Search results for: temporal sequences.

Authors: Mohammad N. Alanazi, David A. Gustafson

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In this article, we introduce a new approach for analyzing UML designs to detect the inconsistencies between multiple state diagrams and sequence diagrams. The Super State Analysis (SSA) identifies the inconsistencies in super states, single step transitions, and sequences. Because SSA considers multiple UML state diagrams, it discovers inconsistencies that cannot be discovered when considering only a single UML state diagram. We have introduced a transition set that captures relationship information that is not specifiable in UML diagrams. The SSA model uses the transition set to link transitions of multiple state diagrams together. The analysis generates three different sets automatically. These sets are compared to the provided sets to detect the inconsistencies. SSA identifies five types of inconsistencies: impossible super states, unreachable super states, illegal transitions, missing transitions, and illegal sequences.

Keywords: Modeling Languages, Object-Oriented Analysis, Sequence Diagrams, Software Models, State Diagrams, UML.

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: HTM, Real time anomaly detection, ECG, Cardiac Anomalies.

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Authors: Feng-Yi Huang, Yuan-Jye Tseng

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In the traditional concept of product life cycle management, the activities of design, manufacturing, and assembly are performed in a sequential way. The drawback is that the considerations in design may contradict the considerations in manufacturing and assembly. The different designs of components can lead to different assembly sequences. Therefore, in some cases, a good design may result in a high cost in the downstream assembly activities. In this research, an integrated design evaluation and assembly sequence planning model is presented. Given a product requirement, there may be several design alternative cases to design the components for the same product. If a different design case is selected, the assembly sequence for constructing the product can be different. In this paper, first, the designed components are represented by using graph based models. The graph based models are transformed to assembly precedence constraints and assembly costs. A particle swarm optimization (PSO) approach is presented by encoding a particle using a position matrix defined by the design cases and the assembly sequences. The PSO algorithm simultaneously performs design evaluation and assembly sequence planning with an objective of minimizing the total assembly costs. As a result, the design cases and the assembly sequences can both be optimized. The main contribution lies in the new concept of integrated design evaluation and assembly sequence planning model and the new PSO solution method. The test results show that the presented method is feasible and efficient for solving the integrated design evaluation and assembly planning problem. In this paper, an example product is tested and illustrated.

Keywords: assembly sequence planning, design evaluation, design for assembly, particle swarm optimization

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Authors: Natarajan Vijayarangan, Prasanna S. Bidare

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Cryptography, Image watermarking and E-banking are filled with apparent oxymora and paradoxes. Random sequences are used as keys to encrypt information to be used as watermark during embedding the watermark and also to extract the watermark during detection. Also, the keys are very much utilized for 24x7x365 banking operations. Therefore a deterministic random sequence is very much useful for online applications. In order to obtain the same random sequence, we need to supply the same seed to the generator. Many researchers have used Deterministic Random Number Generators (DRNGs) for cryptographic applications and Pseudo Noise Random sequences (PNs) for watermarking. Even though, there are some weaknesses in PN due to attacks, the research community used it mostly in digital watermarking. On the other hand, DRNGs have not been widely used in online watermarking due to its computational complexity and non-robustness. Therefore, we have invented a new design of generating DRNG using Pi-series to make it useful for online Cryptographic, Digital watermarking and Banking applications.

Keywords: E-tokens, LFSR, non-linear, Pi series, pseudo random number.

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Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu

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Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.

Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Aissa Saoudi, Hassane Essafi

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In this work we will present a new approach for shot transition auto-detection. Our approach is based on the analysis of Spatio-Temporal Video Slice (STVS) edges extracted from videos. The proposed approach is capable to efficiently detect both abrupt shot transitions 'cuts' and gradual ones such as fade-in, fade-out and dissolve. Compared to other techniques, our method is distinguished by its high level of precision and speed. Those performances are obtained due to minimizing the problem of the boundary shot detection to a simple 2D image partitioning problem.

Keywords: Boundary shot detection, Shot transition detection, Video analysis, Video indexing.

