Search results for: Behavior based advising

Authors: A. Kowcika, E. Umamaheswari, T.V. Geetha

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In this paper we focus on event extraction from Tamil news article. This system utilizes a scoring scheme for extracting and grouping event-specific sentences. Using this scoring scheme eventspecific clustering is performed for multiple documents. Events are extracted from each document using a scoring scheme based on feature score and condition score. Similarly event specific sentences are clustered from multiple documents using this scoring scheme. The proposed system builds the Event Template based on user specified query. The templates are filled with event specific details like person, location and timeline extracted from the formed clusters. The proposed system applies these methodologies for Tamil news articles that have been enconverted into UNL graphs using a Tamil to UNL-enconverter. The main intention of this work is to generate an event based template.

Keywords: Event Extraction, Score based Clustering, Segmentation, Template Generation.

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: Daily activity recognition, healthcare, IoT sensors, transfer learning.

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: Creative problem-based play, educational games, flexible thinking, tertiary education.

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Authors: Shiyang Zhu, G. Q. Lo, D. L. Kwong

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Silicon photonics is merging as a unified platform for driving photonic based telecommunications and for local photonic based interconnect but it suffers from large footprint as compared with the nanoelectronics. Plasmonics is an attractive alternative for nanophotonics. In this work, two CMOS compatible plasmonic waveguide platforms are compared. One is the horizontal metal-insulator-Si-insulator-metal nanoplasmonic waveguide and the other is metal-insulator-Si hybrid plasmonic waveguide. Various passive and active photonic devices have been experimentally demonstrated based on these two plasmonic waveguide platforms.

Keywords: Plasmonics, on-chip integration, Silicon photonics.

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Authors: Ramin Halavati, Saeed B. Shouraki, Mahdieh Noroozian, Saman H. Zadeh

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Packing problems arise in a wide variety of application areas. The basic problem is that of determining an efficient arrangement of different objects in a region without any overlap and with minimal wasted gap between shapes. This paper presents a novel population based approach for optimizing arrangement of irregular shapes. In this approach, each shape is coded as an agent and the agents' reproductions and grouping policies results in arrangements of the objects in positions with least wasted area between them. The approach is implemented in an application for cutting sheets and test results on several problems from literature are presented.

Keywords: Optimization, Bin Packing, Agent Based Systems.

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Authors: Orly Redlich

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Since the establishment of the state of Israel, and as a result of various events that led to it, Jewish citizens and Arab citizens of the state have been in constant conflict, which finds its expression in most levels of life. Therefore, the attitude of one group member to the other group members is mostly tense, loaded, and saturated with mutual suspicion. Within this reality, in many higher education institutions in Israel, Jews and Arabs meet with each other intensively and for several years. For some students, this is their first opportunity for a meaningful cross-cultural encounter. These intercultural encounters, which allow positive interactions between members of different cultural groups, may contribute to the formation of "intercultural competence" which means long-term change in knowledge, attitudes, and behavior towards 'the other culture'. The current study examined the concept of the ‘other’ among Jewish and Arab students studying together and their "intercultural competence". The study also examined whether there is a difference in the perception of the ‘other’ between students studying in different academic programs, and between students taking academic courses on multiculturalism. This quantitative study was conducted among 274 Arab and Jewish students studying together, for bachelors or master's degree, in various academic programs at the Israel Academic College of Ramat-Gan. The background data of the participants are varied, in terms of religion, origin, religiosity, employment status, living area, and marital status. The main hypothesis is that academic, social, and intercultural encounters between Jewish and Arab students, who attend college together, will be a significant factor in building "intercultural competence". Additionally, the existence of "intercultural competence" has been linked to demographic characteristics of the students, as well as the nature of intercultural encounters between Jews and Arabs in a higher education institution. The dependent variables were measured by a self-report questionnaire, using the components of '"intercultural competence"' among students, which are: 1. Cognitive knowledge of the ‘others’, 2. Feelings towards the ‘others’, 3. Change in attitudes towards the 'others', and 4. Change in behavior towards the ‘others’. The findings indicate a higher "intercultural competence" among Arab students than Jews; it was also found higher level of "intercultural competence" among Educational Counseling students than the other respondents. The importance of this research lies in finding the means to develop "intercultural competence" among Jewish and Arab students, which may reduce prejudice and stereotypes towards the other culture and may even prevent occurrences of alienation and violence in cross-cultural encounters in Israel.

