title: An extended Cellular Potts Model analyzing a wound healing assay

abstract: A suitable Cellular Potts Model is developed to reproduce and analyze an in vitro wound-healing assay. The proposed approach is able both to quantify the invasive capacity of the overall cell population and to evaluate selected determinants of single cell movement (velocity, directional movement, and final displacement). In this respect, the present CPM allows us to capture differences and correlations in the migratory behavior of cells initially located at different distances from the wound edge. In the case of an undifferentiated extracellular matrix, the model then predicts that a maximal healing can be obtained by a chemically induced increment of cell elasticity and not by a chemically induced downregulation of intercellular adhesive contacts. Moreover, in the case of two-component substrates (formed by a mesh of collagenous-like threads and by a homogeneous medium), CPM simulations show that both fiber number and cell-fiber adhesiveness influence cell speed and wound closure rate in a biphasic fashion. On the contrary, the topology of the fibrous network affects the healing process by mediating the productive directional cell movement. The paper, also equipped with comments on the computational cost of the CPM algorithm, ends with a throughout discussion of the pertinent experimental and theoretical literature. HighlightsA wound healing assay is modeled by a suitable version of the Cellular Potts Model.Cells are represented as compartmentalized elements.Phenotypic differentiations occur among cells sited in distinct areas of the mass.The invasiveness of the culture depends on cell elasticity and adhesiveness.The healing process depends on topological properties of matrix fibrous component.


title: Design and Analysis of Studies with Binary- Event Composite Endpoints

abstract: Composite endpoints consisting of several binary events, such as distinct perioperative complications, are frequently chosen as the primary outcome in anesthesia studies (and in many other clinical specialties) because (1) no single outcome fully characterizes the disease or outcome of interest, and


title: Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

abstract: Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional re- quirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but its application has been limited by the difficulty of solving large, real-world problems within a reasonable time. To overcome this limitation of MIP, we applied the ant colony optimization (ACO) metaheuristic to develop an ACO-based heuristic algorithm that efficiently solves large and complex forest transportation problems with side constraints. Three hypothetical FTPP were created to test the performance of the ACO algorithm. The environmental impact of forest roads represented by sediment yields was incorporated into the economic analysis of roads as a side con- straint. Four different levels of sediment constraints were analyzed for each problem. The solutions from the ACO algo- rithm were compared with those obtained from a commercially available MIP solver. The ACO solutions were equal to or slightly worse than the MIP solution, but the ACO algorithm took only a fraction of the computation time that was re- quired by the MIP solver.


title: Millimeter-wave frequency reconfigurable T-shaped antenna for 5G networks

abstract: Millimeter-wave reconfigurable antennas are predicted as a future of next generation wireless networks with the availability of wide bandwidth. A coplanar waveguide (CPW) fed T-shaped frequency reconfigurable millimeter-wave antenna for 5G networks is presented. The resonant frequency is varied to obtain the 10dB return loss bandwidth in the frequency range of 23–29GHz by incorporating two variable resistors. The radiation pattern contributes two symmetrical radiation beams at approximately ±30o along the end fire direction. The 3dB beamwidth remains conserved over the entire range of operating bandwidth. The proposed antenna targets the applications of wireless systems operating in narrow passages, corridors, mine tunnels, and person-to-person body centric applications.


title: Brand Concepts as Representations of Human Values: Do Cultural Congruity and Compatibility Between Values Matter?

abstract: Global brands are faced with the challenge of conveying concepts that not only are consistent across borders but also resonate with consumers of different cultures. Building on prior research indicating that abstract brand concepts induce more favorable consumer responses than functional attributes, the authors introduce a generalizable and robust structure of abstract brand concepts as representations of human values. Using three empirical studies conducted with respondents from eight countries, they demonstrate that this proposed structure is particularly useful for predicting (1) brand meanings that are compatible (vs. incompatible) with each other and, consequently, more (less) favorably accepted by consumers when added to an already established brand concept; (2) brand concepts that are more likely to resonate with consumers with differing cultural orientations; and (3) consumers’ responses to attempts to imbue an established brand concept with new, (in)compatible abstract meanings as a function of their own cultural orientations.


title: A Framework for Embedding Analytics in a Business Process

abstract: Competition and cost pressures are creating the need for enterprises to innovate business processes to create new business value, increase operational efficiency, and detect and respond to organizational real time challenges. In order to achieve this, in the past, most organisations have automated their manual processes using business process management technologies and enterprise systems. However, going forward, in order to stay competitive, organizations have to go beyond automating their processes, by making them more intelligent, by embedding analytics into their processes and business applications. In this paper, we propose "Analytics Driven Business Process Improvement (ADBPI) Framework" that provides a structure for how an organization can examine its current business process and enhance this process by embedding analytics within the process.


