Latest Articles from JUCS - Journal of Universal Computer Science Latest 88 Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Fri, 29 Mar 2024 11:08:15 +0200 Pensoft FeedCreator https://lib.jucs.org/i/logo.jpg Latest Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Retail Indicators Forecasting and Planning https://lib.jucs.org/article/112556/ JUCS - Journal of Universal Computer Science 29(11): 1385-1403

DOI: 10.3897/jucs.112556

Authors: Nelson Baloian, Jonathan Frez, José A. Pino, Cristóbal Fuenzalida, Sergio Peñafiel, Belisario Panay, Gustavo Zurita, Horacio Sanson

Abstract: We present a methodology to handle the problem of planning sales goals. The methodology supports the retail manager to carry out simulations to find the most plausible goals for the future. One of the novel aspects of this methodology is that the analysis is based not on current sales levels, as most previous works do, but on those in the future, making a more precise and accurate analysis of the situation. The work presents the solution for a scenario using three sales performance indicators: foot traffic, conversion rate and ticket mean value for sales, but it explains how it can be generalized to more indicators. The contribution of this work is in the first place a framework, which consists of a methodology for performing sales planning, then, an algorithm, which finds the best prediction model for a particular store, and finally, a tool, which helps sales planners to set realistic sales goals based on the predicted sales. First we present the method to choose the best indicator prediction model for each retail store and then we present a tool which allows the retail manager estimate the improvements on the indicators in order to attain a desired sales goal level; the managers may then perform several simulations for various scenarios in a fast and efficient way. The developed tool implementing this methodology was validated by experts in the subject of administration of retail stores yielding good results.

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Research Article Tue, 28 Nov 2023 18:00:08 +0200
Efficiently Finding Cyclical Patterns on Twitter Considering the Inherent Spatio-temporal Attributes of Data https://lib.jucs.org/article/112523/ JUCS - Journal of Universal Computer Science 29(11): 1404-1421

DOI: 10.3897/jucs.112523

Authors: Claudio Gutiérrez-Soto, Patricio Galdames, Daniel Navea

Abstract: Social networks such as Twitter provide thousands of terabytes per day, which can be exploited to find relevant information. This relevant information is used to promote marketing strategies, analyze current political issues, and track market trends, to name a few examples. One instance of relevant information is finding cyclic behavior patterns (i.e., patterns that frequently repeat themselves over time) in the population. Because trending topics on Twitter change rapidly, efficient algorithms are required, especially when considering location and time (i.e., the specific location and time) during broadcasts. This article presents an efficient algorithm based on association rules to find cyclical patterns on Twitter, considering the inherent spatio-temporal attributes of data. Using a Hash Table enhances the efficiency of this algorithm, called HashCycle. Notably, HashCycle does not use minimum support and can detect patterns in a single run over a sequence. The processing times of HashCycle were compared to the Apriori (which is a well-known and widely used on diverse platforms) and Projection-based Partial Periodic Patterns (PPA) algorithms (which is one of the most efficient algorithms in terms of processing times). Empirical results from two spatio-temporal databases (a synthetic data set and one based on Twitter) show that HashCycle has more efficient processing times than two state-of-the-art algorithms: Apriori and PPA.

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Research Article Tue, 28 Nov 2023 16:00:09 +0200
Customized Curriculum and Learning Approach Recommendation Techniques in Application of Virtual Reality in Medical Education https://lib.jucs.org/article/94161/ JUCS - Journal of Universal Computer Science 28(9): 949-966

DOI: 10.3897/jucs.94161

Authors: Abhishek Kumar, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, Badr Alsamani, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Ankit Kumar

Abstract: Virtual Reality (VR) has made considerable gains in the consumer and professional markets. As VR has progressed as a technology, its overall usefulness for educational purposes has grown. On the other hand, the educational field struggles to keep up with the latest innovations, changing affordances, and pedagogical applications due to the rapid evolution of technology. Therefore, many have elaborated on the potential of virtual reality (VR) in learning. This research proposes a novel techniques customized curriculum for medical students and recommendations for their learning process based on deep learning techniques. Here the data has been collected based on the pre-historic performance of the student and their current requirement and these data have been created as a dataset. Then this has been processed for analysis based on CAD system integrated with deep learning techniques for creating a customized curriculum. Initially this data has been processed and analysed to remove missing and invalid data. Then these data were classified for creation of the curriculum using a gradient decision tree integrated with naïve Bayes. From this, the customized curriculum has been generated. Based on this customized curriculum, the learning approach recommendation has been carried out using the fuzzy rules integrated knowledge-based recommendation system. The experimental results of the proposed technique have been carried out with an accuracy of 98%, specificity of 82%, F-1 score of 79%, information overload of 75%, and precision of 81%.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture https://lib.jucs.org/article/94133/ JUCS - Journal of Universal Computer Science 28(9): 930-948

DOI: 10.3897/jucs.94133

Authors: Ashok Kumar Saini, Roheet Bhatnagar, Devesh Kumar Srivastava

Abstract: Machine learning is a branch of computer science concerned with developing algorithms & models capable of ‘learning through data and iterations’. Deep learning simulates the structure and function of human organs and diseases using artificial neural networks with more than one hidden layer. The primary purpose of this work is to develop and test computer vision and machine learning algorithms for classifying Huanglongbing (HLB)-infected, healthy, and unhealthy leaves and fruits of the citrus plant. The images were segmented using a normalized graph cut, and texture information was extracted using a co-occurrence matrix. The collected attributes were used for classification and support vector machine (SVM), and deep learning methods were employed. When rating the classification outcomes, the accuracy of the classification and the number of false positives and false negatives were considered. The result shows that Deep Learning could create categories up to 96.8% of HLB-infected leaves and fruits. Despite a broad variance in intensity from leaves collected in North India, this method suggests it could be beneficial in diagnosing HLB.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
Extracting concepts from triadic contexts using Binary Decision Diagram https://lib.jucs.org/article/67953/ JUCS - Journal of Universal Computer Science 28(6): 591-619

DOI: 10.3897/jucs.67953

Authors: Julio Cesar Vale Neves, Luiz Enrique Zarate, Mark Alan Junho Song

Abstract: Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

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Research Article Tue, 28 Jun 2022 10:00:00 +0300
Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework https://lib.jucs.org/article/24011/ JUCS - Journal of Universal Computer Science 26(4): 496-507

DOI: 10.3897/jucs.2020.026

Authors: Kheir Daouadi, Rim Rebaï, Ikram Amous

Abstract: Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independent features extracted from one single tweet with temporal enrichment of a previously labeled datasets. We conducted experiments on three benchmark datasets with standard evaluation scenarios, and the achieved results demonstrate the efficiency of Twitterbot+ against the state-of-the-art. This yielded a promising accuracy results (>95%). Our proposition is suitable for accurate and real-time use in a Twitter data collection step as an initial filtering technique to improve the quality of research data.

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Research Article Tue, 28 Apr 2020 00:00:00 +0300
Cyberattack Response Model for the Nuclear Regulator in Slovenia https://lib.jucs.org/article/22671/ JUCS - Journal of Universal Computer Science 25(11): 1437-1457

DOI: 10.3217/jucs-025-11-1437

Authors: Samo Tomažič, Igor Bernik

Abstract: Cyberattacks targeting the nuclear sector are now a reality; they are becoming increasingly frequent and sophisticated, while the perpetrators are increasingly motivated. The key stakeholders in the nuclear sector, such as nuclear facility operators, nuclear regulators responsible for nuclear safety or nuclear security, technical support organisations and computer equipment suppliers, must take the necessary cybersecurity measures to prepare for potential cyberattacks and provide the highest possible level of response to such cyberattacks. This can only be achieved by adopting a systematic approach to cyberattack response. When conducting the research study presented herein, a descriptive method was applied to review the scientific literature, various standards, recommendations and guides, as well as to devise an inventory of publicly available sources. On the basis of such an analysis, individual questions were then formulated in order to compile a structured interview, which was conducted with international experts working at nuclear facilities, nuclear regulators, technical support organisations, computer equipment suppliers and other organisations responsible for providing cybersecurity in the nuclear sector. On the basis of their responses, researchers devised an innovative and comprehensive Cyberattack Response Model to be used by Slovenia's nuclear safety regulator and the regulator responsible for the physical protection of nuclear facilities and nuclear and radioactive materials.

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Research Article Thu, 28 Nov 2019 00:00:00 +0200
A Web3.0-based Intelligent Learning System Supporting Education in the 21st Century https://lib.jucs.org/article/22666/ JUCS - Journal of Universal Computer Science 25(10): 1373-1393

DOI: 10.3217/jucs-025-10-1373

Authors: Khaled Halimi, Hassina Seridi-Bouchelaghem

Abstract: The aim of the paper is to describe the design of a Web 3.0-based Intelligent Learning System (ILS) that addressing the students' needs in the 21st century. The design is based theoretically, on the principles of the connectivism theory and technically, it implements the semantic web representations combining with the use of learning analytics techniques. The work emphasises that implementing a learning analytics approach that uses: text classification, sentiment analysis, topics extraction, and text clustering on the basis of a semantic web and ontologies can support the connectivist learning. The semantic learning analytics process, represents the key element of the proposed intelligent learning analytics system to infer and deduce hidden data in the massive learning data thanks to semantic models of i-SoLearn. The aim is to guide students to understand through recommendations, charts and visualisations their learning behaviour and to give teachers feedbacks, enabling them to examine both students' learning and activities. An experimental study using i-SoLearn (an intelligent social learning environment), indicates that designing an ILS based on Web 3.0 techniques is effective and expected to show a great advantage in enhancing the connectivist learning of students in the digital age.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
High-Performance Simulation of Drug Release Model Using Finite Element Method with CPU/GPU Platform https://lib.jucs.org/article/22658/ JUCS - Journal of Universal Computer Science 25(10): 1261-1278

DOI: 10.3217/jucs-025-10-1261

Authors: Akhtar Ali, Imran Bajwa, Rafaqat Kazmi

Abstract: his paper describes a hybrid CPU/GPU approach for solving a two-phase mathematical model numerically. The dynamic of drug release between the first phase (coating) and second phase (arterial tissue) is represented by a system of partial differential equations (PDEs). The system of equations is discretized by Finite Element Method. The whole discretized system involves a large sparse system of equation which requires a high computation. The CPU/GPU approach provides a platform to solve PDEs having extensive computations in parallel. Consequently, this platform can significantly reduce the solution times as compared to the implementation of CPU. This allows for more efficient investigation of different mathematical models, as well as, the governing parameters. In this paper, a significant parallel computing framework is presented to solve the governing equations numerically using the Graphics Processing Units (GPUs) with CUDA. This two-phase model investigates the impact of key parameters related to mass concentrations and drug release from tissue and coating layers. The identification and the role of major parameters such as (Filtration velocity, the ratio of accessible void volume to solid volume, the solid-liquid mass transfer rate) are tinted. Furthermore, the motivation and guidance for using parallel computing in order to handle computational complexities and large sparse system arise after discretizing the model equations are explained. We have designed a hybrid CPU/GPU solution of the proposed model by using Matlab. The parallel performance results show that CPU/GPU architecture is more efficient in large-scale problem simulations.