Keywords: Cross-cultural learning, "intercultural competence", Jewish and Arab students, multiculturalism.

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Authors: A. Kamath, Z. X. Chen, C. J. Gu, F. Zheng, X. P. Wang, N. Singh, G-Q. Lo

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In this paper we use low frequency noise analysis to understand and map the current conduction path in a multi gate junctionless FinFET.  The device used in this study behaves as a gated resistor and shows excellent short channel effect suppression due to its multi gate structure. Generally for a bulk conduction device like the junctionless device studied in this work, the low frequency noise can be modelled using the mobility fluctuation model; however for this device we can also see the effect of carrier fluctuations on the LFN characteristic. The noise characteristic at different gate bias and also the possible location of the traps is explained.

Keywords: LFN analysis, junctionless, Current conduction path, FinFET.

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Authors: Abdolamir Nekoubin

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In this paper the behavior of fixed series compensated extra high voltage transmission lines during faults is simulated. Many over-voltage protection schemes for series capacitors are limited in terms of size and performance, and are easily affected by environmental conditions. While the need for more compact and environmentally robust equipment is required. use of series capacitors for compensating part of the inductive reactance of long transmission lines increases the power transmission capacity. Emphasis is given on the impact of modern capacitor protection techniques (MOV protection). The simulation study is performed using MATLAB/SIMULINK®and results are given for a three phase and a single phase to ground fault.

Keywords: Series compensation, MOV - protected series capacitors, balanced and unbalanced faults

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Authors: C. Alexandru, P. Alexandru

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The paper presents the virtual model of the active suspension system used for improving the dynamic behavior of a motor vehicle. The study is focused on the design of the control system, the purpose being to minimize the effect of the road disturbances (which are considered as perturbations for the control system). The analysis is performed for a quarter-car model, which corresponds to the suspension system of the front wheel, by using the DFC (Design for Control) software solution EASY5 (Engineering Analysis Systems) of MSC Software. The controller, which is a PIDbased device, is designed through a parametric optimization with the Matrix Algebra Tool (MAT), considering the gain factors as design variables, while the design objective is to minimize the overshoot of the indicial response.

Keywords: Active suspension, Controller, Dynamics, Vehicle

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Authors: Rajkishore Prasad, Hiroshi Saruwatari, Kiyohiro Shikano

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This paper describes Independent Component Analysis (ICA) based fixed-point algorithm for the blind separation of the convolutive mixture of speech, picked-up by a linear microphone array. The proposed algorithm extracts independent sources by non- Gaussianizing the Time-Frequency Series of Speech (TFSS) in a deflationary way. The degree of non-Gaussianization is measured by negentropy. The relative performances of algorithm under random initialization and Null beamformer (NBF) based initialization are studied. It has been found that an NBF based initial value gives speedy convergence as well as better separation performance

Keywords: Blind signal separation, independent component analysis, negentropy, convolutive mixture.

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Authors: Hamed Qazanfari, Hamid Hassanpour, Kazem Qazanfari

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In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.

Keywords: Content-based image retrieval, color difference histogram, efficient features selection, entropy, correlation.

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Authors: J.Zouhair, D.Ellison

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This paper includes a review of three physics simulation packages that can be used to provide researchers with a virtual ground for modeling, implementing and simulating complex models, as well as testing their control methods with less cost and time of development. The inverted pendulum model was used as a test bed for comparing ODE, DANCE and Webots, while Linear State Feedback was used to control its behavior. The packages were compared with respect to model creation, solving systems of differential equation, data storage, setting system variables, control the experiment and ease of use. The purpose of this paper is to give an overview about our experience with these environments and to demonstrate some of the benefits and drawbacks involved in practice for each package.

Keywords: DANCE, Inverted Pendulum, ODE, Simulation Packages, Webots.

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Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco

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Neural networks offer an alternative approach both for identification and control of nonlinear processes in process engineering. The lack of software tools for the design of controllers based on neural network models is particularly pronounced in this field. SIMULINK is properly a widely used graphical code development environment which allows system-level developers to perform rapid prototyping and testing. Such graphical based programming environment involves block-based code development and offers a more intuitive approach to modeling and control task in a great variety of engineering disciplines. In this paper a SIMULINK based Neural Tool has been developed for analysis and design of multivariable neural based control systems. This tool has been applied to the control of a high purity distillation column including non linear hydrodynamic effects. The proposed control scheme offers an optimal response for both theoretical and practical challenges posed in process control task, in particular when both, the quality improvement of distillation products and the operation efficiency in economical terms are considered.