Follow the Profit or the Herd? Exploring Social Effects in Peer-to-Peer Lending


Communication and health in a multiethnic society


title: Laplacian Patch-Based Image Synthesis

abstract: Patch-based image synthesis has been enriched with global optimization on the image pyramid. Successively, the gradient-based synthesis has improved structural coherence and details. However, the gradient operator is directional and inconsistent and requires computing multiple operators. It also introduces a significantly heavy computational burden to solve the Poisson equation that often accompanies artifacts in non-integrable gradient fields. In this paper, we propose a patch-based synthesis using a Laplacian pyramid to improve searching correspondence with enhanced awareness of edge structures. Contrary to the gradient operators, the Laplacian pyramid has the advantage of being isotropic in detecting changes to provide more consistent performance in decomposing the base structure and the detailed localization. Furthermore, it does not require heavy computation as it employs approximation by the differences of Gaussians. We examine the potentials of the Laplacian pyramid for enhanced edge-aware correspondence search. We demonstrate the effectiveness of the Laplacian-based approach over the state-of-the-art patchbased image synthesis methods.


title: Morphological awareness in developmental dyslexia

abstract: This study examines morphological awareness in developmental dyslexia. While the poor phonological awareness of dyslexic children has been related to their difficulty in handling the alphabetical principle, less is known about their morphological awareness, which also plays an important part in reading development. The aim of this study was to analyze in more detail the implications of the phonological impairments of dyslexics in dealing with larger units of language such as morphemes. First, the performance of dyslexic children in a series of morphological tasks was compared with the performance of children matched on reading-level and chronological age. In all the tasks, the dyslexic group performed below the chronological age control group, suggesting that morphological awareness cannot be developed entirely independently of reading experience and/or phonological skills. Comparisons with the reading-age control group indicated that, while the dyslexic children were poorer in the morphemic segmentation tasks, they performed normally for their reading level in the sentence completion tasks. Furthermore, they produced more derived words in the production task. This suggests that phonological impairments prevent the explicit segmentation of affixes while allowing the development of productive morphological knowledge. A second study compared dyslexic subgroups defined by their degree of phonological impairment. Our results suggest that dyslexics develop a certain type of morphological knowledge, which they use as a compensatory reading strategy.


title: SCALE-Sim: Systolic CNN Accelerator

abstract: Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications. However, the research community lacks tools to insights on both the design trade-offs and efficient mapping strategies for systolic-array based accelerators. We introduce Systolic CNN Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based cycle accurate DNN accelerator simulator. SCALE-Sim exposes various micro-architectural features as well as system integration parameters to the designer to enable comprehensive design space exploration. This is the first systolic-array simulator tuned for running DNNs to the best of our knowledge. Using SCALE-Sim, we conduct a suite of case studies and demonstrate the effect of bandwidth, data flow and aspect ratio on the overall runtime and energy of Deep Learning kernels across vision, speech, text, and games. We believe that these insights will be highly beneficial to architects and ML practitioners.


Paragraph Vector based Topic Model for Language Model Adaptation


title: Improving agile requirements: the Quality User Story framework and tool

abstract: User stories are a widely adopted requirements notation in agile development. Yet, user stories are too often poorly written in practice and exhibit inherent quality defects. Triggered by this observation, we propose the Quality User Story (QUS) framework, a set of 13 quality criteria that user story writers should strive to conform to. Based on QUS, we present the Automatic Quality User Story Artisan (AQUSA) software tool. Relying on natural language processing (NLP) techniques, AQUSA detects quality defects and suggest possible remedies. We describe the architecture of AQUSA, its implementation, and we report on an evaluation that analyzes 1023 user stories obtained from 18 software companies. Our tool does not yet reach the ambitious 100 % recall that Daniel Berry and colleagues require NLP tools for RE to achieve. However, we obtain promising results and we identify some improvements that will substantially improve recall and precision.


title: Voxel-based morphometry—The methods

abstract: At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.