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Research Article Mon, 28 Oct 2019 00:00:00 +0200
Digital Investigation of IoT Devices in the Criminal Scene https://lib.jucs.org/article/22652/ JUCS - Journal of Universal Computer Science 25(9): 1199-1218

DOI: 10.3217/jucs-025-09-1199

Authors: François Bouchaud, Gilles Grimaud, Thomas Vantroys, Pierrick Buret

Abstract: The Internet of Things (IoT) is everywhere around us. Smart communicating objects are offering the digitalization of lives. They create new opportunities within criminal investigations. In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty in exploiting the IoT lies in the heterogeneous nature of the devices, the lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on the collection. The identification phase is quite unexplored. It addresses the challenges of locating hidden devices and finding the best evidence to be collected. The matter of facts is the traditional method of digital forensics does not fully fit the IoT environment. Furthermore, the investigator can no longer consider a connected object as a single device, but as an interconnected whole one, anchored in a cross-disciplinary environment. This paper presents the methodology for identifying and classifying connected objects in search of the best evidence to be collected. It offers techniques for detecting and locating the appropriate equipment. Based on frequency mapping and interactions, it transfers the concept of "fingerprinting" into the field of crime scene. It focuses on the technical and data criteria to successfully select the relevant IoT devices. It gives a general classiffication as well as the limits of such an approach. It shows the collection of digital evidence by focusing on pertinent information from the Internet of Things.

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Research Article Sat, 28 Sep 2019 00:00:00 +0300
Determination of System Weaknesses Based on the Analysis of Vulnerability Indexes and the Source Code of Exploits https://lib.jucs.org/article/22645/ JUCS - Journal of Universal Computer Science 25(9): 1043-1065

DOI: 10.3217/jucs-025-09-1043

Authors: Andrey Fedorchenko, Elena Doynikova, Igor Kotenko

Abstract: Currently the problem of monitoring the security of information systems is highly relevant. One of the important security monitoring tasks is to automate the process of determination of the system weaknesses for their further elimination. The paper considers the techniques for analysis of vulnerability indexes and exploit source code, as well as their subsequent classification. The suggested approach uses open security sources and incorporates two techniques, depending on the available security data. The first technique is based on the analysis of publicly available vulnerability indexes of the Common Vulnerability Scoring System for vulnerability classification by weaknesses. The second one complements the first one in case if there are exploits but there are no associated vulnerabilities and therefore the indexes for classification are absent. It is based on the analysis of the exploit source code for the features, i.e. indexes, using graph models. The extracted indexes are further used for weakness determination using the first technique. The paper provides the experiments demonstrating an effectiveness and potential of the developed techniques. The obtained results and the methods for their enhancement are discussed.

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Research Article Sat, 28 Sep 2019 00:00:00 +0300
Building an Educational Platform Using NLP: A Case Study in Teaching Finance https://lib.jucs.org/article/23607/ JUCS - Journal of Universal Computer Science 24(10): 1403-1423

DOI: 10.3217/jucs-024-10-1403

Authors: Soto Montalvo, Jesus Palomo, Carmen Orden

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

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Research Article Sun, 28 Oct 2018 00:00:00 +0300
Modelling of Automotive Engine Dynamics using Diagonal Recurrent Neural Network https://lib.jucs.org/article/23542/ JUCS - Journal of Universal Computer Science 24(9): 1330-1342

DOI: 10.3217/jucs-024-09-1330

Authors: Yujia Zhai, Kejun Qian, Fei Xue, Moncef Tayahi

Abstract: The spark-ignition (SI) engine dynamics is described as a severely nonlinear and fast process. A black-box model obtained by system identification approach is often valuable for the control and fault diagnosis application on such systems. Recurrent neural network (RNN) might be better suited for such dynamical system modelling due to its feedback back scheme if compared with feed-forward neural network. However, the computational load for RNN limits its practical application. In this paper, a diagonal recurrent neural network (DRNN) is investigated to model SI engine dynamics to achieve a balance between the modelling performance and computational burden. The data collection procedure and algorithms for training DRNN are presented too. Satisfactory results on modelling have been obtained with moderate cost on computation.

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Research Article Fri, 28 Sep 2018 00:00:00 +0300
Cloud Biometric Authentication: An Integrated Reliability and Security Method Using the Reinforcement Learning Algorithm and Queue Theory https://lib.jucs.org/article/23145/ JUCS - Journal of Universal Computer Science 24(4): 372-391

DOI: 10.3217/jucs-024-04-0372

Authors: A M N Balla Husamelddin, Guang Chen, Weipeng Jing

Abstract: While cloud systems deliver a larger amount of computing power, they do not guarantee full security and reliability. Focusing on improving successful job execution under resource constraints and security problems, this work proposes an enhanced, effective, integrated and novel approach to security and reliability. To apply a high level of security in the system, our novel approach uses cloud biometric authentication by splitting the biometric data into small chunks and spreading it over the cloud's resources. Reliability is enhanced through successful job execution by employing an adaptive reinforcement learning (RL) algorithm combined with a queuing theory. Our approach supports task schedulers to effectively adapt to dynamic changes in cloud environments. Based on the idea of reliability, we developed an adaptive action-selection, which controls the action selection dynamically by considering queue buffer size and the uncertainty value function. We evaluated the performance of our approach by several experiments conducted in terms of successful task execution and utilization rate and then compared our approach with other job scheduling policies. The experimental results demonstrated the efficiency of our method and achieved the objectives of the proposed system.

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Research Article Sat, 28 Apr 2018 00:00:00 +0300
Terrorism in the 2015 Election Period in Turkey: Content Analysis of Political Leaders' Social Media Activity https://lib.jucs.org/article/23059/ JUCS - Journal of Universal Computer Science 23(3): 256-279

DOI: 10.3217/jucs-023-03-0256

Authors: Ahmet Güneyli, Metin Ersoy, Şevki Kıralp

Abstract: In this research, a case study was conducted, analyzing the Twitter messages posted between July and November 2015 by six political leaders in Turkey. The Twitter messages posted by President Erdoğan, AKP's leader Davutoğlu, CHP's leader Kiliçdaroğlu, MHP's leader Bahçeli and HDP's co-chairs Demirtaş and Yüsekdağ were all examined. The analysis focused primarily on the messages that were related to terrorism. The research utilized a descriptive and qualitative approach as well as thematic content analysis. To consolidate the thematic content analysis, numerical data (total word amount and arithmetic mean), charts and tables were used. During the relevant period, terrorism and political instability dominated Turkish politics, and, on 1 November 2015, early elections were held. This research concluded that the six leaders differed in their approaches towards terrorism, which constituted a key issue for the political leaders in Turkey by dominating the political discourse during the electoral campaign.

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Research Article Tue, 28 Mar 2017 00:00:00 +0300
Generating Politician Profiles based on Content Analysis of Social Network Datasets https://lib.jucs.org/article/23058/ JUCS - Journal of Universal Computer Science 23(3): 236-255

DOI: 10.3217/jucs-023-03-0236

Authors: Klara Grčić, Marina Babac, Vedran Podobnik

Abstract: Social networks are nowadays an influential tool in the hands of the centres of political power because of their possibilities for direct and two-way communication with citizens in real time, dissemination of information, or a self-promotion and marketing. The use of social networks in the political context has become extremely important in the analysis and prediction of elections and generally in monitoring activities of politicians and public opinion. In this paper, we provide a content analysis of Facebook activities of leading European Union (EU) politicians to generate their extended individual profiles. Based on these profiles, a comparative analysis between the European Commissioners (i.e., EU ministers) and Croatian ministers is provided showing certain unexpected differences in their online behaviour. Summarizing these results, a model for prediction of online political behaviour is proposed.

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Research Article Tue, 28 Mar 2017 00:00:00 +0300
Exploring Teachers' Perceptions on Modeling Effort Demanded by CSCL Designs with Explicit Artifact Flow Support https://lib.jucs.org/article/23595/ JUCS - Journal of Universal Computer Science 22(10): 1398-1417

DOI: 10.3217/jucs-022-10-1398

Authors: Osmel Bordies, Yannis Dimitriadis

Abstract: Artifact flow represents an important aspect of teaching/learning processes, especially in CSCL situations in which complex relationships may be found. However, explicit modeling of CSCL processes with artifact flow may increase the cognitive load and associated effort of the teachers-designers and therefore decrease the efficiency of the design process. The empirical study, reported in this paper and grounded on mixed methods, provides evidence of the effort overload when teachers are involved in designing CSCL situations in a controlled environment. The results of the study illustrate the problem through the subjective perception of the participating teachers, complemented with objective parameters, such as time consumed, errors committed, uncertainty and objective complexity metrics.

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Research Article Sat, 1 Oct 2016 00:00:00 +0300
Web Service SWePT: A Hybrid Opinion Mining Approach https://lib.jucs.org/article/23208/ JUCS - Journal of Universal Computer Science 22(5): 671-690

DOI: 10.3217/jucs-022-05-0671

Authors: Yolanda Baca-Gomez, Alicia Martinez, Paolo Rosso, Hugo Estrada, Delia Irazu Hernandez Farias

Abstract: The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.

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Research Article Sun, 1 May 2016 00:00:00 +0300
Opinion Retrieval for Twitter Using Extrinsic Information https://lib.jucs.org/article/23205/ JUCS - Journal of Universal Computer Science 22(5): 608-629

DOI: 10.3217/jucs-022-05-0608

Authors: Yoon-Sung Kim, Young-In Song, Hae-Chang Rim

Abstract: Opinion retrieval in social networks is a very useful field for industry because it can provide a facility for monitoring opinions about a product, person or issue in real time. An opinion retrieval system generally retrieves topically relevant and subjective documents based on topical relevance and a degree of subjectivity. Previous studies on opinion retrieval only considered the intrinsic features of original tweet documents and thus suffer from the data sparseness problem. In this paper, we propose a method of utilizing the extrinsic information of the original tweet and solving the data sparseness problem. We have found useful extrinsic features of related tweets, which can properly measure the degree of subjectivity of the original tweet. When we performed an opinion retrieval experiment including proposed extrinsic features within a learning-to-rank framework, the proposed model significantly outperformed both the baseline system and the state-of-the-art opinion retrieval system in terms of Mean Average Precision (MAP) and Precision@K (P@K) metrics.

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Research Article Sun, 1 May 2016 00:00:00 +0300
A Novel Similar Temporal System Call Pattern Mining for Efficient Intrusion Detection https://lib.jucs.org/article/23120/ JUCS - Journal of Universal Computer Science 22(4): 475-493

DOI: 10.3217/jucs-022-04-0475

Authors: Vangipuram Radhakrishna, Puligadda Kumar, Vinjamuri Janaki

Abstract: Software security pattern mining is the recent research interest among researchers working in the areas of security and data mining. When an application runs, several process and system calls associated are invoked in background. In this paper, the major objective is to identify the intrusion using temporal pattern mining. The idea is to find normal temporal system call patterns and use these patterns to identify abnormal temporal system call patterns. For finding normal system call patterns, we use the concept of temporal association patterns. The reference sequence is used to obtain temporal association system call patterns satisfying specified dissimilarity threshold. To find similar (normal) temporal system call patterns, we apply our novel method which performs only a single database scan, reducing unnecessary extra overhead incurred when multiple scans are performed thus achieving space and time efficiency. The importance of the approach coins from the fact that this is first single database scan approach in the literature. To find if a given process is normal or abnormal, it is just sufficient to verify if there exists a temporal system call pattern which is not similar to the reference system call support sequence for specified threshold. This eliminates the need for finding decision rules by constructing decision table. The approach is efficient as it eliminates the need for finding decision rules (2n is usually very large for even small value of n) and thus aims at efficient dimensionality reduction as we consider only similar temporal system call sequence for deciding on intrusion.

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Research Article Fri, 1 Apr 2016 00:00:00 +0300
Mining Social Networks for Calculation of SmartSocial Influence https://lib.jucs.org/article/23054/ JUCS - Journal of Universal Computer Science 22(3): 394-415

DOI: 10.3217/jucs-022-03-0394

Authors: Vanja Smailovic, Vedran Podobnik

Abstract: In today's networked society where everybody and everything becomes inter-connected, it is very important to be able to identify key actors and key relationships in such a complex multi-layered eco-system. This paper focuses on the specific research challenge of identifying the most influential actors in a social network built through combining relationships among same actors in two different domains - communication domain (proxied through real-world mobile phone communication data) and social networking service domain (proxied through real-world Facebook data). A practical aspect of the paper is evaluated through the SmartSocial Platform, which uses methodology and implements algorithms that enable: i) joining multiple relations among actors across different social networks into the single unified social network; as well as ii) mining created unified social network for identification of most influential actors. Evaluation of the proposed approach is based on the social experiment with 465 users. Experiment results underline two important paper contributions: i) posting frequency sensitivity analysis shows a significant effect of posting frequency on social influence scores; and ii) interdependency analysis shows a synergic effect of combining data from communication and social networking service domains when it comes to calculating influence scores.

