DOI: 10.3897/jucs.154672

Authors: Aleksandar Velinov, Aleksandra Mileva, Simon Volpert, Sebastian Zillien, Steffen Wendzel

Abstract: Network steganography has existed for several decades and it uses network traffic and network protocols as carriers for embedding secret messages in a stealthy manner. Quick UDP Internet Connections (QUIC) is a novel secure and reliable transport layer network protocol that is encapsulated in the User Datagram Protocol (UDP) and utilizes the Transport Layer Security Version 1.3 (TLSv1.3) standard. In addition, Hypertext Transfer Protocol Version 3 (HTTP/3) employs QUIC. In this paper, we present a systematic analysis of the covert channels that can be found in QUIC. Twenty novel covert channels are identified by applying the latest covert channel pattern based taxonomy, and an analysis of their transmission rate, undetectability, and robustness is presented, together with suggested countermeasures. A single covert channel is implemented as a proof of concept tool and is appropriately evaluated.

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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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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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DOI: 10.3897/jucs.94134

Authors: Loveleen Kumar, Manish Jain

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

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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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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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DOI: 10.3897/jucs.94132

Authors: Vijay Krishna Pallaw, Kamred Udham Singh

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

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DOI: 10.3897/jucs.94127

Authors: Lalan Kumar, Kamred Udham Singh

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

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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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DOI: 10.3897/jucs.2020.059

Authors: Ranjan Behera, Sushree Das, Santanu Rath, Sanjay Misra, Robertas Damasevicius

Abstract: Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

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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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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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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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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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DOI: 10.3217/jucs-025-08-1018

Authors: Rodrigo Barbosa Sounited States Of America Orrego, Jorge Luis Victória Barbosa

Abstract: Resources of a city are urban assets such as hospitals and pharmacies (health facilities) or accessible ramps and adapted toilets (accessibility resources). This paper addresses the problem of resource management for smart cities combining crowdsourcing with gamification, and proposes a model called CORE-MM. This model allows the use of crowdsourcing techniques so that the management of cities resources is done by the citizens, without having to rely on an organization or public administration. To encourage participation in this resource management, this model also uses techniques of gamification. CORE-MM proposes the use of crowdsourcing integrated with gamification to manage the resources of a smart city, with two interdependent objectives: to motivate the use of the system by the users, and to encourage their participation in the sharing and management of information. The scientific contribution of this work is that CORE-MM treats the resource management considering a generic resources approach for smart cities. A prototype of CORE-MM was offered to volunteers and a questionnaire was developed to collect data and to evaluate the model, its performance and relevance. Results with volunteers indicated good perceived ease of use and good perceived utility. From the affirmations of the questionnaire that the 10 volunteers that tested the CORE-MM prototype had to answer, 91.67% agreed on the ease of use of the system and 8.33% manifested indifference in their responses. Regarding the utility of the system, 99.17% agreed and only 0.83% were indifferent. These results point to positive perspectives regarding the use of the application in possible situations and real locations.

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DOI: 10.3217/jucs-025-04-0418

Authors: Zeynep Ozger, Bulent Bolat, Banu Diri

Abstract: Microarray technology is widely used to report gene expression data. The inclusion of many features and few samples is one of the characteristic features of this platform. In order to define significant genes for a particular disease, the problem of high-dimensionality microarray data should be overcome. The Artificial Bee Colony (ABC) Algorithm is a successful meta-heuristic algorithm that solves optimization problems effectively. In this paper, we propose a hybrid gene selection method for discriminatively selecting genes. We propose a new probabilistic binary Artificial Bee Colony Algorithm, namely PrBABC, that is hybridized with three different filter methods. The proposed method is applied to nine microarray datasets in order to detect distinctive genes for classifying cancer data. Results are compared with other wellknown meta-heuristic algorithms: Binary Differential Evolution Algorithm (BinDE), Binary Particle Swarm Optimization Algorithm (BinPSO), and Genetic Algorithm (GA), as well as with other methods in the literature. Experimental results show that the probabilistic self-adaptive learning strategy integrated into the employed-bee phase can boost classification accuracy with a minimal number of genes.