title: Distilling Word Embeddings: An Encoding Approach

abstract: Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, so that we can reduce model complexity by a large margin as well as retain high accuracy, achieving a good compromise between efficiency and performance. Experiments reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.


title: Blind Format String Attacks

abstract: Although Format String Attacks (FSAs) are known for many years there is still a number of applications that have been found to be vulnerable to such attacks in the recent years. According to the CVE database, the number of FSA vulnerabilities is stable over the last 5 years, even as FSA vulnerabilities are assumingly easy to detect. Thus we can assume, that this type of bugs will still be present in future. Current compiler-based or system-based protection mechanisms are helping to restrict the exploitation this kind of vulnerabilities, but are insufficient to circumvent an attack in all cases.


Information About Low Participation in Cancer Screening Demotivates Other People


title: Residential green spaces and mortality: A systematic review.

abstract: BACKGROUND ::: A number of studies have associated natural outdoor environments with reduced mortality but there is no systematic review synthesizing the evidence. ::: ::: ::: OBJECTIVES ::: We aimed to systematically review the available evidence on the association between long-term exposure to residential green and blue spaces and mortality in adults, and make recommendations for further research. As a secondary aim, we also conducted meta-analyses to explore the magnitude of and heterogeneity in the risk estimates. ::: ::: ::: METHODS ::: Following the PRISMA statement guidelines for reporting systematic reviews and meta-analysis, two independent reviewers searched studies using keywords related to natural outdoor environments and mortality. ::: ::: ::: DISCUSSION ::: Our review identified twelve eligible studies conducted in North America, Europe, and Oceania with study populations ranging from 1645 up to more than 43 million individuals. These studies are heterogeneous in design, study population, green space assessment and covariate data.We found that the majority of studies show a reduction of the risk of cardiovascular disease (CVD) mortality in areas with higher residential greenness. Evidence of a reduction of all-cause mortality is more limited, and no benefits of residential greenness on lung cancer mortality are observed. There were no studies on blue spaces. ::: ::: ::: CONCLUSIONS ::: This review supports the hypothesis that living in areas with higher amounts of green spaces reduces mortality, mainly CVD. Further studies such as cohort studies with more and better covariate data, improved green space assessment and accounting well for socioeconomic status are needed to provide further and more complete evidence, as well as studies evaluating the benefits of blue spaces.


title: Dual Graph Regularized Dictionary Learning

abstract: Dictionary learning (DL) techniques aim to find sparse signal representations that capture prominent characteristics in a given data. Such methods operate on a data matrix $Y\in \mathbb {R}^{N\times M}$ , where each of its columns $y_i\in \mathbb {R}^N$ constitutes a training sample, and these columns together represent a sampling from the data manifold. For signals $y\in \mathbb {R}^N$ residing on weighted graphs, an additional challenge is incorporating the underlying geometric structure of the data domain into the learning process. In such cases, the topological graph structure may provide a crucial interpretation for the columns, while the data manifold itself may also possess a low-dimensional intrinsic structure that should be taken into account. In this work, we propose a novel dictionary learning algorithm for graph signals that simultaneously takes into account the underlying structure in both the signal and the manifold domains. Specifically, we require that the dictionary atoms are smooth with respect to the graph topology, as encapsulated by the graph Laplacian matrix. Furthermore, we propose to learn this graph Laplacian within the dictionary learning process, adapting it to promote the desired smoothness. Utilizing the manifold structure, we propose to encourage the smoothness of the sparse representations on the data manifold in a similar manner. Both these smoothness forces implicitly enhance the learned dictionary. The efficiency of the proposed approach is demonstrated on synthetic examples as well as on real data, showing that it outperforms other dictionary learning methods in typical problems such as resistance to noise and data completion.