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Research Article Tue, 1 Mar 2016 00:00:00 +0200
Content-based Information Retrieval by Named Entity Recognition and Verb Semantic Role Labelling https://lib.jucs.org/article/23832/ JUCS - Journal of Universal Computer Science 21(13): 1830-1848

DOI: 10.3217/jucs-021-13-1830

Authors: Betina J, G. Mahalakshmi

Abstract: Tamil Siddha medicine, an ancient medicinal system has yielded us a wide range of untapped information about traditional medicines. In this paper, we explore into the various Natural Language Processing techniques that can be implemented to this syntactically rich corpus. As domain information mostly concentrates on the central concepts, we start our work by identifying the Named Entities and categorizing them. An integrated NER classifier is built which comprises of SVM and Decision Tree classifier with an accuracy as high as 95%. These entities play different roles in different context. Hence their roles are labelled along with the predicates surrounding them. These roles and predicates give rise to a rule based sentence tagging system, trained by an MEM model, to tag different contents in this otherwise unstructured text. These two important techniques are then exploited to develop our Information Retrieval System that combines the methods category tagging done by Named Entity Recognition and content tagging done by Semantic Role Labelling. The system takes full advantage of the rich features of the language and hence can be expanded to other domains.

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Research Article Mon, 28 Dec 2015 00:00:00 +0200
Cross-Language Source Code Re-Use Detection Using Latent Semantic Analysis https://lib.jucs.org/article/23824/ JUCS - Journal of Universal Computer Science 21(13): 1708-1725

DOI: 10.3217/jucs-021-13-1708

Authors: Enrique Flores, Alberto Barrón-Cedeño, Lidia Moreno, Paolo Rosso

Abstract: Nowadays, Internet is the main source to get information from blogs, encyclopedias, discussion forums, source code repositories, and more resources which are available just one click away. The temptation to re-use these materials is very high. Even source codes are easily available through a simple search on the Web. There is a need of detecting potential instances of source code re-use. Source code re-use detection has usually been approached comparing source codes in their compiled version. When dealing with cross-language source code re-use, traditional approaches can deal only with the programming languages supported by the compiler. We assume that a source code is a piece of text ,with its syntax and structure, so we aim at applying models for free text re-use detection to source code. In this paper we compare a Latent Semantic Analysis (LSA) approach with previously used text re-use detection models for measuring cross-language similarity in source code. The LSA-based approach shows slightly better results than the other models, being able to distinguish between re-used and related source codes with a high performance.

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Research Article Mon, 28 Dec 2015 00:00:00 +0200
PSO-Based Feature Selection for Arabic Text Summarization https://lib.jucs.org/article/23654/ JUCS - Journal of Universal Computer Science 21(11): 1454-1469

DOI: 10.3217/jucs-021-11-1454

Authors: Ahmed Al-Zahrani, Hassan Mathkour, Hassan Abdalla

Abstract: Feature-based approaches play an important role and are widely applied in extractive summarization. In this paper, we use particle swarm optimization (PSO) to evaluate the effectiveness of different state-of-the-art features used to summarize Arabic text. The PSO is trained on the Essex Arabic summaries corpus data to determine the best particle that represents the most appropriate simple/combination of eight informative/structure features used regularly by Arab summarizers. Based on the elected features and their relevant weights in each PSO iteration, the input text sentences are scored and ranked to extract the top ranking sentences in the form of an output summary. The output summary is then compared with a reference summary using the cosine similarity function as the fitness function. The experimental results illustrate that Arabs summarize texts simply, focusing on the first sentence of each paragraph.

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Research Article Sun, 1 Nov 2015 00:00:00 +0200
Leveraging Hybrid Recommenders with Multifaceted Implicit Feedback https://lib.jucs.org/article/22959/ JUCS - Journal of Universal Computer Science 21(2): 223-247

DOI: 10.3217/jucs-021-02-0223

Authors: Marcelo Manzato, Edson B. Santos Junior, Rudinei Goularte

Abstract: Research into recommender systems has focused on the importance of considering a variety of users' inputs for an efficient capture of their main interests. However, most collaborative filtering efforts are related to latent factors and implicit feeback, which do not consider the metadata associated with both items and users. This article proposes a hybrid recommender model which exploits implicit feedback from users by considering not only the latent space of factors that describes the user and item, but also the available metadata associated with content and individuals. Such descriptions are an important source for the construction of a user's profile that contains relevant and meaningful information about his/her preferences. The proposed model is generic enough to be used with many descriptions and types and characterizes users and items with distinguished features that are part of the whole recommendation process. The model was evaluated with the well-known MovieLens dataset and its composing modules were compared against other approaches reported in the literature. The results show its effectiveness in terms of prediction accuracy.

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Research Article Sun, 1 Feb 2015 00:00:00 +0200
A Utility-Oriented Routing Scheme for Interest-Driven Community-Based Opportunistic Networks https://lib.jucs.org/article/23821/ JUCS - Journal of Universal Computer Science 20(13): 1829-1854

DOI: 10.3217/jucs-020-13-1829

Authors: Xiuwen Fu, Wenfeng Li, Giancarlo Fortino, Pasquale Pace, Gianluca Aloi, Wilma Russo

Abstract: Opportunistic networks, as representative networks evolved from social networks and Ad-hoc networks, have been on cutting edges in recent years. Many research efforts have focused on realistic mobility models and cost-effective routing schemes. The concept of "community", as one of the most inherent attributes of opportunistic networks, has been proved to be very helpful in simulating mobility traces of human society and selecting suitable message forwarders. This paper proposes an interest-driven community-based mobility model by considering location preference and time variance in human behavior patterns. Based on this enhanced mobility model, a novel two-layer routing algorithm, named InterCom, is presented by jointly considering utilities generated by users' activity degree and social relationships. The results, obtained throughout an intensive simulation analysis, show that the proposed routing scheme is able to improve delivery ratio while keeping the routing overhead and transmission delay within a reasonable range with respect to well-known routing schemes for opportunistic networks.

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Research Article Fri, 28 Nov 2014 00:00:00 +0200
Developing Distributed Collaborative Applications with HTML5 under the Coupled Objects Paradigm https://lib.jucs.org/article/23816/ JUCS - Journal of Universal Computer Science 20(13): 1712-1737

DOI: 10.3217/jucs-020-13-1712

Authors: Nelson Baloian, Diego Aguirre, Gustavo Zurita

Abstract: One of the main tasks in developing distributed collaborative systems is to support synchronization processes. The Coupled Objects paradigm has emerged as a way to easily support these processes by dynamically coupling arbitrary user interface objects between heterogeneous applications. In this article we present an architecture for developing distributed collaborative applications using HTML5 and show its usage through the design and implementation of a series of collaborative systems in different scenarios. The experience of developing and using this architecture has shown that it is easy to use, robust and has good performance.

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Research Article Fri, 28 Nov 2014 00:00:00 +0200
Decision Support System to Diagnosis and Classification of Epilepsy in Children https://lib.jucs.org/article/23254/ JUCS - Journal of Universal Computer Science 20(6): 907-923

DOI: 10.3217/jucs-020-06-0907

Authors: Rui Rijo, Catarina Silva, Luis Pereira, Dulce Gonçalves, Margarida Agostinho

Abstract: Clinical decision support systems play an important role in organizations. They have a tight relation with the information systems. Our goal is to develop a system to support the diagnosis and the classification of epilepsy in children. Around 50 million people in the world have epilepsy. Epilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Exams such as electroencephalograms and magnetic resonances are often used to create a more accurate diagnosis in a short amount of time. After the diagnosis process, physicians classify epilepsy according to the International Classification of Diseases, ninth revision (ICD-9). Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. The classification process is time consuming and, in some cases, demands for complementary exams. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and ICD-9-based classification in children. We put forward a text mining approach using electronically processed medical records, and apply the K-Nearest Neighbor technique as a white-box multiclass classifier approach to classify each instance, mapping it to the corresponding ICD-9-based standard code. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data. To overcome this hurdle, in this work we also propose and explore ways of expanding the dataset.

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Research Article Sun, 1 Jun 2014 00:00:00 +0300
Development of Navigation Skills through Audio Haptic Videogaming in Learners who are Blind https://lib.jucs.org/article/23965/ JUCS - Journal of Universal Computer Science 19(18): 2677-2697

DOI: 10.3217/jucs-019-18-2677

Authors: Jaime Sánchez, Marcia Campos

Abstract: This study presents the development of a video game with audio and haptic interfaces that allows for the stimulation of orientation and mobility skills in people who are blind through the use of virtual environments. We evaluate the usability and the impact of the use of an audio and haptic-based videogame on the development of orientation and mobility skills in school-age learners who are blind. The results show that the interfaces used in the videogame are usable and appropriately designed, and that the haptic interface is as effective as the audio interface for orientation and mobility purposes.

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Research Article Sun, 1 Dec 2013 00:00:00 +0200
Text Analysis for Monitoring Personal Information Leakage on Twitter https://lib.jucs.org/article/23931/ JUCS - Journal of Universal Computer Science 19(16): 2472-2485

DOI: 10.3217/jucs-019-16-2472

Authors: Dongjin Choi, Jeongin Kim, Xeufeng Piao, Pankoo Kim

Abstract: Social networking services (SNSs) such as Twitter and Facebook can be considered as new forms of media. Information spreads much faster through social media than any other forms of traditional news media because people can upload information with no time and location constraints. For this reason, people have embraced SNSs and allowed them to become an integral part of their everyday lives. People express their emotional status to let others know how they feel about certain information or events. However, they are likely not only to share information with others but also to unintentionally expose personal information such as their place of residence, phone number, and date of birth. If such information is provided to users with inappropriate intentions, there may be serious consequences such as online and offline stalking. To prevent information leakages and detect spam, many researchers have monitored e-mail systems and web blogs. This paper considers text messages on Twitter, which is one of the most popular SNSs in the world, to reveal various hidden patterns by using several coefficient approaches. This paper focuses on users who exchange Tweets and examines the types of information that they reciprocate other's Tweets by monitoring samples of 50 million Tweets which were collected by Stanford University in November 2009. We chose an active Twitter user based on "happy birthday" rule and detecting their information related to place to live and personal names by using proposed coefficient method and compared with other coefficient approaches. As a result of this research, we can conclude that the proposed coefficient method is able to detect and recommend the standard English words for non-standard words in few conditions. Eventually, we detected 88,882 (24.287%) more name included Tweets and 14,054 (3.84%) location related Tweets compared by using only standard word matching method.

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Research Article Tue, 1 Oct 2013 00:00:00 +0300
An Item based Geo-Recommender System Inspired by Artificial Immune Algorithms https://lib.jucs.org/article/23814/ JUCS - Journal of Universal Computer Science 19(13): 2013-2033

DOI: 10.3217/jucs-019-13-2013

Authors: Antonio Cabanas-Abascal, Eduardo García-Machicado, Lisardo Prieto-González, Antonio Seco

Abstract: Nowadays, one of the most relevant features provided by in almost every web site is a recommender system. However, they are usually focused on the common characteristics of several items which are shared among the users without taking into account that there are other very important features, such as geo-position. To face this lack of such relevant factors, authors propose the usage of a useful system that will aid in tasks related to pattern detection and fast adaptability to changes: Artificial Immune System. A combination of both systems and the addition of a geographic component will provide a new solution to this problem, which will solve as well these issues as other ones like comparison tasks in big data.