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Authors: Daria N. Podvigina, Tatiana V. Chernigovskaya

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We have studied the temporal characteristics of bistable perception of the stimuli of two types: one involves alterations in a perceived depth and another one has an ambiguous content. We used the Necker lattice and lines of shadowed circles ambiguously perceived either as spheres or holes as stimuli of the first type. The Winson figure (the Eskimo/Indian picture) was a stimulus of the second type. We have analyzed how often the reversals occurred (reversal rate) and for how long each of the two interpretations, or percepts, was observed during one presentation (stability durations). For all three ambiguous images the reversal rate and the stability durations had similar values, which provide another evidence for a significant role of top-down processes in multistable perception.

Keywords: Multistable perception, perceived depth, reversal rate, top-down processes.

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Authors: Jafar Ahmadi, Zhohreh Asiaban, Sedigheh Fabriki Ourang

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Brassinosteroids (BRs) regulate cell elongation, vascular differentiation, senescence, and stress responses. BRs signal through the BES1/BZR1 family of transcription factors, which regulate hundreds of target genes involved in this pathway. In this research a comprehensive genome-wide analysis was carried out in BES1/BZR1 gene family in Arabidopsis thaliana, Cucumis sativus, Vitis vinifera, Glycin max and Brachypodium distachyon. Specifications of the desired sequences, dot plot and hydropathy plot were analyzed in the protein and genome sequences of five plant species. The maximum amino acid length was attributed to protein sequence Brdic3g with 374aa and the minimum amino acid length was attributed to protein sequence Gm7g with 163aa. The maximum Instability index was attributed to protein sequence AT1G19350 equal with 79.99 and the minimum Instability index was attributed to protein sequence Gm5g equal with 33.22. Aliphatic index of these protein sequences ranged from 47.82 to 78.79 in Arabidopsis thaliana, 49.91 to 57.50 in Vitis vinifera, 55.09 to 82.43 in Glycin max, 54.09 to 54.28 in Brachypodium distachyon 55.36 to 56.83 in Cucumis sativus. Overall, data obtained from our investigation contributes a better understanding of the complexity of the BES1/BZR1 gene family and provides the first step towards directing future experimental designs to perform systematic analysis of the functions of the BES1/BZR1 gene family.

Keywords: BES1/BZR1, Brassinosteroids, Phylogenetic analysis, Transcription factor.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias

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Gene Ontology terms have been actively used to annotate various protein sets. SWISS-PROT, TrEMBL, and InterPro are protein databases that are annotated according to the Gene Ontology terms. However, direct implementation of the Gene Ontology terms for annotation of anonymous protein sequences is not easy, especially for species not commonly represented in biological databases. UTMGO is developed as a tool that allows the user to quickly and easily search for a group of semantically related Gene Ontology terms. The applicability of the UTMGO is demonstrated by applying it to annotation of anonymous protein sequence. The extended UTMGO uses the Gene Ontology terms together with protein sequences associated with the terms to perform the annotation task. GOPET, GOtcha, GoFigure, and JAFA are used to compare the performance of the extended UTMGO.

Keywords: Anonymous protein sequence, Gene Ontology, Protein sequence annotation, Protein sequence alignment

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Authors: Rosalyn R. Porle, Ali Chekima, Farrah Wong, G. Sainarayanan

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Arms detection is one of the fundamental problems in human motion analysis application. The arms are considered as the most challenging body part to be detected since its pose and speed varies in image sequences. Moreover, the arms are usually occluded with other body parts such as the head and torso. In this paper, histogram-based skin colour segmentation is proposed to detect the arms in image sequences. Six different colour spaces namely RGB, rgb, HSI, TSL, SCT and CIELAB are evaluated to determine the best colour space for this segmentation procedure. The evaluation is divided into three categories, which are single colour component, colour without luminance and colour with luminance. The performance is measured using True Positive (TP) and True Negative (TN) on 250 images with manual ground truth. The best colour is selected based on the highest TN value followed by the highest TP value.

Keywords: image colour analysis, image motion analysis, skin, wavelet transform.

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Authors: Pedro Pablo González Pérez, Hiram I. Beltrán, Arturo Rojo-Domínguez, Máximo EduardoSánchez Gutiérrez

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Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.

Keywords: multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

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In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: Mahmoud Elmezain, Ayoub Al-Hamadi, Robert Niese, Bernd Michaelis

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Real-time hand tracking is a challenging task in many computer vision applications such as gesture recognition. This paper proposes a robust method for hand tracking in a complex environment using Mean-shift analysis and Kalman filter in conjunction with 3D depth map. The depth information solve the overlapping problem between hands and face, which is obtained by passive stereo measuring based on cross correlation and the known calibration data of the cameras. Mean-shift analysis uses the gradient of Bhattacharyya coefficient as a similarity function to derive the candidate of the hand that is most similar to a given hand target model. And then, Kalman filter is used to estimate the position of the hand target. The results of hand tracking, tested on various video sequences, are robust to changes in shape as well as partial occlusion.