title: Phantom cell realization in LTE and its performance analysis

abstract: In recent years, there has been a tremendous increase in the cellular traffic due to the availability of wide range of devices: smart phones, net-books, tablets, etc. The existing cellular networks will be unable to cater to the increasing demands in near future, thus we need technological enhancements in the cellular infrastructure to meet the ever increasing user requirements. Various approaches have been suggested to increase the existing cellular capacity and provide higher data rates, some of which include deployment of small cells under the coverage area of Macro cells, where a cell denotes the region covered by a particular base station (BS). However, since these small cells cover small regions there exists a significant handover signalling overhead. We suggest an approach where small cells, called Phantom cells, are deployed within the Macro cell coverage area, and the existing Macro BS functions as the centralized controller for all the Phantom BSs deployed within its range. Phantom BSs act as a supplement to the existing Radio Access Network in the LTE infrastructure where it handles the data plane (D-plane) and Macro BSs handle the control plane (C-plane). This paper proposes the definitions of Copiane and D-plane, the modifications in the user equipment (UE) protocol stack which enable the concurrent operation with dual BSs (Phantom and Macro). Mechanisms are developed for the communication of a Phantom BS with a Macro BS over the new interface (X3 interface). NS-3 simulations were performed incorporating the designed architecture for Phantom based HetNets and a significant improvement in UE throughput is observed in comparision with legacy networks comprising of Macro BS.


title: Inter-User Interference in Body Sensor Networks: Preliminary Investigation and an Infrastructure-Based Solution

abstract: Inter-user interference is the interference in communication when several Body Sensor Networks (BSNs) operate in the same vicinity. As BSN users congregate in an area, interference due to concurrently communicating BSNs will increase, resulting in poor performance. In this paper, we conduct a preliminary investigation of the impact of inter-user interference and investigate its behavior with respect to parameters such as number of networks and the rates at which these networks communicate. Inter-user interference, is seen to reduce Packet Delivery Ratio by almost 35% in cases of 8 or more high-rate networks operating in the same location. We also propose a system to mitigate the adverse effects of inter-user interference. Our solution uses a fixed network infrastructure to monitor and identify BSNs that are likely to interfere with each other. The network then recommends changes to the BSN protocol to lessen interference between them. We also implement an instance of our system using a Wireless Sensor Network (WSN) infrastructure to reduce interference. The system is shown to significantly decrease the impact of inter-user interference.


title: Understanding overlay characteristics of a large-scale peer-to-peer IPTV system

abstract: This article presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system, the most popular and largest p2p IPTV (Internet Protocol Television) system today. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of node degree, session length, and peer participation in simultaneous overlays. Our studies reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) PPLive overlays are similar to random graphs in structure and thus more robust and resilient to the massive failure of nodes, (2) Average degree of a peer in the overlay is independent of the channel population size and the node degree distribution can be fitted by a piecewise function, (3) The availability correlation between PPLive peer pairs is bimodal, that is, some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing peers, PPLive peers are impatient and session lengths (discretized, per channel) are typically geometrically distributed, (5) Channel population size is time-sensitive, self-repeated, event-dependent, and varies more than in p2p file-sharing networks, (6) Peering relationships are slightly locality-aware, and (7) Peer participation in simultaneous overlays follows a Zipf distribution. We believe that our findings can be used to understand current large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems.


title: On the Robustness of Authorship Attribution Based on Character N -gram Features

abstract: A number of independent authorship attribution studies have demonstrated the effectiveness of character n-gram features for representing the stylistic properties of text. However, the vast majority of these studies examined the simple case where the training and test corpora are similar in terms of genre, topic, and distribution of the texts. Hence, there are doubts whether such a simple and low-level representation is equally effective in realistic conditions where some of the above factors are not possible to remain stable. In this study, the robustness of authorship attribution based on character n-gram features is tested under cross-genre and cross-topic conditions. In addition, the distribution of texts over the candidate authors varies in training and test corpora to imitate real cases. Comparative results with another competitive text representation approach based on very frequent words show that character n-grams are better able to capture stylistic properties of text when there are significant differences among the training and test corpora. Moreover, a set of guidelines to tune an authorship attribution model according to the properties of training and test corpora is provided. * Assistant Professor, University of the Aegean. 422 JOURNAL OF LAW AND POLICY


title: A faster model selection criterion for OP-ELM and OP-KNN: Hannan-Quinn criterion

abstract: The Optimally Pruned Extreme Learning Machine (OP- ELM) and Optimally Pruned K-Nearest Neighbors (OP-KNN) algorithms use the a similar methodology based on random initialization (OP-ELM) or KNN initialization (OP-KNN) of a Feedforward Neural Network fol- lowed by ranking of the neurons; ranking is used to determine the best combination to retain. This is achieved by Leave-One-Out (LOO) cross- validation. In this article is proposed to use the Hannan-Quinn (HQ) Criterion as a model selection criterion, instead of LOO. It proved to be efficient and as good as the LOO one for both OP-ELM and OP-KNN, while decreasing computations by factors of four to five for OP-ELM and up to 24 for OP-KNN.