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Research Article Mon, 1 Jul 2013 00:00:00 +0300
Semantic Integration of Heterogeneous Data Sources in the MOMIS Data Transformation System https://lib.jucs.org/article/23813/ JUCS - Journal of Universal Computer Science 19(13): 1986-2012

DOI: 10.3217/jucs-019-13-1986

Authors: Maurizio Vincini, Domenico Beneventano, Sonia Bergamaschi

Abstract: In the last twenty years, many data integration systems following a classical wrapper/mediator architecture and providing a Global Virtual Schema (a.k.a. Global Virtual View - GVV) have been proposed by the research community. The main issues faced by these approaches range from system-level heterogeneities, to structural syntax level heterogeneities at the semantic level. Despite the research effort, all the approaches proposed require a lot of user intervention for customizing and managing the data integration and reconciliation tasks. In some cases, the effort and the complexity of the task is huge, since it requires the development of specific programming codes. Unfortunately, due to the specificity to be addressed, application codes and solutions are not frequently reusable in other domains. For this reason, the Lowell Report 2005 has provided the guideline for the definition of a public benchmark for information integration problem. The proposal, called THALIA (Test Harness for the Assessment of Legacy information Integration Approaches), focuses on how the data integration systems manage syntactic and semantic heterogeneities, which definitely are the greatest technical challenges in the field. We developed a Data Transformation System (DTS) that supports data transformation functions and produces query translation in order to push down to the sources the execution. Our DTS is based on MOMIS, a mediator-based data integration system that our research group is developing and supporting since 1999. In this paper, we show how the DTS is able to solve all the twelve queries of the THALIA benchmark by using a simple combination of declarative translation functions already available in the standard SQL language. We think that this is a remarkable result, mainly for two reasons: firstly to the best of our knowledge there is no system that has provided a complete answer to the benchmark, secondly, our queries does not require any overhead of new code.

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Research Article Mon, 1 Jul 2013 00:00:00 +0300
Web Resource Sense Disambiguation in Web of Data https://lib.jucs.org/article/23805/ JUCS - Journal of Universal Computer Science 19(13): 1871-1891

DOI: 10.3217/jucs-019-13-1871

Authors: Farzam Matinfar, Mohammadali Nematbakhsh, Georg Lausen

Abstract: This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation in Web of Data and shows the role of designed system in interlinking datasets in Web of Data and word sense disambiguation scope. We specify the core labelling properties in semantic web to identify the name of entities which are described in web resources and use them to identify the candidate senses for a web resource. Moreover, we define the web resource's context to identify the most appropriate sense for each of the input web resources. Evaluation of the system shows the high coverage of the core labelling properties and the high performance of the sense disambiguation algorithm.

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Research Article Mon, 1 Jul 2013 00:00:00 +0300
A Review of Mobile Location-based Games for Learning across Physical and Virtual Spaces https://lib.jucs.org/article/23872/ JUCS - Journal of Universal Computer Science 18(15): 2120-2142

DOI: 10.3217/jucs-018-15-2120

Authors: Nikolaos Avouris, Nikoleta Yiannoutsou

Abstract: In this paper we review mobile location-based games for learning. These games are played in physical space, but at the same time, they are supported by actions and events in an interconnected virtual space. Learning in these games is related to issues like the narrative structure, space and game rules and content that define the virtual game space. First, we introduce the theoretical and empirical considerations of mobile location based games, and then we discuss an analytical framework of their main characteristics through typical examples. In particular, we focus on their narrative structure, the interaction modes that they afford, their use of physical space as prop for action, the way this is linked to virtual space and the possible learning impact the game activities have. Finally we conclude with an outline of future trends and possibilities that these kinds of playful activities can have on learning, especially outside school, like in environmental studies and visits in museums and other sites of cultural and historical value.

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Research Article Wed, 1 Aug 2012 00:00:00 +0300
The Modelling of a Digital Forensic Readiness Approach for Wireless Local Area Networks https://lib.jucs.org/article/23720/ JUCS - Journal of Universal Computer Science 18(12): 1721-1740

DOI: 10.3217/jucs-018-12-1721

Authors: Sipho Ngobeni, Hein Venter, Ivan Burke

Abstract: Over the past decade, wireless mobile communication technology based on the IEEE 802.11 Wireless Local Area Networks (WLANs) has been adopted worldwide on a massive scale. However, as the number of wireless users has soared, so has the possibility of cybercrime. WLAN digital forensics is seen as not only a response to cybercrime in wireless networks, but also a means to stem the increase of cybercrime in WLANs. The challenge in WLAN digital forensics is to intercept and preserve all the communications generated by the mobile stations and to conduct a proper digital forensic investigation. This paper attempts to address this issue by proposing a wireless digital forensic readiness model designed to monitor, log and preserve wireless network traffic for digital forensic investigations. Thus, the information needed by the digital forensic experts is rendered readily available, should it be necessary to conduct a digital forensic investigation. The availability of this digital information can maximise the chances of using it as digital evidence and it reduces the cost of conducting the entire digital forensic investigation process.

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Research Article Thu, 28 Jun 2012 00:00:00 +0300
Information Security Service Culture - Information Security for End-users https://lib.jucs.org/article/23715/ JUCS - Journal of Universal Computer Science 18(12): 1628-1642

DOI: 10.3217/jucs-018-12-1628

Authors: Rahul Rastogi, Rossouw Solms

Abstract: Information security culture has been found to have a profound influence on the compliance of end-users to information security policies and controls in their organization. Similarly, a complementary aspect of information security is the culture of information security managers and developers in the organization. This paper calls this is as the 'information security service culture' (ISSC). ISSC shapes and guides the behaviour of information security managers and developers as they formulate information security policies and controls. Thus, ISSC has profound influence on the nature of these policies and controls and thereby on the interaction of end-users with these artefacts. ISSC is useful in transforming information security managers and developers from their present-day technology-focused approach to an end-user centric approach.

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Research Article Thu, 28 Jun 2012 00:00:00 +0300
Security-enhanced Search Engine Design in Internet of Things https://lib.jucs.org/article/23459/ JUCS - Journal of Universal Computer Science 18(9): 1218-1235

DOI: 10.3217/jucs-018-09-1218

Authors: Xiaojun Qian, Xiaoping Che

Abstract: This paper elaborates the challenges in searching imposed by the burgeoning fieldof Internet of Things (IoT). Firstly it overviews the evolution of the new field to its predecessors: searching in the mobile computing, ubiquitous computing and information retrieve. Then,it identifies five research thrusts: architecture design, search locality, real-time, scalability and divulging information. It also sketches several presumptive IoT scenarios, and uses them to iden-tify key capabilities missing in today's systems. On top of these challenging issues, we report our undertaking work – a security-enhanced search engine for Internet of Things based on El-liptic Curve Cryptography (ECC) security protocol. We also report our preliminary experimental results.

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Research Article Tue, 1 May 2012 00:00:00 +0300
Goal-Driven Process Navigation for Individualized Learning Activities in Ubiquitous Networking and IoT Environments https://lib.jucs.org/article/23454/ JUCS - Journal of Universal Computer Science 18(9): 1132-1151

DOI: 10.3217/jucs-018-09-1132

Authors: Jian Chen, Qun Jin, Runhe Huang

Abstract: In the study, we propose an integrated adaptive framework to support and facilitate individualized learning through sharing the successful process of learning activities based on similar learning patterns in the ubiquitous learning environments empowered by Internet of Things (IoT). This framework is based on a dynamic Bayesian network that gradually adapts to a target student’s needs and information access behaviours. By analysing the log data of learning activities and extracting students' learning patterns, our analysis results show that most of students often use their preferred learning patterns in their learning activities, and the learning achievement is affected by the learning process. Based on these findings, we try to optimise the process of learning activities using the extracted learning patterns, infer the learning goal of target students, and provide a goal-driven navigation of individualized learning process according to the similarity of the extracted learning patterns.

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Research Article Tue, 1 May 2012 00:00:00 +0300
Automatic Tag Attachment Scheme based on Text Clustering for Efficient File Search in Unstructured Peer-to-Peer File Sharing Systems https://lib.jucs.org/article/23392/ JUCS - Journal of Universal Computer Science 18(8): 1032-1047

DOI: 10.3217/jucs-018-08-1032

Authors: Ting Qin, Satoshi Fujita

Abstract: In this paper, the authors address the issue of automatic tag attachment to the documents distributed over a P2P network aiming at improving the efficiency of file search in such networks. The proposed scheme combines text clustering with a modified tag extraction algorithm, and is executed in a fully distributed manner. Meanwhile, the optimal cluster number can also be fixed automatically through a distance cost function. We have conducted experiments to evaluate the accuracy of the proposed scheme. The result of experiments indicates that the proposed approach is capable of making effective and efficient tag attachment in real scenarios; i.e., for more than 90% of documents, it attaches the same tags as the ones attached by human reviewers. Moreover, it proofs by the experiments that the optimal cluster number is almost the same as the number of topics from the website.

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Research Article Sat, 28 Apr 2012 00:00:00 +0300
Wikipedia-Based Semantic Interpreter Using Approximate Top-k Processing and Its Application https://lib.jucs.org/article/23166/ JUCS - Journal of Universal Computer Science 18(5): 650-675

DOI: 10.3217/jucs-018-05-0650

Authors: Jong Kim, Ashwin Kashyap, Sandilya Bhamidipati

Abstract: Proper representation of the meaning of texts is crucial for enhancing many data mining and information retrieval tasks, including clustering, computing semantic relatedness between texts, and searching. Representing of texts in the concept-space derived from Wikipedia has received growing attention recently. This concept-based representation is capable of extracting semantic relatedness between texts that cannot be deduced with the bag of words model. A key obstacle, however, for using Wikipedia as a semantic interpreter is that the sheer size of the concepts derived from Wikipedia makes it hard to efficiently map texts into concept-space. In this paper, we develop an efficient and effective algorithm which is able to represent the meaning of a text by using the concepts that best match it. In particular, our approach first computes the approximate top-k Wikipedia concepts that are most relevant to the given text. We then leverage these concepts for representing the meaning of the given text. The experimental results show that the proposed technique provides significant gains in execution time without causing significant reduction in precision. We then explore the effectiveness of the proposed algorithm on a real world problem. In particular, we show that this novel scheme could be leveraged to boost the effectiveness in finding topic boundaries in a news video.

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Research Article Thu, 1 Mar 2012 00:00:00 +0200
Key Person Analysis in Social Communities within the Blogosphere https://lib.jucs.org/article/23086/ JUCS - Journal of Universal Computer Science 18(4): 577-597

DOI: 10.3217/jucs-018-04-0577

Authors: Anna Zygmunt, Piotr Bródka, Przemysław Kazienko, Jarosław Koźlak

Abstract: Identifying key persons active in social groups in the blogosphere is performed by means of social network analysis. Two main independent approaches are considered in the paper: (i) discovery of the most important individuals in persistent social communities and (ii) regular centrality measures applied either to social groups or the entire network. A new method for separating of groups stable over time, fulfilling given conditions of activity level of their members is proposed. Furthermore, a new concept for extracting user roles and key persons in such groups is also presented. This new approach was compared to the typical clustering method and the structural node position measure applied to rank users. The experimental studies have been carried out on real two-year blogosphere data.

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Research Article Tue, 28 Feb 2012 00:00:00 +0200
A Visited Item Frequency Based Recommender System: Experimental Evaluation and Scenario Description https://lib.jucs.org/article/30036/ JUCS - Journal of Universal Computer Science 17(14): 2009-2028

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.