Keywords: Computer Vision and Image Analysis, Object Tracking, Gesture Recognition.

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Authors: Michal Mardiak, Jaroslav Polec

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In this paper we proposed comparison of four content based objective metrics with results of subjective tests from 80 video sequences. We also include two objective metrics VQM and SSIM to our comparison to serve as “reference” objective metrics because their pros and cons have already been published. Each of the video sequence was preprocessed by the region recognition algorithm and then the particular objective video quality metric were calculated i.e. mutual information, angular distance, moment of angle and normalized cross-correlation measure. The Pearson coefficient was calculated to express metrics relationship to accuracy of the model and the Spearman rank order correlation coefficient to represent the metrics relationship to monotonicity. The results show that model with the mutual information as objective metric provides best result and it is suitable for evaluating quality of video sequences.

Keywords: Objective quality metrics, mutual information, region recognition, content based metrics

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Authors: Farhad Asadi, Hossein Sadati

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In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Hung Tuan Nguyen

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In this paper, with the purpose of further reducing the complexity of the system, while keeping its temporal and spatial focusing performance, we investigate the possibility of using optimal one bit time reversal (TR) system for impulse radio ultra wideband multi-user wireless communications. The results show that, by optimally selecting the number of used taps in the pre-filter the optimal one bit TR system can outperform the full one bit TR system. In some cases, the temporal and spatial focusing performance of the optimal one bit TR system appears to be compatible with that of the original TR system. This is a significant result as the overhead cost is much lower than it is required in the original TR system.

Keywords: Time reversal, optimal one bit, UWB, multi-user interference, inter symbol interference

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Authors: Nadezhda M. Usmanova, Nikolai V. Tomilin

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Mammalian genomes contain large number of retroelements (SINEs, LINEs and LTRs) which could affect expression of protein coding genes through associated transcription factor binding sites (TFBS). Activity of the retroelement-associated TFBS in many genes is confirmed experimentally but their global functional impact remains unclear. Human SINEs (Alu repeats) and mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich gene rich genome segments consistent with the view that they can contribute to regulation of gene expression. We have shown earlier that Alu are involved in formation of cis-regulatory modules (clusters of TFBS) in human promoters, and other authors reported that Alu located near promoter CpG islands have an increased frequency of CpG dinucleotides suggesting that these Alu are undermethylated. Human Alu and mouse B1/B2 elements have an internal bipartite promoter for RNA polymerase III containing conserved sequence motif called B-box which can bind basal transcription complex TFIIIC. It has been recently shown that TFIIIC binding to B-box leads to formation of a boundary which limits spread of repressive chromatin modifications in S. pombe. SINEassociated B-boxes may have similar function but conservation of TFIIIC binding sites in SINEs located near mammalian promoters has not been studied earlier. Here we analysed abundance and distribution of retroelements (SINEs, LINEs and LTRs) in annotated sequences of the Database of mammalian transcription start sites (DBTSS). Fractions of SINEs in human and mouse promoters are slightly lower than in all genome but >40% of human and mouse promoters contain Alu or B1/B2 elements within -1000 to +200 bp interval relative to transcription start site (TSS). Most of these SINEs is associated with distal segments of promoters (-1000 to -200 bp relative to TSS) indicating that their insertion at distances >200 bp upstream of TSS is tolerated during evolution. Distribution of SINEs in promoters correlates negatively with the distribution of CpG sequences. Using analysis of abundance of 12-mer motifs from the B1 and Alu consensus sequences in genome and DBTSS it has been confirmed that some subsegments of Alu and B1 elements are poorly conserved which depends in part on the presence of CpG dinucleotides. One of these CpG-containing subsegments in B1 elements overlaps with SINE-associated B-box and it shows better conservation in DBTSS compared to genomic sequences. It has been also studied conservation in DBTSS and genome of the B-box containing segments of old (AluJ, AluS) and young (AluY) Alu repeats and found that CpG sequence of the B-box of old Alu is better conserved in DBTSS than in genome. This indicates that Bbox- associated CpGs in promoters are better protected from methylation and mutation than B-box-associated CpGs in genomic SINEs. These results are consistent with the view that potential TFIIIC binding motifs in SINEs associated with human and mouse promoters may be functionally important. These motifs may protect promoters from repressive histone modifications which spread from adjacent sequences. This can potentially explain well known clustering of SINEs in GC-rich gene rich genome compartments and existence of unmethylated CpG islands.

