, Padmanabhan Krishnan

Abstract: In this paper we extend the notion of homomorphisms and decomposition to timed automata. This is based on the classical Hartmanis-Stearns decomposition results for finite state automata. As in the classical theory, the existence of non-trivial orthogonal partitions is both necessary and sufficient for non-trivial decompositions. Of course, now these partitions have to include both the set of states and the set of timers (or clocks) in the system. We present an example which illustrate the various issues.

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DOI: 10.3217/jucs-003-10-1126

Authors: Hermann Maurer

Abstract: In this paper we discuss two novel ideas to improve teaching and information transfer between persons in general. The first aspect is centered around proposing to use the "Tamagotchi craze" for teaching purposes, the second deals with new ways of unobtrusively collecting data on the subjective satisfaction of persons with information and teaching material offered on the Web.

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