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Research Article Sat, 1 Oct 2011 00:00:00 +0300
An Intelligent System for Automated Binary Knowledge Document Classification and Content Analysis https://lib.jucs.org/article/30035/ JUCS - Journal of Universal Computer Science 17(14): 1991-2008

DOI: 10.3217/jucs-017-14-1991

Authors: Tzu-An Chiang, Chun-Yi Wu, Charles Trappey, Amy J. C. Trappey

Abstract: Many companies rely on patent engineers to search patent documents and offer recommendations and advice to R and D engineers. Given the increasing number of patent documents filed each year, new means to effectively and efficiently identify and manage technology specific patent documents are required. This research applies a back-propagation artificial neural network (BPANN), a hierarchical ontology technique, and a normalized term frequency (NTF) method to develop an intelligent system for binary knowledge document classification and content analysis. The intelligent system minimizes inappropriate patent document classification and reduces the effort required to search and screen patents for analysis. Finally, this paper uses the design of light emitting diode (LED) lamps as a case study to illustrate and verify the efficiency of automated binary knowledge document classification and content analysis.

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Research Article Sat, 1 Oct 2011 00:00:00 +0300
A Context Aware Recommender System for Creativity Support Tools https://lib.jucs.org/article/30019/ JUCS - Journal of Universal Computer Science 17(12): 1743-1763

DOI: 10.3217/jucs-017-12-1743

Authors: George Sielis, Christos Mettouris, George Papadopoulos, Aimilia Tzanavari, Roger Dols, Quintin Siebers

Abstract: The development of methods that can enhance the creativity process is becoming a continuous necessity. Through the years several researchers modelled and defined creativity focusing to the psychological aspect of the topic. More recent researchers approach creativity as a computerized process by simulating it within creativity support tools (CST). This article supports that usage of context aware recommender system, in creativity support tools and more specifically, collaborative creativity support tools (CCST) can enhance creativity process. In this work we focus on the development of a context awareness recommender system and look into how such a system can be useful for the creativity process, through preliminary evaluation results in regards to its usefulness and usability.

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Research Article Mon, 1 Aug 2011 00:00:00 +0300
Document Retrieval Using SIFT Image Features https://lib.jucs.org/article/29871/ JUCS - Journal of Universal Computer Science 17(1): 3-15

DOI: 10.3217/jucs-017-01-0003

Authors: Dan Smith, Richard Harvey

Abstract: This paper describes a new approach to document classification based on visual features alone. Text-based retrieval systems perform poorly on noisy text. We have conducted series of experiments using cosine distance as our similarity measure, selecting varying numbers local interest points per page, and varying numbers of nearest neighbour points in the similarity calculations. We have found that a distance-based measure of similarity outperforms a rank-based measure except when there are few interest points. We show that using visual features substantially outperforms textbased approaches for noisy text, giving average precision in the range 0.4-0.43 in several experiments retrieving scientific papers.

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Research Article Sat, 1 Jan 2011 00:00:00 +0200
Extraction of Contextualized User Interest Profiles in Social Sharing Platforms https://lib.jucs.org/article/29761/ JUCS - Journal of Universal Computer Science 16(16): 2196-2213

DOI: 10.3217/jucs-016-16-2196

Authors: Rafael Schirru, Stephan Baumann, Martin Memmel, Andreas Dengel

Abstract: Along with the emergence of the Web 2.0, E-learning more often takes place in open environments such as wikis, blogs, and resource sharing platforms. Nowadays, many companies deploy social media technologies to foster the knowledge transfer in the enterprise. They offer Enterprise 2.0 platforms where knowledge workers can share contents according to their different topics of interest. In this article we present an approach extracting contextualized user profiles in an enterprise resource sharing platform according to the users' different topics of interest. The system analyses the social annotations of each user's preferred resources and identifies thematic groups. For every group a weighted term vector is derived that represents the respective topic of interest. Each user profile consists of several such vectors that way enabling recommendation lists with a high degree of inter-topic diversity as well as targeted context-sensitive recommendations. The proposed approach has been tested in our Enterprise 2.0 platform ALOE. A first evaluation has shown that the method is likely to identify reasonable user interest topics and that resource recommendations for these topics are widely appreciated by the users.

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Research Article Sat, 28 Aug 2010 00:00:00 +0300
Real-time Analysis of Time-based Usability and Accessibility for Human Mobile-Web Interactions in the Ubiquitous Internet https://lib.jucs.org/article/29745/ JUCS - Journal of Universal Computer Science 16(15): 1953-1972

DOI: 10.3217/jucs-016-15-1953

Authors: Yung Kim

Abstract: In the ubiquitous Internet, human mobile-web interactions can be evaluated with real-time analysis of time-based usability and accessibility with the different types of mobile Internet devices including smart phones (e.g. iPhone, Android phone, etc.). A ubiquitous mobile-web interaction server, accessible with a variety of mobile Internet devices, could be a unified estimation hub in real-time analysis of human-centric mobile-web interactions. We propose the real-time analysis scheme based on real-time estimation of time-based usability and accessibility for human mobile-web interactions with a name-based directory server for social networking in the ubiquitous Internet environment. We present an implementation of a ubiquitous mobile-web directory service and discuss our approach with some empirical results.

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Research Article Sun, 1 Aug 2010 00:00:00 +0300
A Service-Oriented Platform for Ubiquitous Personalized Multimedia Provisioning https://lib.jucs.org/article/29688/ JUCS - Journal of Universal Computer Science 16(10): 1291-1310

DOI: 10.3217/jucs-016-10-1291

Authors: Zhiwen Yu, Changde Li, Xingshe Zhou, Haipeng Wang

Abstract: As multimedia contents are becoming widely used in ubiquitous computing environments among many application fields, e.g., education, entertainment, and live surveillance, the demand of personalized access to these contents has increased dramatically. The provisioning of ubiquitous personalized multimedia services (UPMSs) is a challenging task, which involves a lot of heterogeneous entities ranging from objects, devices to software. In this work, we propose a three-layer software platform, called UPmP to support efficient development and deployment of UPMSs. It fulfills the core functionalities for ubiquitous personalized multimedia provisioning including service management, multimedia recommendation, adaptation, and delivery. We adopt service-oriented approach in building the platform. The enabling technologies such as component representation, service lifecycle management, platform configuration, and service composition are described in detail. The experimental results show that the UPmP is flexible to be configured under different settings.

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Research Article Fri, 28 May 2010 00:00:00 +0300
Classification of Software for the Simulation of Light Scattering and Realization within an Internet Information Portal https://lib.jucs.org/article/29677/ JUCS - Journal of Universal Computer Science 16(9): 1176-1189

DOI: 10.3217/jucs-016-09-1176

Authors: Jens Hellmers, Thomas Wriedt

Abstract: Light scattering studies are done by researchers of various scientific areas. As the calculation of the scattering behavior by small particles is rather complex, corresponding programs usually can be used for specific problems only and therefore a multitude of programs have been developed over the years. To enable researchers to find the best fitting one for their scattering problem a categorization scheme for such software is presented here. This scheme is used within an actual project to set up a new internet information portal on the topic of light scattering. The approach for the integration of the scheme as well as the implementation of a corresponding search tool is described in this article.

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Research Article Sat, 1 May 2010 00:00:00 +0300
Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization https://lib.jucs.org/article/29654/ JUCS - Journal of Universal Computer Science 16(6): 938-955

DOI: 10.3217/jucs-016-06-0938

Authors: Gamgarn Somprasertsri, Pattarachai Lalitrojwong

Abstract: Online customer reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The high-level problem of opinion summarization addresses how to determine the sentiment, attitude or opinion that an author expressed in natural language text with respect to a certain feature. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the performance of opinion orientation identification. It is important to properly identify the semantic relationships between product features and opinions. We proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information. By applying dependency relations and ontological knowledge with probabilistic based model, the result of our experiments shows that our approach is more flexible and effective.

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Research Article Sun, 28 Mar 2010 00:00:00 +0200
SOM Clustering to Promote Interoperability of Directory Metadata: A Grid-Enabled Genetic Algorithm Approach https://lib.jucs.org/article/29640/ JUCS - Journal of Universal Computer Science 16(5): 800-820

DOI: 10.3217/jucs-016-05-0800

Authors: Lei Li, Vijay Vaishnavi, Art Vandenberg

Abstract: Directories provide a general mechanism for describing resources and enabling information sharing within and across organizations. Directories must resolve differing structures and vocabularies in order to communicate effectively, and interoperability of the directories is becoming increasingly important. This study proposes an approach that integrates a genetic algorithm with a neural network based clustering algorithm - Self-Organizing Maps (SOM) - to systematically cluster directory metadata, highlight similar structures, recognize developing patterns of practice, and potentially promote homogeneity among the directories. The proposed approach utilizes the computing power of Grid infrastructure to improve system performance. The study also explores the feasibility of automating the SOM clustering process in a converging domain by incrementally building a stable SOM map with respect to an initial reference set. Empirical investigations were conducted on sets of Lightweight Directory Access Protocol (LDAP) directory metadata. The experimental results show that the proposed approach can effectively and efficiently cluster LDAP directory metadata at the level of domain experts and a stable SOM map can be created for a set of converging LDAP directory metadata.

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Research Article Mon, 1 Mar 2010 00:00:00 +0200
Block-based Against Segmentation-based Texture Image Retrieval https://lib.jucs.org/article/29606/ JUCS - Journal of Universal Computer Science 16(3): 402-423

DOI: 10.3217/jucs-016-03-0402

Authors: Mohammad Faizal Ahmad Fauzi, Paul Lewis

Abstract: This paper concerns the best approach to the capture of local texture features for use in content-based image retrieval (CBIR) applications. From our previous work, two approaches have been suggested, the multiscale block-based approach and the automatic texture segmentation approach. Performance comparison as well as advantages and disadvantages of the two methods are presented in this paper. The databases used are the Brodatz and VisTex databases, as well as three museum image collections of various sizes and contents, with each collection presenting different challenges to the CBIR systems. Experimental observations suggest that the two approaches both perform well, with the multiscale technique having the edge in retrieval performance and scale invariance, while the segmentation technique has the edge in lighter computational complexity as well as having the shape information for later purposes. The choice between the two approaches thus depends on application.

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Research Article Mon, 1 Feb 2010 00:00:00 +0200
An Approach to Generation of Decision Rules https://lib.jucs.org/article/29579/ JUCS - Journal of Universal Computer Science 16(1): 140-158

DOI: 10.3217/jucs-016-01-0140

Authors: Zhang Mingyi, Li Danning, Zhang Ying

Abstract: Classical classification and clustering based on equivalence relations are very important tools in decision-making. An equivalence relation is usually determined by properties of objects in a given domain. When making decision, anything that can be spoken about in the subject position of a natural sentence is an object, properties of which are fundamental elements of the knowledge of the given domain. This gives the possibility of representing the concept related to a given domain. In general, the information about a set of the objects is uncertain or incomplete. Various approaches representing uncertainty of a concept were proposed. In particular, Zadeh?s fuzzy set theory and Pawlak?s rough set theory have been most influential on this research field. Zadeh characterizes uncertainty of a concept by introducing a membership function and a similarity (fuzzy equivalence) relation of a set of objects. Pawlak then characterizes uncertainty of a concept by union of some equivalence classes of an equivalence relation. As one of particular important and widely used binary relations, equivalence relation plays a fundamental role in classification, clustering, pattern recognition, polling, automata, learning, control inference and natural language understanding, etc.  An equivalence relation is a binary relation with reflexivity, symmetry and transitivity. However, in many real situations, it is not sufficient to consider equivalence relations only. In fact, a lot of relations determined by the attributes of objects do not satisfy transitivity. In particular, information obtained from a domain of objects is not transitive, when we make decision based on properties of objects. Moreover, the information about symmetry of a relation is mostly uncertain. So, it is needed to approximately make decision and reasoning by indistinct concepts. This provokes us to explore a new class of relations, so-called class of fuzzy semi-equivalence relations. In this paper we introduce the notion of fuzzy semi-equivalence relations and study its properties. In particular, a constructive method of fuzzy semi-equivalence classes is presented. Applying it we present approaches to the fuzzyfication of indistinct concepts approximated by fuzzy relative and semi-equivalence classes, respectively. And an application of the fuzzy semi-equivalence relation theory to generate decision rules is outlined.