Keywords: Retroelement, promoter, CpG island, DNAmethylation.

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Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici

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Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.

Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.

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Authors: Mallikarjun B. Channappagoudar, Pallapa Venkataram

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As a node monitoring protocol, which is a part of network management, operates in distributed manner, conformance testing of such protocols is more tedious than testing a peer-to-peer protocol. Various works carried out to give the methodology to do conformance testing of distributed protocol. In this paper, we have presented a formal approach for conformance testing of a Node Monitoring Protocol, which uses both static and mobile agents, for MANETs. First, we use SDL to obtain MSCs, which represent the scenario descriptions by sequence diagrams, which in turn generate test sequences and test cases. Later, Testing and Test Control Notation Version-3 (TTCN-3) is used to execute test cases with respect to generated test sequences to know the conformance of protocol against the given specification. This approach shows, the effective conformance testing of the distributed protocols for the network with varying node density and complex behavior. Experimental results for the protocol scenario represent the effectiveness of the method used.

Keywords: Conformance Testing, FSM, Mobile agent, TTCN, Test sequence.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias

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Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.

Keywords: automatic clustering, bioinformatics tool, gene ontology, protein sequence annotation, semantic similarity search

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Authors: T. Holden, G. Tremberger, Jr, E. Cheung, R. Subramaniam, R. Sullivan, N. Gadura, P. Schneider, P. Marchese, A. Flamholz, T. Cheung, D. Lieberman

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A nucleotide sequence can be expressed as a numerical sequence when each nucleotide is assigned its proton number. A resulting gene numerical sequence can be investigated for its fractal dimension in terms of evolution and chemical properties for comparative studies. We have investigated such nucleotide fluctuation in the 16S rRNA gene of archaea thermophiles. The studied archaea thermophiles were archaeoglobus fulgidus, methanothermobacter thermautotrophicus, methanocaldococcus jannaschii, pyrococcus horikoshii, and thermoplasma acidophilum. The studied five archaea-euryarchaeota thermophiles have fractal dimension values ranging from 1.93 to 1.97. Computer simulation shows that random sequences would have an average of about 2 with a standard deviation about 0.015. The fractal dimension was found to correlate (negative correlation) with the thermophile-s optimal growth temperature with R2 value of 0.90 (N =5). The inclusion of two aracheae-crenarchaeota thermophiles reduces the R2 value to 0.66 (N = 7). Further inclusion of two bacterial thermophiles reduces the R2 value to 0.50 (N =9). The fractal dimension is correlated (positive) to the sequence GC content with an R2 value of 0.89 for the five archaea-euryarchaeota thermophiles (and 0.74 for the entire set of N = 9), although computer simulation shows little correlation. The highest correlation (positive) was found to be between the fractal dimension and di-nucleotide Shannon entropy. However Shannon entropy and sequence GC content were observed to correlate with optimal growth temperature having an R2 of 0.8 (negative), and 0.88 (positive), respectively, for the entire set of 9 thermophiles; thus the correlation lacks species specificity. Together with another correlation study of bacterial radiation dosage with RecA repair gene sequence fractal dimension, it is postulated that fractal dimension analysis is a sensitive tool for studying the relationship between genotype and phenotype among closely related sequences.

Keywords: Fractal dimension, archaea thermophiles, Shannon entropy, GC content

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Authors: Maryam Vafadar, Alireza Behrad, Saeed Akbari

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Nowadays, with the emerging of the new applications like robot control in image processing, artificial vision for visual servoing is a rapidly growing discipline and Human-machine interaction plays a significant role for controlling the robot. This paper presents a new algorithm based on spatio-temporal volumes for visual servoing aims to control robots. In this algorithm, after applying necessary pre-processing on video frames, a spatio-temporal volume is constructed for each gesture and feature vector is extracted. These volumes are then analyzed for matching in two consecutive stages. For hand gesture recognition and classification we tested different classifiers including k-Nearest neighbor, learning vector quantization and back propagation neural networks. We tested the proposed algorithm with the collected data set and results showed the correct gesture recognition rate of 99.58 percent. We also tested the algorithm with noisy images and algorithm showed the correct recognition rate of 97.92 percent in noisy images.