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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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DOI: 10.3217/jucs-024-06-0725

Authors: Xiucai Ye, Tetsuya Sakurai

Abstract: The analysis of microarray gene expression data to obtain useful information is a challenging problem in bioinformatics. Feature selection is an efficient computational technique in processing the analysis of high-dimensional microarray data. Due to the lack of label information in practice, unsupervised feature selection is considered to be more practically important and correspondingly more difficult. In this paper, we propose a novel unsupervised feature selection method, which utilizes local regression and discriminant analysis for structure learning on microarray gene expression data. By imposing row sparsity on the weight matrix through l2,1-norm regularization, the proposed method optimizes for selecting the discriminative genes which are more informative and better capture the interesting natural classes of samples. We develop an effective algorithm to solve the l2,1-norm-based optimization problem in our method and present the convergence analysis. Finally, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method not only achieves good performance, but also outperforms other state-of-the-art unsupervised feature selection methods.

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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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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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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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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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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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DOI: 10.3217/jucs-020-10-1433

Authors: Rolando Quintero, Giovanni Guzman, Miguel Torres, Rolando Menchaca-Mendez, Marco Moreno-Ibarra, Felix Mata

Abstract: Nowadays, there are many applications such as disaster mitigation, survey-ing or geology-support, and others where Digital Elevation Models (DEM) are useful in the field. DEM typically requires a huge amount of data, making the tasks of DEMtransmission over a wireless network or storing and displaying it in a mobile device very complex. These tasks are important challenges in computer science research. Up-to-date, the compression techniques are used to compress DEMs with a high compression coefficient, nevertheless, whether the user requires to access the file or to obtain certaininformation about the raster data, it is necessary to decompress the entire DEM file. In consequence, these approaches are not well suited for applications focused on deviceswith limited hardware resources. In this paper, a novel compression/decompression technique is presented. This approach is capable of obtaining the specific parameterssuch as altitudes and contour lines of a sub-region of the DEM without using a full decompression stage. A detailed analysis of the properties and complexity of our approachis presented.

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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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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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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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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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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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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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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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DOI: 10.3217/jucs-018-09-1093

Authors: Feiyu Lin, Kurt Sandkuhl, Kevin Xu

Abstract: The Internet of Things (IoT) aims at linking smart objects that are relevant to the user and embedding intelligence into the environment. It is more and more accepted in the scientific community and expected by end users, that pervasive services should be able to adapt to the circumstances or situation in which a computing task takes place, and maybe even detect all relevant parameters for this purpose. Work presented in this paper addresses the challenge of bringing together concepts and experiences from two different areas: context modeling and ontology matching. Current work in the field of automatic ontology matching does not sufficiently take into account the context of the user during the matching process. The main contributions of this paper are (1) the introduction of the concept of "context" in the ontology matching process, (2) an approach for context-based semantic matching, which is building on different (weighted) levels of overlap for a better ranking of alignment elements depending on user's context, (3) an evaluation of the context-based matching in experiments and from user's perspectives.

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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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DOI: 10.3217/jucs-017-07-1021

Authors: Muhammad Fahad, Nejib Moalla, Abdelaziz Bouras, Muhammad Qadir, Muhammad Farukh

Abstract: The massive growth in ontology development has opened new research challenges such as ontology management, search and retrieval for the entire semantic web community. These results in many recent developments, like OntoKhoj, Swoogle, OntoSearch2, that facilitate tasks user have to perform. These semantic web portals mainly treat ontologies as plain texts and use the traditional text classification algorithms for classifying ontologies in directories and assigning predefined labels rather than using the semantic knowledge hidden within the ontologies. These approaches suffer from many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define an ontology classification problem and categorize it into many sub-problems. We present a new ontological methodology for the classification of web ontologies, which has been guided by the requirements of the emerging Semantic Web applications and by the lessons learnt from previous systems. The proposed framework, OntClassifire, is tested on 34 ontologies with a certain degree of overlapping domain, and effectiveness of the ontological mechanism is verified. It benefits the construction, maintenance or expansion of ontology directories on the semantic web that help to focus on the crawling and improving the quality of search for the software agents and people. We conclude that the use of a context specific knowledge hidden in the structure of ontologies gives more accurate results for the ontology classification.