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Research Article Fri, 1 Jan 2010 00:00:00 +0200
Automatically Deciding if a Document was Scanned or Photographed https://lib.jucs.org/article/29564/ JUCS - Journal of Universal Computer Science 15(18): 3364-3375

DOI: 10.3217/jucs-015-18-3364

Authors: Gabriel Pereira e Silva, Rafael Lins, Brenno Miro, Steven Simske, Marcelo Thielo

Abstract: Portable digital cameras are being used widely by students and professionals in different fields as a practical way to digitize documents. Tools such as PhotoDoc enable the batch processing of such documents, performing automatic border removal and perspective correction. A PhotoDoc processed document and a scanned one look very similar to the human eye if both are in true color. However, if one tries to automatically binarize a batch of documents digitized from portable cameras compared to scanners, they have different features. The knowledge of their source is fundamental for successful processing. This paper presents a classification strategy to distinguish between scanned and photographed documents. Over 16,000 documents were tested with a correct classification rate of over 99.96%.

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Research Article Mon, 28 Dec 2009 00:00:00 +0200
Layout Analysis for Camera-Based Whiteboard Notes https://lib.jucs.org/article/29561/ JUCS - Journal of Universal Computer Science 15(18): 3307-3324

DOI: 10.3217/jucs-015-18-3307

Authors: Szilárd Vajda, Thomas Plötz, Gernot Fink

Abstract: A domain where, even in the era of electronic document processing, handwriting is still widely used is note-taking on a whiteboard. Such documents are either captured by a pen-tracking device or — which is much more challenging — by a camera. In both cases the layout analysis of realistic whiteboard notes is an open research problem. In this paper we propose a camera-based three-stage approach for the automatic layout analysis of whiteboard documents. Assuming a reasonable foreground-background separation of the handwriting it starts with a locally adaptive binarization followed by connected component extraction. The latter are then automatically classified as representing either simple graphical elements of a mindmap or elementary text patches. In the final stage the text patches are subject to a clustering procedure in order to generate hypotheses for those image regions where textual annotations of the mindmap can be found. In order to demonstrate the effectiveness of the proposed approach we report results of a writer independent experimental evaluation on a data set of mindmap images created by several different writers without any constraints on writing or drawing style.

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Research Article Mon, 28 Dec 2009 00:00:00 +0200
A Flexible Strategy-Based Model Comparison Approach: Bridging the Syntactic and Semantic Gap https://lib.jucs.org/article/29478/ JUCS - Journal of Universal Computer Science 15(11): 2225-2253

DOI: 10.3217/jucs-015-11-2225

Authors: Kleinner Oliveira, Karin Breitman, Toacy Oliveira

Abstract: In this paper we discuss the importance of model comparison as one of the pillars of model-driven development (MDD). We propose an innovative, flexible, model comparison approach, based on the composition of matching strategies. The proposed approach is fully implemented by a match operator that combines syntactical matching rule, synonym dictionary and typographic similarity strategies to a semantic, ontology-based strategy. Ontologies are semantically richer, have greater power of expression than UML models and can be formally verified for consistency, thus providing more reliability and accuracy to model comparison. The proposed approach is presented in the format of a workflow that provides clear guidance to users and facilitates the inclusion of new matching strategies and evolution.

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Research Article Mon, 1 Jun 2009 00:00:00 +0300
Semantic Information in Medical Information Systems: Utilization of Text Mining Techniques to Analyze Medical Diagnoses https://lib.jucs.org/article/29287/ JUCS - Journal of Universal Computer Science 14(22): 3781-3795

DOI: 10.3217/jucs-014-22-3781

Authors: Andreas Holzinger, Regina Geierhofer, Felix Mödritscher, Roland Tatzl

Abstract: Most information in Hospitals is still only available in text format and the amount of this data is immensely increasing. Consequently, text mining is an essential area of medical informatics. With the aid of statistic and linguistic procedures, text mining software attempts to dig out (mine) information from plain text. The aim is to transform data into information. However, for the efficient support of end users, facets of computer science alone are insufficient; the next step consists of making the information both usable and useful. Consequently, aspects of cognitive psychology must be taken into account in order to enable the transformation of information into knowledge of the end users. In this paper we describe the design and development of an application for analyzing expert comments on magnetic resonance images (MRI) diagnoses by applying a text mining method in order to scan them for regional correlations. Consequently, we propose a calculation of significant co-occurrences of diseases and defined regions of the human body, in order to identify possible risks for health.

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Research Article Sun, 28 Dec 2008 00:00:00 +0200
A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives https://lib.jucs.org/article/29199/ JUCS - Journal of Universal Computer Science 14(18): 2912-2935

DOI: 10.3217/jucs-014-18-2912

Authors: Josep Lladós, Dimosthenis Karatzas, Joan Mas, Gemma Sánchez

Abstract: Mass digitization of document collections with further processing and semantic annotation is an increasing activity among libraries and archives at large for preservation, browsing and navigation, and search purposes. In this paper we propose a software architecture for the process of converting high volumes of document collections to semantically annotated digital libraries. The proposed architecture recognizes two sources of knowledge in the conversion pipeline, namely document images and humans. The Image Analysis module and the Correction and Validation module cover the initial conversion stages. In the former information is automatically extracted from document images. The latter involves human intervention at a technical level to define workflows and to validate the image processing results. The second stage, represented by the Knowledge Capture modules requires information specific to the particular knowledge domain and generally calls for expert practitioners. These two principal conversion stages are coupled with a Knowledge Management module which provides the means to organise the extracted and acquired knowledge. In terms of data propagation, the architecture follows a bottom-up process, starting with document image units, called terms, and progressively building meaningful concepts and their relationships. In the second part of the paper we describe a real scenario with historical document archives implemented according to the proposed architecture.

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Research Article Wed, 1 Oct 2008 00:00:00 +0300
Testing Website Usability in Spanish-Speaking Academia through Heuristic Evaluation and Cognitive Walkthroughs https://lib.jucs.org/article/29068/ JUCS - Journal of Universal Computer Science 14(9): 1513-1528

DOI: 10.3217/jucs-014-09-1513

Authors: María González, Toni Granollers, Afra Pascual

Abstract: Although usability evaluations have been focused on assessing different contexts of use, no proper specifications have been addressed towards the particular environment of academic websites in the Spanish-speaking context of use. Considering that this context involves hundreds of millions of potential users, the AIPO Association is running the UsabAIPO Project. The ultimate goal is to promote an adequate translation of international standards, methods and ideal values related to usability in order to adapt them to diverse Spanish-related contexts of use. This article presents the main statistical results coming from the Second and Third Stages of the UsabAIPO Project, where the UsabAIPO Heuristic method (based on Heuristic Evaluation techniques) and seven Cognitive Walkthroughs were performed over 69 university websites. The planning and execution of the UsabAIPO Heuristic method and the Cognitive Walkthroughs, the definition of two usability metrics, as well as the outline of the UsabAIPO Heuristic Management System prototype are also sketched.

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Research Article Thu, 1 May 2008 00:00:00 +0300
Ranking Retrieval Systems with Partial Relevance Judgements https://lib.jucs.org/article/29022/ JUCS - Journal of Universal Computer Science 14(7): 1020-1030

DOI: 10.3217/jucs-014-07-1020

Authors: Shengli Wu, Fabio Crestani

Abstract: Some measures such as mean average precision and recall level precision are considered as good system-oriented measures, because they concern both precision and recall that are two important aspects for effectiveness evaluation of information retrieval systems. However, such good system-oriented measures suffer from some shortcomings when partial relevance judgments are used. In this paper, we discuss how to rank retrieval systems in the condition of partial relevance judgments, which is common in major retrieval evaluation events such as TREC conferences and NTCIR workshops. Four system-oriented measures, which are mean average precision, recall level precision, normalized discount cumulative gain, and normalized average precision over all documents, are discussed. Our investigation shows that averaging values over a set of queries may not be the most reliable approach to rank a group of retrieval systems. Some alternatives such as Borda count, Condorcet voting, and the Zero-one normalization method, are investigated. Experimental results are also presented for the evaluation of these methods.

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Research Article Tue, 1 Apr 2008 00:00:00 +0300
Compensation Models for Interactive Advertising https://lib.jucs.org/article/28970/ JUCS - Journal of Universal Computer Science 14(4): 557-565

DOI: 10.3217/jucs-014-04-0557

Authors: Astrid Dickinger, Steffen Zorn

Abstract: Due to a shift in the marketing focus from mass to micro markets, the importance of one-to-one communication in advertising has increased. Interactive media provide possible answers to this shift. However, missing standards in payment models for interactive media are a hurdle in the further development. The paper reviews interactive advertising payment models. Furthermore, it adapts the popular FCB grid as a tool for both advertisers and publishers or broadcasters to examine effective interactive payment models.

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Research Article Thu, 28 Feb 2008 00:00:00 +0200
Informatics for Historians: Tools for Medieval Document XML Markup, and their Impact on the History-Sciences https://lib.jucs.org/article/28938/ JUCS - Journal of Universal Computer Science 14(2): 193-210

DOI: 10.3217/jucs-014-02-0193

Authors: Benjamin Burkard, Georg Vogeler, Stefan Gruner

Abstract: This article is a revised and extended version of [VBG, 07]. We conjecture that the digitalization of historical text documents as a basis of data mining and information retrieval for the purpose of progress in the history sciences is urgently needed. We present a novel, specialist XML tool-suite supporting the working historian in the transcription of original medieval charters into a machine-readable form, and we also address some latest developments which can be found in the field since the publication of [VBG, 07].

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Research Article Mon, 28 Jan 2008 00:00:00 +0200
An Ontology-based Approach to Support Text Mining and Information Retrieval in the Biological Domain https://lib.jucs.org/article/28910/ JUCS - Journal of Universal Computer Science 13(12): 1881-1907

DOI: 10.3217/jucs-013-12-1881

Authors: Khaled Khelif, Rose Dieng-Kuntz, Pascal Barbry

Abstract: This paper describes an ontology-based approach aiming at helping biologists to annotate their documents and at facilitating their information retrieval task. Our approach, based on semantic web technologies, relies on formalised ontologies, semantic annotations of scientific articles and knowledge extraction from texts. We propose a method/system for the generation of ontology-based semantic annotations (MeatAnnot) and a system allowing biologists to draw advanced inferences on these annotations (MeatSearch). This approach was proposed to support biologists working on DNA microarray experiments in the validation and the interpretation of their results, but it can probably be extended to other massive analyses of biological events (as provided by proteomics, metabolomics...).

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Research Article Sat, 1 Dec 2007 00:00:00 +0200
An Adaptable Framework for Ontology-based Content Creation on the Semantic Web https://lib.jucs.org/article/28906/ JUCS - Journal of Universal Computer Science 13(12): 1835-1853

DOI: 10.3217/jucs-013-12-1835

Authors: Onni Valkeapää, Olli Alm, Eero Hyvönen

Abstract: Creation of rich, ontology-based metadata is one of the major challenges in developing the Semantic Web. Emerging applications utilizing semantic web techniques, such as semantic portals, cannot be realized if there are no proper tools to provide metadata for them. This paper discusses how to make provision of metadata easier and cost-effective by an annotation framework comprising of annotation editor combined with shared ontology services. We have developed an annotation system Saha supporting distributed collaboration in creating annotations, and hiding the complexity of the annotation schema and the domain ontologies from the annotators. Saha adapts flexibly to different metadata schemas, which makes it suitable for different applications. Support for using ontologies is based on ontology services, such as concept searching and browsing, concept URI fetching, semantic autocompletion and linguistic concept extraction. The system is being tested in various practical semantic portal projects.

