Irfan Riaz. Department of Electronics, Communication Engineering, Hanyang University, South Korea.

In this paper, we present a new human detection scheme for thermal images by using CENsus TRansform hISTogram ( CENTRIST) features and Support Vector Machines (SVMs). Human detection in a thermal image is a difficult task due to low image resolution, thermal noising, lack of color, and poor texture information. For thermal images, contour is one of the most useful and discriminative information, so capturing it efficiently is important. Histogram of Oriented Gradient ( HOG) is still the most proven way to capture the human contour. CENTRIST is a computationally efficient technique to capture contour cues as compared to HOG, but so far no one has implemented and tested the accuracy of CENTRIST descriptor for infrared thermal images. We developed CENTRIST based human dete ction system for thermal images and tested its variants. We also made a new dataset of thermal images, since there was no realistic dataset. Experimental result s show that CENTRIST exhibits better detection accuracy than HOG, while reducing the training and the testing time significantly.

Martin Steiger. Fraunhofer IGD, Fraunhoferstr., 64283 Darmstadt Thorsten May. Fraunhofer IGD, Fraunhoferstr., 64283 Darmstadt Jörn Kohlhammer. Fraunhofer IGD, Fraunhoferstr., Darmstadt

In this paper we present a set of extension techniques to stabilize interactive dynamic graph layout algorithms. It works with different existing Focus & Context methods. We first deal with the initial placement of newly inserted nodes to mitigate acting forces in the layout algorithm. Then, their influence on the existing layout is gradually increased to create a smooth transition between the old and the new layout. To complement this approach we use a look -ahead strategy that integrates additional nodes in the layout to stabilize the layout even more. Our approach is validated using both quantitative and qualitative measures.

Diana Fernández Prieto. Universidad de los Andes. Bogotá, Colombia. Researcher

Decision making in the context of urban and regional planning requires communication among different stakeholders. This communication process has several barriers because of domain differences, the different nature and types of data, lack of integrated analysis tools, deficiencies in the interaction with data, and information overload. To overcome these difficulties, interactive visualizations are commonly used, and the

Daw-Tung Lin. Department of Computer Science, Information Engineering, National Taipei

Pedestrian tracking plays a crucial role in security and intelligent video surveillance. Occlusion handling is a challenging concern in tracking multiple people. Adaptive, ad vanced solutions are required to accurately track pedestrians for video surveillance purposes. This paper presents a novel method of tracking multiple people in occlusion conditions. In this study, we developed a combined component - based human-shaped template and a particle filter, resulting in improved object occlusion handling. The proposed system is capable of tracking specific people in real -time, as well as handling the occlusion of multiple objects. We predicted occlusions using the Kalman filter, and tracked objects continuously using our component -shape-template particle filter. The experimental results show that our algorithm is feasible and stable. In tests, the proposed tracking algorithm achieved an accuracy of up to 99.7%. The proposed approach outperformed that of comparable methods in analyzing all test video datasets in this study. Our low false -negative rate demonstrates that the proposed tracking method is robust and offers superior occlusion handling.

Bernard Ijesunor Akhigbe. Obafemi Awolowo University, Nigeria. Researcher

The potentials of factor analysis are summarily to reveal underlying factor structures, test the theory of analytic user-centric models, and demonstrate their causal relationships. Its common applicative types are: Exploratory, confirmatory and structural equation modelling techniques. This paper, underscores it basically due to its multidimensional and multivariate data analytic knack. Analytically, it manages the multidimensionality of user -centric data and yet present s replicable result. Therefore, o ur enthusiasm hopefully is to whet the appetite of IS researche rs, particularly those that are engaged in user -centric evaluative research in a way that motivates them t o pursue additional knowledge about the technique. However, this technique has matured in other related fields , such as: The C ognitive and Behavioural sciences, H uman Computer Interaction and Psychology from where IS draws from in terms of user - related studies. This is not yet the case in IS. An algorithmic like framework i s therefore introduced, and its use to assess an IS with the purpose of underscoring the use of the FA methodology is reported in fulfillment of the aim of this paper. As a result, a m easurement model is presented. The use of the FA technique is therefore recommended based on the degree of validity and reliability demonstrated by the model, which is significantly replicable.

Amine Chohra. Images, Signals Researcher, Intelligent Systems Laboratory (LISSI / EA 3956), Paris-East Researcher

Decision-making in negotiation with incomplete i nformation, having an irrational part, is a complex problem. Inspired from research works aiming to analyze human behavior and those o n social negotiation psychology, the integration of personality aspects, with the essential time parameter, is becoming necessary. For this purpose, first, one to one bargaining process, in which a buyer agent and a seller agent negotiate over single issue (price), is developed , where the basic behaviors based on time (Faratin et al. , 1998) and personality aspects (conciliatory, neutral, and aggressive) are suggested . Second, a cognitive approach, based on the five -factor model in personality (Fiske, 1949; Tupes and Christal, 1961; Norman, 1963), is suggested to control the resulting time -personality behaviors with incomplete information. In fact, the five factors are the extraversion, the agreeableness, the conscientiousness, the neuroticism, and the openness to experience. Afterwards, experimental environments and measures, allowing a set of experiments are detailed. Results, concerning time - personality behaviors, demonstrate that more increasing conciliatory aspects lead to increased agreement point (price) and decreased agreement time, and more increasing aggressive aspects lead to decreased agreement point and increased agreement time. Finally, from a study case, of three different personalities corresponding to three different cognitive orientations, experimental results illustrate the promising way of the suggested cognitive approach in the control of the time-personality behaviors.