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DOI: 10.3217/jucs-017-06-0961

Authors: Zhuzhong Qian, Sheng Zhang, Kangbin Yim, Sanglu Lu

Abstract: Service composition is an effective approach for large-scale multimedia delivery. In previous works, user requirement is represented as one fixed functional path which is composed of several functional components in a certain order. Actually, there may be several functional paths (deliver different quality level multimedia data, e.g., image pixel, frame rate) that can meet one request. And due to the diversity of devices and connections in pervasive environment, system should choose a suitable media quality delivery path in accordance with context, instead of one fixed functional path. This paper presents a deep study of multimedia delivery problem and proposes an on-line algorithm LDPath and an off-line centralized algorithm LD/RPath respectively. LDPath aims at delivering multimedia data to end user with lowest delay by choosing services to build delivery paths hop-by-hop, which is adapted to the unstable open environment. And LD/RPath is developed for a relatively stable environment, which generates delivery paths according to the trade-off between delay and reliability metrics, because the service reliability is also an important fact in such scenario. Experimental results show that both algorithms have good performance with low overhead to the system.

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DOI: 10.3217/jucs-016-09-1139

Authors: Peter Danner, Daniel Hein

Abstract: The use of modern computing equipment by emergency service units in a disaster area assures increased efficiency during disaster response. Emergency devices must be easy to use and secure. Trusted Computing is a promising approach to help protect the software integrity of commodity emergency devices and thus increase their security. To efficiently use Trusted Computing in an emergency scenario it is necessary to establish an initial trust relationship between the emergency infrastructure providers and a user, her devices, and the software running on those devices. Currently, this requires physical presence of the involved entities. In this paper we propose a remote protocol that employs electronic identity facilities and Trusted Computing to aggregate the identity of a user, the identity of her devices and a set of trusted software states as well as the users facilities and skills. Such a protocol alleviates the need for physical presence. Thus, the protocol facilitates deployment of new electronic emergency equipment, while maintaining a high level of security. We belief that such a protocol is an important step in the process of introducing new capabilities for disaster response.

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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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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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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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DOI: 10.3217/jucs-015-03-0678

Authors: Martin Memmel, Michael Kockler, Rafael Schirru

Abstract: In today's information environments, tagging is widely used to provide information about arbitrary types of digital resources. This information is usually created by end users with different motivations and for different kinds of purposes. When aiming to support users in the tagging process, these differences play an important role. In this paper several approaches to generate tag recommendations are discussed, and a prototypical recommender system for the social resource sharing platform ALOE is presented. This interactive system allows users to control the generation of the recommendations by selecting the sources to be used as well as their impact. The component was introduced at DFKI, and a first evaluation showed that the recommender component was considered as helpful by a majority of users.

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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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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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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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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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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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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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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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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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DOI: 10.3217/jucs-011-02-0271

Authors: Juliusz Kulikowski

Abstract: There is considered a problem of information profiles and information resources collection forming in distributed data- and/or knowledge bases as a result of an attempt to satisfy the information requirements of the customers represented by their information profiles. It is shown that the interests of managers of data- and knowledge bases are not fully convergent and that they participate in a composite, partially co-operative, partially non-co-operative n-persons games. There is given a formal description of the strategies used in such games, as well as the methods of decision making of the players on the level of open access (OADB) as well as on local (LDB) databases one.

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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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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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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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