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Research Article Sat, 1 Dec 2007 00:00:00 +0200
Efficient Access Methods for Temporal Interval Queries of Video Metadata https://lib.jucs.org/article/28862/ JUCS - Journal of Universal Computer Science 13(10): 1411-1433

DOI: 10.3217/jucs-013-10-1411

Authors: Spyros Sioutas, Kostas Tsichlas, Bill Vassiliadis, Dimitrios Tsolis

Abstract: Indexing video content is one of the most important problems in video databases. In this paper we present linear time and space algorithms for handling video metadata that represent objects or events present in various frames of the video sequence. To accomplish this, we make a straightforward reduction of this problem to the intersection problem in Computational Geometry. Our first result is an improvement over the one of V. S. Subrahmanian [Subramanian, 1998] by a logarithmic factor in storage. This is achieved by using different basic data structures. Then, we present two other interesting time-efficient approaches. Finally a reduction to a special geometric problem is considered according to which we can achieve two optimal in time and space solutions in main and external memory model of computation respectively. We also present an extended experimental evaluation.

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Research Article Sun, 28 Oct 2007 00:00:00 +0300
Ontology and Grammar of the SOPHIE Choreography Conceptual Framework - An Ontological Model for Knowledge Management https://lib.jucs.org/article/28844/ JUCS - Journal of Universal Computer Science 13(9): 1157-1183

DOI: 10.3217/jucs-013-09-1157

Authors: Sinuhé Arroyo

Abstract: Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management (KM) automation in SOA. Building services communicate with each other by exchanging self-contained messages. Depending on the specific requirements of the business model they serve and the application domain for which services were deployed, a number of mismatches (i.e. sequence and cardinality of messages exchanges, structure and format of messages and content semantics), can occur which prevent interoperation among a prior compatible services. Existing choreography technologies attempt to model such external visible behavior. However, they lack the consistent semantic support required to fully meet the necessities of heterogeneous KM environments. This paper describes the ontology and grammar of SOPHIE, a semantic service-based choreography framework for overcoming conversational pattern mismatches in knowledge intensive environments. Consequently, the paper provides an overview of the framework that depicts its main building blocks, so a good understantind of the ontology and grammar that summarize the conceptual model is gained. Such ontology allows the desing and description of fully fledged choreographies that can be used, as a result of a mediation task, to produce the mediating structures that in fact allow dynamic service-to-service interoperation. Finally, a use case centred in the telcomunications field serves as proof of concept of how SOPHIE is being applied.

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Research Article Fri, 28 Sep 2007 00:00:00 +0300
Improving LO Quality through Instructional Design Based on an Ontological Model and Metadata https://lib.jucs.org/article/28823/ JUCS - Journal of Universal Computer Science 13(7): 970-979

DOI: 10.3217/jucs-013-07-0970

Authors: Erla Morales, Francisco García-Peñalvo, Ángela Barrón

Abstract: The activities developed in this paper were aimed at providing an awareness of the elements that should be considered in quality learning objects instructional design for e-learning systems. We thus propose our own LO definition taking into account aggregation level number 2. On this basis, we analyze cognitive theories for promoting learning and we explain issues relating to the LO characteristics that help to improve their quality for suitable management. To achieve this we propose an instructional design based on an ontological model which explains the relationship between the instructional design elements and a specific classification to improve their management.

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Research Article Sat, 28 Jul 2007 00:00:00 +0300
Analysis of Conversation Quanta for Conversational Knowledge Circulation https://lib.jucs.org/article/28733/ JUCS - Journal of Universal Computer Science 13(2): 177-185

DOI: 10.3217/jucs-013-02-0177

Authors: Ken Saito, Hidekazu Kubota, Yasuyuki Sumi, Toyoaki Nishida

Abstract: In this paper, we present a computational approach to understanding and augmenting the conversational knowledge process. We introduce the concept of the conversation quantization, a technique of approximating a continuous flow of conversation by a series of conversation quanta that represent points of the discourse. To investigate what the nature of conversation quanta is, we attempt to extract conversation quanta from two types of the meeting videos by hand. As a result, we have obtained some profitable suggestions about conversation quanta.

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Research Article Wed, 28 Feb 2007 00:00:00 +0200
A Mechanism for Solving Conflicts in Ambient Intelligent Environments https://lib.jucs.org/article/28584/ JUCS - Journal of Universal Computer Science 12(3): 284-296

DOI: 10.3217/jucs-012-03-0284

Authors: Pablo Haya, Germán Montoro, Abraham Esquivel, Manuel García-Herranz, Xavier Alamán

Abstract: Ambient Intelligence scenarios describe situations in which multitude of devices and agents live together. In this kind of scenarios is frequent to see the appearance of conflicts when modifying the state of a device as for example a lamp. Those problems are not as much of sharing of resources as of conflict of orders coming from different agents. This coexistence must deal also with the desire of privacy of the different users over their personal information such as where they are, what their preferences are or to whom this information should be available. When facing incompatible orders over the state of a device it turns necessary to make a decision. In this paper we propose a centralised mechanism based on prioritized FIFO queues to decide the order in which the control of a device is granted. The priority of the commands is calculated following a policy that considers issues such as the commander's role, command's type, context's state and commander-context and commander-resource relations. Finally we propose a set of particular policies for those resources that do not adjust to the general policy. In addition we present a model pretending to integrate privacy through limiting and protecting contextual information.

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Research Article Tue, 28 Mar 2006 00:00:00 +0300
A Collaborative Biomedical Research System https://lib.jucs.org/article/28562/ JUCS - Journal of Universal Computer Science 12(1): 80-98

DOI: 10.3217/jucs-012-01-0080

Authors: Adel Taweel, Alan Rector, Jeremy Rogers

Abstract: The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent "chronicles" of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.

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Research Article Sat, 28 Jan 2006 00:00:00 +0200
Semantic Web Technologies Applied to e-learning Personalization in <e-aula> https://lib.jucs.org/article/28468/ JUCS - Journal of Universal Computer Science 11(9): 1470-1481

DOI: 10.3217/jucs-011-09-1470

Authors: Pilar Sancho, Iván Martínez, Baltasar Fernández-Manjón

Abstract: Despite the increasing importance gained by e-learning standards in the past few years, and the unquestionable goals reached (mainly regarding interoperability among e-learning contents) current e-learning standards are yet not sufficiently aware of the context of the learner. This means that only a limited support for adaptation regarding individual characteristics is currently being provided. In this article, we propose the use of semantic metadata for Learning Object (LO) contextualization in order to adapt instruction to the learner's cognitive requirements in three different ways: background knowledge, knowledge objectives and the most suitable learning style. In our pilot e-learning platform () the context for LOs is addressed in two different ways: knowledge domain and instructional design. We propose the use of ontologies as the knowledge representation mechanism to allow the delivery of learning material that is relevant to the current situation of the learner.

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Research Article Wed, 28 Sep 2005 00:00:00 +0300
On Complexity of Collective Communications on a Fat Cube Topology https://lib.jucs.org/article/28422/ JUCS - Journal of Universal Computer Science 11(6): 944-961

DOI: 10.3217/jucs-011-06-0944

Authors: Vladimir Kutálek, Václav Dvořák

Abstract: A recent renewed interest in hypercube interconnection network has been concentrated to the more scalable version known as a fat cube. The paper introduces several router models for fat nodes and uses them for cost comparison of both the hypercube and fat cube topologies. Analysis of time complexity of collective communications is done next and lower bounds on the number of communication steps are derived. Examples of particular communication algorithms on the 2D-fat cube topology with 8 processors are summarized and described in detail. The performed study shows that a large variety of fat cubes can provide much desired flexibility, trading cost for performance and manufacturability.

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Research Article Tue, 28 Jun 2005 00:00:00 +0300
A Provably Efficient Computational Model For Approximate Spatiotemporal Retrieval https://lib.jucs.org/article/28411/ JUCS - Journal of Universal Computer Science 11(6): 830-849

DOI: 10.3217/jucs-011-06-0830

Authors: Vasilis Delis, Christos Makris, Spyros Sioutas

Abstract: The paper is concerned with the effective and efficient processing of spatiotemporal selection queries under varying degrees of approximation. Such queries may employ operators like overlaps, north, during, etc., and their result is a set of entities standing approximately in some spatiotemporal relation with respect to a query object X. The contribution of the present work is twofold: i) it presents a formal mathematical framework for representing multidimensional relations at varying granularity levels, modelling relation approximation through the concept of relation convexity, ii) it subsequently exploits the proposed framework for developing approximate spatiotemporal retrieval mechanisms, combining a set of existing as well as new main memory and secondary memory data structures that achieve either optimal or the best known performance in terms of time and space complexity, for both the static and the dynamic setting.

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Research Article Tue, 28 Jun 2005 00:00:00 +0300
KMDL - Capturing, Analysing and Improving Knowledge-Intensive Business Processes https://lib.jucs.org/article/28377/ JUCS - Journal of Universal Computer Science 11(4): 452-472

DOI: 10.3217/jucs-011-04-0452

Authors: Norbert Gronau, Claudia Müller, Roman Korf

Abstract: Existing approaches in the area of knowledge-intensive processes focus on integrated knowledge and process management systems, the support of processes with KM systems, or the analysis of knowledge-intensive activities. For capturing knowledge-intensive business processes well known and established methods do not meet the requirements of a comprehensive and integrated approach of process-oriented knowledge management. These approaches are not able to visualise the decisions, actions and measures which are causing the sequence of the processes in an adequate manner. Parallel to conventional processes knowledge-intensive processes exist. These processes are based on conversions of knowledge within these processes. To fill these gaps in modelling knowledge-intensive business processes the Knowledge Modelling and Description Language (KMDL) got developed. The KMDL is able to represent the development, use, offer and demand of knowledge along business processes. Further it is possible to show the existing knowledge conversions which take place additionally to the normal business processes. The KMDL can be used to formalise knowledge-intensive processes with a focus on certain knowledge-specific characteristics and to identify process improvements in these processes. The KMDL modelling tool K-Modeler is introduced for a computer-aided modelling and analysing. The technical framework and the most important functionalities to support the analysis of the captured processes are introduced in the following contribution.

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Research Article Thu, 28 Apr 2005 00:00:00 +0300
RankFeed - Recommendation as Searching without Queries: New Hybrid Method of Recommendation https://lib.jucs.org/article/28353/ JUCS - Journal of Universal Computer Science 11(2): 229-249

DOI: 10.3217/jucs-011-02-0229

Authors: Maciej Kiewra

Abstract: The paper describes RankFeed a new adaptive method of recommendation that benefits from similarities between searching and recommendation. Concepts such as: the initial ranking, the positive and negative feedback widely used in searching are applied to recommendation in order to enhance its coverage, maintaining high accuracy. There are four principal factors that determine the method s behaviour: the quality document ranking, navigation patterns, textual similarity and the list of recommended pages that have been ignored during the navigation. In the evaluation part, the local site s behaviour of the RankFeed ranking is contrasted with PageRank. Additionally, recommendation behaviour of RankFeed versus other classical approaches is evaluated.

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Research Article Mon, 28 Feb 2005 00:00:00 +0200
Collaborative Web Browsing Based on Semantic Extraction of User Interests with Bookmarks https://lib.jucs.org/article/28350/ JUCS - Journal of Universal Computer Science 11(2): 213-228

DOI: 10.3217/jucs-011-02-0213

Authors: Jason Jung

Abstract: With the exponentially increasing amount of information available on the World Wide Web, users have b een getting more difficult to seek relevant information. Several studies have been conducted on the concept of adaptive approaches, in which the user s personal interests are taken into account. In this paper, we propose a user-support mechanism based on the sharing of knowledge with other users through the collaborative Web browsing, focusing specifically on the user s interests extracted from his or her own bookmarks. Simple URL based boo kmarks are endowed with semantic and structural information through the conceptualization based on ontology. In order to deal with the dynamic usage of bookmarks, ontology learning based on a hierarchical clustering method can be exploited. This system is composed of a facilitator agent and multiple personal agents. In experiments conducted with this system, it was found that approximately 53.1% of the total time was saved during collaborat ive browsing for the purpose of seeking the equivalent set of information, as compared with normal personal Web browsing.