Keywords: Distillation, neural networks, software tools, identification, control.

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Authors: Han Xiao, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labelled samples and flexible network. We have conducted experiments on four UCI open datasets and achieved good results as expected. In conclusion, our model could handle more sparse labelled and more high-dimension dataset in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, Classifier.

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Authors: Sotiria Tzivinikou, Dimitra Kagkara

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Teacher’s sense of self-efficacy can affect significantly both teacher’s and student’s performance. More specific, self-efficacy is associated with the learning outcomes as well as student’s motivation and self-efficacy. For example, teachers with high sense of self-efficacy are more open to innovations and invest more effort in teaching. In addition to this, effective inclusive education is associated with higher levels of teacher’s self-efficacy. Pre-service teachers with high levels of self-efficacy could handle student’s behavior better and more effectively assist students with special educational needs. Teacher preparation programs are also important, because teacher’s efficacy beliefs are shaped early in learning, as a result the quality of teacher’s education programs can affect the sense of self-efficacy of pre-service teachers. Usually, a number of pre-service teachers do not consider themselves well prepared to work with students with special educational needs and do not have the appropriate sense of self-efficacy. This study aims to investigate the factors that contribute to the improvement of the sense of self-efficacy of pre-service special educators by using an academic practicum training program. The sample of this study is 159 pre-service special educators, who also participated in the academic practicum training program. For the purpose of this study were used quantitative methods for data collection and analysis. Teacher’s self-efficacy was assessed by the teachers themselves with the completion of a questionnaire which was based on the scale of Teacher’s Sense of Efficacy Scale. Pre and post measurements of teacher’s self-efficacy were taken. The results of the survey are consistent with those of the international literature. The results indicate that a significant number of pre-service special educators do not hold the appropriate sense of self-efficacy regarding teaching students with special educational needs. Moreover, a quality academic training program constitutes a crucial factor for the improvement of the sense of self-efficacy of pre-service special educators, as additional for the provision of high quality inclusive education.

Keywords: Inclusive education, pre-service, self-efficacy, training program.

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Authors: First G.M. Karthik, Second Ramachandra.V.Pujeri, Dr.

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Generalized Center String (GCS) problem are generalized from Common Approximate Substring problem and Common substring problems. GCS are known to be NP-hard allowing the problems lies in the explosion of potential candidates. Finding longest center string without concerning the sequence that may not contain any motifs is not known in advance in any particular biological gene process. GCS solved by frequent pattern-mining techniques and known to be fixed parameter tractable based on the fixed input sequence length and symbol set size. Efficient method known as Bpriori algorithms can solve GCS with reasonable time/space complexities. Bpriori 2 and Bpriori 3-2 algorithm are been proposed of any length and any positions of all their instances in input sequences. In this paper, we reduced the time/space complexity of Bpriori algorithm by Constrained Based Frequent Pattern mining (CBFP) technique which integrates the idea of Constraint Based Mining and FP-tree mining. CBFP mining technique solves the GCS problem works for all center string of any length, but also for the positions of all their mutated copies of input sequence. CBFP mining technique construct TRIE like with FP tree to represent the mutated copies of center string of any length, along with constraints to restraint growth of the consensus tree. The complexity analysis for Constrained Based FP mining technique and Bpriori algorithm is done based on the worst case and average case approach. Algorithm's correctness compared with the Bpriori algorithm using artificial data is shown.

Keywords: Constraint Based Mining, FP tree, Data mining, GCS problem, CBFP mining technique.

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Authors: Hadi Veisi, Mansour Jamzad

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In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation are evaluated and compared. In training and evaluation, each image block is assigned to a network based on its complexity value. Best-SNR is another alternative in selecting compressor network for image blocks in evolution phase which chooses one of the trained networks such that results best SNR in compressing the input image block. In our evaluations, best results are obtained when overlapping the blocks is allowed and choosing the networks in compressor is based on the Best-SNR. In this case, the results demonstrate superiority of this method comparing with previous similar works and JPEG standard coding.