Keywords: Back propagation neural network, Feature vector, Hand gesture recognition, k-Nearest Neighbor, Learning vector quantization neural network, Robot control, Spatio-temporal volume, Visual servoing

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Authors: Suguru Yoshioka, Satoshi Tojo

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This paper introduces a temporal epistemic logic CBCTL that updates agent-s belief states through communications in them, based on computational tree logic (CTL). In practical environments, communication channels between agents may not be secure, and in bad cases agents might suffer blackouts. In this study, we provide inform* protocol based on ACL of FIPA, and declare the presence of secure channels between two agents, dependent on time. Thus, the belief state of each agent is updated along with the progress of time. We show a prover, that is a reasoning system for a given formula in a given a situation of an agent ; if it is directly provable or if it could be validated through the chains of communications, the system returns the proof.

Keywords: communication channel, computational tree logic, reasoning system, temporal epistemic logic.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: Metagenomics, phenotype prediction, deep learning, embeddings, multiple instance learning.

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Authors: Salam Khalifa, Naveed Ahmed

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We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignement method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data.

Keywords: 3D video, 3D animation, RGB-D video, Temporally Coherent 3D Animation.

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Authors: Rajdeep Niyogi

Abstract:

The objective of the paper is twofold. First, to develop a formal framework for planning for mobile agents. A logical language based on a temporal logic is proposed that can express a type of tasks which often arise in network management. Second, to design a planning algorithm for such tasks. The aim of this paper is to study the importance of finding plans for mobile agents. Although there has been a lot of research in mobile agents, not much work has been done to incorporate planning ideas for such agents. This paper makes an attempt in this direction. A theoretical study of finding plans for mobile agents is undertaken. A planning algorithm (based on the paradigm of mobile computing) is proposed and its space, time, and communication complexity is analyzed. The algorithm is illustrated by working out an example in detail.

Keywords: Acting, computer network, mobile agent, mobile computing, planning, temporal logic.

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Authors: Elena Carcano, Mirzi Betasolo

Abstract:

This study aims to provide sub-hourly streamflow predictions and associated rating curves for small catchments of intermittent and torrential flow regime characterized by flash floods occurring especially during April and November. The methodology entails two lumped conceptual hydrological models which work in series. The total model is based upon eleven parameters and shows good flexibility in handling different input sets. Runoff Coefficient has contributed to improving the model’s performances and has been treated as an additional parameter; while Sensitivity Analysis has highlighted how slight changes in the model’s input can lead to changes in model’s output. The adopted procedure is steady and useful to give very practical engineering information at the expense of a parsimonious request both in input data and in the number of adopted parameters. According to the obtained results, the authors encourage the test of this combined procedure on different hydrological scenarios in order to provide information for poorly monitored catchments and not updated sites.

Keywords: Streamflow rating curve, chronological data, streamflow sequences, conceptual models.

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Authors: S. Makal, L. Ozyilmaz, S. Palavaroglu

Abstract:

Gene, principal unit of inheritance, is an ordered sequence of nucleotides. The genes of eukaryotic organisms include alternating segments of exons and introns. The region of Deoxyribonucleic acid (DNA) within a gene containing instructions for coding a protein is called exon. On the other hand, non-coding regions called introns are another part of DNA that regulates gene expression by removing from the messenger Ribonucleic acid (RNA) in a splicing process. This paper proposes to determine splice junctions that are exon-intron boundaries by analyzing DNA sequences. A splice junction can be either exon-intron (EI) or intron exon (IE). Because of the popularity and compatibility of the artificial neural network (ANN) in genetic fields; various ANN models are applied in this research. Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression Neural Networks (GRNN) are used to analyze and detect the splice junctions of gene sequences. 10-fold cross validation is used to demonstrate the accuracy of networks. The real performances of these networks are found by applying Receiver Operating Characteristic (ROC) analysis.

Keywords: Gene, neural networks, ROC analysis, splice junctions.

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Authors: Ibrahim Ozkan

Abstract:

In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: Cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading.

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