Martin D. Sykora, Thomas W. Jackson, Ann O’Brien, Suzanne Elayan

With the uptake of social media, such as Facebook and Twitter, there is now a vast amount of new user generated content on a daily basis, much of it in the form of short, informal free -form text. Businesses, institutions, governments and law enforcement organisations are now actively seeking ways to monitor and more generally analyse public response to various events, products and services. Our primary aim in this project was the development of an approach for capturing a wide an d comprehensive range of emotions from sparse, text based messages in social -media, such as Twitter, to help monitor emotional responses to events. Prior work has focused mostly on negative / positive sentiment classification tasks, and although numerous approaches employ highly elaborate and effective techniques with some success, the sentiment or emotion granularity is generally limiting and arguably not always most appropriate for real-world problems. In this paper we employ an ontology engineering appr oach to the problem of fine -grained emotion detection in sparse messages. Messages are also processed using a custom NLP pipeline, which is appropriate for the sparse and informal nature of text encountered on micro -blogs. Our approach detects a range of e ight high -level emotions; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. We report f -measures (recall and precision) and compare our approach to two related approaches from recent literature.

Cuneyt Yucelbas. Dept. Of Electrical-Electronics Engineering, Selcuk University, Konya Researcher, TURKEY. Researcher

Sleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in di fferent time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring systems get popularity. In this study, we obtained EEG, EMG and EOG sign als of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transf orm (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others . According to th e results, the maximum test classification accuracy was reported as 79.72% by using of FFT method for subject1. Also, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,58 and 70.34% with use of FFT, Welch and AR, respectively.

Henryk Piech. Czestochowa University of Technology, Dabrowskiego, Poland Researcher

In the presented investigation, communication security aspects are analyzed in an audited protocol operation run. The run is represented by mutually interleaved protocols. Each protocol consists of communication operations and these operations are decomposed into action sets. An action can play the role of an argument in logic rules described in works [3]. Rules help to extract security attributes which will be corrected after every operation. The action recognition is part of the proposed method and the adequate algorithm refers to protocols, keys, messages and users. The correction part refers to jurisdiction over m essage, believing in user honesty, key sharing, information freshness, the degree of encryption, etc. The investigation analysis and equivalent procedures are not complex and may be realized dynamically during the auditing process.

Martin Lukac. Graduate School of Information Sciences, Tohoku University, Sendai Researcher, Japan. Researcher

A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), an d behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In each of the above mentioned modules, many different algorithms and algorithms networks exists an d provide various performances on a case by case basis. Thus, a mechanism that for any of the three computational stages selects the best possible algorithm is required. We propose a platform based on the algorithm selection approach to the problem of natu ral image understanding. This selection mechanism is based on machine learning; a bottom -up algorithm selection from real -world image features and a top -down algorithm selection using information obtained from a high level symbolic world description and al gorithm suitability. To accommodate the high -speed processing requirements, the high -frequency of real -time reconfiguration and a low -cost of implementation, we are using present a novel dynamic reconfigurable VLSI processor for real-time adaptation of the algorithm selection. The new architecture includes a fine - grain Digital Reconfigurable Processor, a distributed configuration memory to solve the data transfer bottleneck and an intra-chip packet routing scheme to reduce the size of the configuration memory.

Zehra Kavasoğlu. Istanbul Technical University Istanbul/Turkey

Every e-commerce web site today has the product review feature which allows customers to express their opinions and comments about the product they have purchased. These comments are important for potential customers when deciding which product to buy. However, reading large amounts of cust omer reviews available for each product is a time consuming process. For this reason, customers usually tend to read small pieces of topmost comments and skip the rest of them. Also, depending on personal preferences and needs, customers might be interested in different features of various products. Therefore, a feature based summarization of the products is very helpful for potential customers in selecting the best product option. Existing feature based review summarization methods create a product summary for a common user profile ignoring the individual preferences. In this paper, we propose a novel feature based approach for personalized review summarization by giving importance to potential individual customer preferences. In order to evaluate our metho d, a dataset has been collected from a popular Turkish e - commerce web site. The experimental results show that our method is successful in finding and summarizing the most relevant reviews for the active user.

Pedro Isaías

Editorial preface for Volume 8, Issue 2 of the IADIS International Journal on Computer Science and Information Systems (IJCSIS). This issue brings together peer-reviewed original research contributions covering the wide spectrum of Information Systems, Artificial Intelligence, Software Engineering, and Digital Transformation.