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Research Article Mon, 28 Feb 2005 00:00:00 +0200
Software/Hardware Co-Design of Efficient and Secure Cryptographic Hardware https://lib.jucs.org/article/28339/ JUCS - Journal of Universal Computer Science 11(1): 66-82

DOI: 10.3217/jucs-011-01-0066

Authors: Nadia Nedjah, Luiza Mourelle

Abstract: Most cryptography systems are based on the modular exponentiation to perform the non-linear scrambling operation of data. It is performed using successive modular multiplications, which are time consuming for large operands. Accelerating cryptography needs optimising the time consumed by a single modular multiplication and/or reducing the total number of modular multiplications performed. Using a genetic algorithm, we first yield the minimal sequence of powers, generally called addition chain, that need to be computed to finally obtain the modular exponentiation result. Then, we exploit the co-design methodology to engineer a cryptographic device that accelerates the encryption/decryption throughput without requiring considerable hardware area. Moreover the obtained designed cryptographic hardware is completely secure against known attacks.

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Research Article Fri, 28 Jan 2005 00:00:00 +0200
Identifying Employee Competencies in Dynamic Work Domains: Methodological Considerations and a Case Study https://lib.jucs.org/article/28159/ JUCS - Journal of Universal Computer Science 9(12): 1500-1518

DOI: 10.3217/jucs-009-12-1500

Authors: Tobias Ley, Dietrich Albert

Abstract: We present a formalisation for employee competencies which is based on a psychological framework separating the overt behavioural level from the underlying competence level. On the competence level, employees draw on action potentials (knowledge, skills and abilities) which in a given situation produce performance outcomes on the behavioural level. Our conception is based on the competence performance approach by [Korossy 1997] and [Korossy 1999] which uses mathematical structures to establish prerequisite relations on the competence and the performance level. From this framework, a methodology for assessing competencies in dynamic work domains is developed which utilises documents employees have created to assess the competencies they have been acquiring. By means of a case study, we show how the methodology and the resulting structures can be validated in an organisational setting. From the resulting structures, employee competency profiles can be derived and development planning can be supported. The structures also provide the means for making inferences within the competency assessment process which in turn facilitates continuous updating of competency profiles and maintenance of the structures.

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Research Article Sun, 28 Dec 2003 00:00:00 +0200
Transparency and Transfer of Individual Competencies - A Concept of Integrative Competence Management https://lib.jucs.org/article/28140/ JUCS - Journal of Universal Computer Science 9(12): 1372-1380

DOI: 10.3217/jucs-009-12-1372

Authors: Kai Reinhardt, Klaus North

Abstract: The present state of research on competence management does not provide any suitable model that can be used in practice. Neither results from organizational nor from cognitive and social sciences meet the requirements for an application-oriented competence management completely as yet. An integrative competence management must be able to synchronise individual with organisational competencies. This linking is still neglected in research. A convenient solution has not been described yet. This article presents a model for an integrated competence management model, which gives approaches from both cognitive science and organizational science a practical framework of action.

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Research Article Sun, 28 Dec 2003 00:00:00 +0200
Structural Case-Based Reasoning and Ontology-Based Knowledge Management: A Perfect Match? https://lib.jucs.org/article/28055/ JUCS - Journal of Universal Computer Science 9(7): 608-626

DOI: 10.3217/jucs-009-07-0608

Authors: Ralph Bergmann, Martin Schaaf

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

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Research Article Mon, 28 Jul 2003 00:00:00 +0300
Process-oriented Knowledge Structuring https://lib.jucs.org/article/28038/ JUCS - Journal of Universal Computer Science 9(6): 542-550

DOI: 10.3217/jucs-009-06-0542

Authors: Kai Mertins, Peter Heisig, Kay Alwert

Abstract: Within a business environment, where the fast and reliable access to knowledge is a key success factor, an efficient handling of the organizational knowledge is crucial. Therefore the need for methods and techniques, which allow to structure and maintain complex knowledge bases according to the requirements emerging from the daily work have a high priority. This article provides a business process oriented approach to structure organizational knowledge and information bases. The approach was developed within applied research in the industrial, service and administrative sector. Following this approach, three different types of knowledge structures and their visualization have been developed by the Fraunhofer IPK and are currently applied and tested in organizations. Beside the approach itself, these three types of knowledge structure and the cases of application shall be introduced here.

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Research Article Sat, 28 Jun 2003 00:00:00 +0300
Unified Access to Heterogeneous Audiovisual Archives https://lib.jucs.org/article/28031/ JUCS - Journal of Universal Computer Science 9(6): 510-519

DOI: 10.3217/jucs-009-06-0510

Authors: Y. Avrithis, G. Stamou, M. Wallace, F. Marques, Philippe Salembier, X. Giro, Werner Haas, Heribert Vallant, Michael Zufferey

Abstract: In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This innovative system exploits the advances in handlings a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced services characterized by the tri-fold semantic phrasing of the request (query), unified handling and personalized response. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of the queries that users my issue, and the context determined by user profiles. It also provides a personalization process of the response in order to provide end_users with desired information. From a technical point of view, the FAETHON system plays the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to the new MPEG standards.

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Research Article Sat, 28 Jun 2003 00:00:00 +0300
XML and MPEG-7 for Interactive Annotation and Retrieval using Semantic Meta-data https://lib.jucs.org/article/27916/ JUCS - Journal of Universal Computer Science 8(10): 965-984

DOI: 10.3217/jucs-008-10-0965

Authors: Mathias Lux, Werner Klieber, Jutta Becker, Klaus Tochtermann, Harald Mayer, Helmut Neuschmied, Werner Haas

Abstract: The evolution of the Web is not only accompanied by an increasing diversity of multimedia but by new requirements towards intelligent research capabilities, user specific assistance, intuitive user interfaces and platform independent information presentation. To reach these and further upcoming requirements new standardized Web technologies and XML based description languages are used. The Web Information Space has transformed into a Knowledge marketplace where worldwide located participants take part into the creation, annotation and consumption of knowledge. This paper points out the design of semantic retrieval frameworks and a prototype implementation for audio and video annotation, storage and retrieval using the MPEG-7 standard and semantic web reference implementations. MPEG-7 plays an important role towards the standardized enrichment of multimedia with semantics on higher abstraction levels and a related improvement of query results.

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Research Article Mon, 28 Oct 2002 00:00:00 +0200
Components of a Model of Context-Sensitive Hypertexts https://lib.jucs.org/article/27913/ JUCS - Journal of Universal Computer Science 8(10): 924-943

DOI: 10.3217/jucs-008-10-0924

Authors: Alexander Mehler

Abstract: On the background of rising Intranet applications the automatic generation of adaptable, context-sensitive hypertexts becomes more and more important [El-Beltagy et al., 2001]. This observation contradicts the literature on hypertext authoring, where Information Retrieval techniques prevail, which disregard any linguistic and context-theoretical underpinning. As a consequence, resulting hypertexts do not manifest those schematic structures, which are constitutive for the emergence of text types and the context-mediated understanding of their instances, i.e. natural language texts. This paper utilizes Systemic Functional Linguistics (SFL) and its context model as a theoretical basis of hypertext authoring. So called Systemic Functional Hypertexts (SFHT) are proposed, which refer to a stratified context layer as the proper source of text linkage. The purpose of this paper is twofold: First, hypertexts are reconstructed from a linguistic point of view as a kind of supersign, whose constituents are natural language texts and whose structuring is due to intra- and intertextual coherence relations and their context-sensitive interpretation. Second, the paper prepares a formal notion of SFHTs as a first step towards operationalization of fundamental text linguistic concepts. On this background, SFHTs serve to overcome the theoretical poverty of many approaches to link generation.

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Research Article Mon, 28 Oct 2002 00:00:00 +0200
Topic Map Generation Using Text Mining https://lib.jucs.org/article/27889/ JUCS - Journal of Universal Computer Science 8(6): 623-633

DOI: 10.3217/jucs-008-06-0623

Authors: Karsten Böhm, Gerhard Heyer, Uwe Quasthoff, Christian Wolff

Abstract: Starting from text corpus analysis with linguistic and statistical analysis algorithms, an infrastructure for text mining is described which uses collocation analysis as a central tool. This text mining method may be applied to different domains as well as languages. Some examples taken form large reference databases motivate the applicability to knowledge management using declarative standards of information structuring and description. The ISO/IEC Topic Map standard is introduced as a candidate for rich metadata description of information resources and it is shown how text mining can be used for automatic topic map generation.

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Research Article Fri, 28 Jun 2002 00:00:00 +0300
Bibliometric Analysis and Visualisation of Intellectual Capital https://lib.jucs.org/article/27878/ JUCS - Journal of Universal Computer Science 8(5): 516-525

DOI: 10.3217/jucs-008-05-0516

Authors: Andrea Kasztler, Karl-Heinz Leitner

Abstract: On the basis of an example gained from the perspective of a person reading Intellectual Capital (IC) reports this paper explains the method of BibTechMonTM which is based on an analysis of the co-occurrence of different terms within databases and the algorithm to visualise the results [Kopcsa, A., Schiebel, E. (1998b)]. The application of this method for the IC report is currently a major step in improving the IC reporting system within ARC Seibersdorf research GmbH. In this paper the advantages and potentials of using BibTechMonTM in the context of IC reporting will be demonstrated by means of the 2001 IC report of ARC Seibersdorf research GmbH.

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Research Article Tue, 28 May 2002 00:00:00 +0300
MPEG and its Relevance for Content-based Multimedia Retrieval https://lib.jucs.org/article/27801/ JUCS - Journal of Universal Computer Science 7(6): 530-547

DOI: 10.3217/jucs-007-06-0530

Authors: Werner Haas, Harald Mayer

Abstract: The utilization of new emerging standards such as MPEG-7 is expected to be a major breakthrough for content-based multimedia data retrieval. The main features of the MPEG standards series and of related standards, formats and protocols are presented. It is discussed, how they, despite their partially early and immature stage, can best be utilized to yield effective results in the context of a knowledge management environment. Complementary to that, the current status and state of the art in content-based retrieval for images, video and audio content is briefly presented. In the context of the KNOW-Center we are developing a prototype platform to implement a user friendly and highly informative access to audiovisual content as a potential component for a future knowledge management system. The technical requirements and the system architecture for the prototype platform are described.

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Research Article Thu, 28 Jun 2001 00:00:00 +0300
Categorisation by Context https://lib.jucs.org/article/27512/ JUCS - Journal of Universal Computer Science 4(9): 719-736

DOI: 10.3217/jucs-004-09-0719

Authors: Giuseppe Attardi, Sergio Marco, Davide Salvi

Abstract: Assistance in retrieving of documents on the World Wide Web is provided either by search engines, through keyword based queries, or by catalogues, which organise documents into hierarchical collections. Maintaining catalogues manually is becoming increasingly difficult due to the sheer amount of material on the Web, and therefore it will be soon necessary to resort to techniques for automatic classification of documents. Classification is traditionally performed by extracting information for indexing a document from the document itself. The paper describes the technique of categorisation by context, which exploits the context perceivable from the structure of HTML documents to extract useful information for classifying the documents they refer to. We present the results of experiments with a preliminary implementation of the technique.

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Research Article Mon, 28 Sep 1998 00:00:00 +0300