Keywords: Adaptive image compression, Image complexity, Multi-layer perceptron neural network, JPEG Standard, PSNR.

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Authors: Dibyendu Ghoshal, Shimal Das

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Image compression based on fractal coding is a lossy compression method and normally used for gray level images range and domain blocks in rectangular shape. Fractal based digital image compression technique provide a large compression ratio and in this paper, it is proposed using YUV colour space and the fractal theory which is based on iterated transformation. Fractal geometry is mainly applied in the current study towards colour image compression coding. These colour images possesses correlations among the colour components and hence high compression ratio can be achieved by exploiting all these redundancies. The proposed method utilises the self-similarity in the colour image as well as the cross-correlations between them. Experimental results show that the greater compression ratio can be achieved with large domain blocks but more trade off in image quality is good to acceptable at less than 1 bit per pixel.

Keywords: Fractal coding, Iterated Function System (IFS), Image compression, YUV colour space.

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Authors: S. Dikmen Kucuk, A. Tozluoglu, Y. Guner

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In recent years, petroleum-based polymers began to be limited due to effects on human and environmental point of view in many countries. Thus, organic-based biodegradable materials have attracted much interest in the composite industry because of environmental concerns. As a result of this, it has been asked that inorganic and petroleum-based materials should be reduced and altered with biodegradable materials. In this point, in this study, it is aimed to investigate the potential of use of TEMPO (2,2,6,6- tetramethylpiperidine 1-oxyl)-mediated oxidation nano-fibrillated cellulose instead of EPDM (ethylene-propylene-diene monomer) rubber, which is a petroleum-based material. Thus, the exchange of petroleum-based EPDM rubber with organic based cellulose nanofibers, which are environmentally friendly (green) and biodegradable, will be realized. The effect of tempo-oxidized cellulose nanofibers (TCNF) instead of EPDM rubber was analyzed by rheological, mechanical, chemical, thermal and aging analyses. The aged surfaces were visually scrutinized and surface morphological changes were examined via scanning electron microscopy (SEM). The results obtained showed that TEMPO oxidation nano-fibrillated cellulose can be used at an amount of 1.0 and 2.2 phr resulting the values stay within tolerance according to customer standard and without any chemical degradation, crack, colour change or staining.

Keywords: EPDM, cellulose, green materials, nanofibrillated cellulose, TCNF, tempo-oxidized nanofiber.

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Authors: Arafet Bouaicha, Hatem Allegui, Amar Rouane, El-Hassane Aglzim, Abdelkader Mami

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In order to further understand the behavior of PEM fuel cell and optimize their performance, it is necessary to perform measurements in real time. The internal impedance measurement by electrochemical impedance spectroscopy (EIS) is of great importance. In this work, we present the impedance measurement method of a PEM fuel cell by electrochemical impedance spectroscopy method and the realization steps of electronic load for this measuring technique implementation. The theoretical results are obtained from the simulation of software PSPICE® and experimental tests are carried out using the Ballard Nexa™ PEM fuel cell system.

Keywords: Electronic load, MOS transistor, PEM fuel cell, Impedance measurement, Electrochemical Impedance Spectroscopy (EIS).

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Authors: Tibor Kulcsar, Gabor Sarossy, Gabor Bereznai, Robert Auer, Janos Abonyi

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On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.

Keywords: indexing high dimensional databases, dimensional reduction, clustering, similarity, k-nn algorithm.

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Authors: Zhongjie Wang, Xiaofei Xu, Dechen Zhan

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With deep development of software reuse, componentrelated technologies have been widely applied in the development of large-scale complex applications. Component identification (CI) is one of the primary research problems in software reuse, by analyzing domain business models to get a set of business components with high reuse value and good reuse performance to support effective reuse. Based on the concept and classification of CI, its technical stack is briefly discussed from four views, i.e., form of input business models, identification goals, identification strategies, and identification process. Then various CI methods presented in literatures are classified into four types, i.e., domain analysis based methods, cohesion-coupling based clustering methods, CRUD matrix based methods, and other methods, with the comparisons between these methods for their advantages and disadvantages. Additionally, some insufficiencies of study on CI are discussed, and the causes are explained subsequently. Finally, it is concluded with some significantly promising tendency about research on this problem.

Keywords: Business component, component granularity, component identification, reuse performance.

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Authors: Vitomir Struc, Nikola Pavesic

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Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in "real-life" operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.

Keywords: Biometrics, face recognition, appearance based methods, image degradations, the XM2VTS database.

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Authors: M. Anis-ur- Rehman, A. Abdullah, Mariam Ansari , Zeb-un-Nisa, M. S. Awan

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Samples of CoFe2-xCrxO4 where x varies from 0.0 to 0.5 were prepared by co-precipitation route. These samples were sintered at 750°C for 2 hours. These particles were characterized by X-ray diffraction (XRD) at room temperature. The FCC spinel structure was confirmed by XRD patterns of the samples. The crystallite sizes of these particles were calculated from the most intense peak by Scherrer formula. The crystallite sizes lie in the range of 37-60 nm. The lattice parameter was found decreasing upon substitution of Cr. DC electrical resistivity was measured as a function of temperature. The room temperature thermoelectric power was measured for the prepared samples. The magnitude of Seebeck coefficient depends on the composition and resistivity of the samples.

Keywords: Ferrites, crystallite size, drift mobility, seebeck coefficient, thermopower.

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Authors: Binyamin Yusoff, Imran Taib, Lazim Abdullah, Abd Fatah Wahab

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Intuitionistic fuzzy sets as proposed by Atanassov, have gained much attention from past and latter researchers for applications in various fields. Similarity measures between intuitionistic fuzzy sets were developed afterwards. However, it does not cater the conflicting behavior of each element evaluated. We therefore made some modification to the similarity measure of IFS by considering conflicting concept to the model. In this paper, we concentrate on Zhang and Fu-s similarity measures for IFSs and some examples are given to validate these similarity measures. A simple modification to Zhang and Fu-s similarity measures of IFSs was proposed to find the best result according to the use of degree of indeterminacy. Finally, we mark up with the application to real decision making problems.

Keywords: Intuitionistic fuzzy sets, similarity measures, multicriteriadecision making.

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Authors: Matthias Dehmer, Frank Emmert Streib, Alexander Mehler, Jürgen Kilian

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Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

Keywords: Graph similarity, hierarchical and directed graphs, hypertext, generalized trees, web structure mining.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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Authors: Mohammad J. Heydari, Farnaz Derakhshan

Abstract:

In this paper, we focus on the problem of driving and herding a collection of autonomous actors to a given area. Then, a new method based on multi-agent coordination is proposed for solving the problem. In our proposed method, we assume that the environment is covered by sensors. When an event is occurred, sensors forward information to a sink node. Based on received information, the sink node will estimate the direction and the speed of movement of actors and announce the obtained value to the actors. The actors coordinate to reach the target location.

Keywords: Coordination, Environmental Inspection, Multiagent systems, Wireless Sensor and Actor Networks (WSANs)

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Authors: Borna Jafarpour, Azadeh Nematzadeh, Vahid Kazempour, Babak Sadeghian

Abstract:

Cellular automata have been used for design of cryptosystems. Recently some secret sharing schemes based on linear memory cellular automata have been introduced which are used for both text and image. In this paper, we illustrate that these secret sharing schemes are vulnerable to dishonest participants- collusion. We propose a cheating model for the secret sharing schemes based on linear memory cellular automata. For this purpose we present a novel uniform model for representation of all secret sharing schemes based on cellular automata. Participants can cheat by means of sending bogus shares or bogus transition rules. Cheaters can cooperate to corrupt a shared secret and compute a cheating value added to it. Honest participants are not aware of cheating and suppose the incorrect secret as the valid one. We prove that cheaters can recover valid secret by removing the cheating value form the corrupted secret. We provide methods of calculating the cheating value.

Keywords: Cellular automata, cheating model, secret sharing, threshold scheme.

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Authors: Yao-Hong Tsai

Abstract:

Salient points are frequently used to represent local properties of the image in content-based image retrieval. In this paper, we present a reduction algorithm that extracts the local most salient points such that they not only give a satisfying representation of an image, but also make the image retrieval process efficiently. This algorithm recursively reduces the continuous point set by their corresponding saliency values under a top-down approach. The resulting salient points are evaluated with an image retrieval system using Hausdoff distance. In this experiment, it shows that our method is robust and the extracted salient points provide better retrieval performance comparing with other point detectors.

Keywords: Barnard detector, Content-based image retrieval, Points reduction, Salient point.

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