title: Reasoning about Object Affordances in a Knowledge Base Representation

abstract: Reasoning about objects and their affordances is a fundamental problem for visual intelligence. Most of the previous work casts this problem as a classification task where separate classifiers are trained to label objects, recognize attributes, or assign affordances. In this work, we consider the problem of object affordance reasoning using a knowledge base representation. Diverse information of objects are first harvested from images and other meta-data sources. We then learn a knowledge base (KB) using a Markov Logic Network (MLN). Given the learned KB, we show that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zero-shot affordance prediction and object recognition given human poses.


title: Cancer incidence in patients with polyglutamine diseases: a population-based study in Sweden

abstract: Summary Background Polyglutamine (polyQ) diseases are characterised by the expansion of CAG triplet repeats in specific genes. The accumulated encoded proteins affect the transcription of numerous transcription factors. We investigated whether polyQ diseases reduce the risk of cancer development. Methods Data on patients with the polyQ diseases Huntington's disease (HD), spinobulbar muscular atrophy (SBMA), and hereditary ataxia (HA) in Sweden were linked to the Swedish Cancer Registry. We calculated standardised incidence ratios for cancers at specific sites or of specific types and the risks were compared with those in the general population. We also analysed risks in the unaffected parents of patients. Findings In the period January, 1969, to December, 2008, we identified 1510 patients with HD, 471 with SBMA, and 3425 with HA. Cancer was diagnosed in 91 (6·0%) HD patients, 34 (7·2%) SBMA patients, and 421 (12·3%) HA patients. The standardised incidence ratios were 0·47 (95% CI 0·38–0·58), 0·65 (0·45–0·91), and 0·77 (0·70–0·85), respectively. Before diagnosis of polyQ disease, the risk of cancer was even lower. Cancer incidence and risk in the unaffected parents of patients with polyQ diseases were similar to those in the general population. Interpretation The consistently decreased incidence of cancer in patients with polyQ diseases suggests that a common mechanism protects against the development of cancer. This feature could be related to the polyQ-tract expansion seen in these diseases. Further studies are warranted to investigate the underlying mechanisms linking cancer and polyQ diseases. Funding Swedish Cancer Society, Swedish Council for Working Life and Social Research.


title: Label Distribution Learning Forests

abstract: Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.


title: Modeling and control of electroadhesion force in DC voltage

abstract: In this paper, a new model for electroadhesion between two surface-insulated plates under DC electric field is presented and control of dynamic responses of electroadhesion force is discussed. Under DC electric field, even if the voltage difference between the plates is constant, electroadhesion force increases or decreases over time depending on the insulating materials. The increase had been explained by Johnsen–Rahbek (JR) model, but the decrease had not been focused or modeled by physically meaningful way. In addition, the previous models did not explicitly consider the mechanical behaviors of the electrodes, although the mechanical behaviors considerably affect the response. In this work, we introduced a new model that combines both electrical and mechanical behaviors. The electrical part, which is based on JR model, explained the force decrease under DC field, in addition to the force increase that had been explained using JR model. The mechanical part was represented by a combination of a spring and a damper. Numerical simulations using the model successfully reproduced characteristics behaviors of electroadhesion force, which include force decay under constant voltages and relatively smaller initial force. Using the inverse model, we carried out experiments to control dynamic responses of electroadhesion force, which successfully controlled force responses against pulse voltages. Through the experiments, we also showed the importance of the neutralization of surface charges for obtaining reproducible responses.


Discriminating Flash Events from DDoS Attacks: A Comprehensive Review


title: Variation in the plant height of spring common wheat (Triticum aestivum L.) hybrid forms under different ecological and geographical conditions

abstract: The results of a two-year study (2013–2014) of the variation in plant height of spring common wheat hybrid forms (F4 and F5) in three geographical localities in Russia (Tyumen oblast) and Germany (Baden-Wurttemberg and Lower Saxony) considerably differing in soil and climatic conditions are described. These three localities are characterized according to temperature and water availability during the growth seasons of the spring wheat. The differences between the geographical localities in water supply and aridity during two growth seasons (2013–2014) were assessed using Selyaninov’s hydrothermal coefficient (HTC). The plant height of different hybrids differently responded to changing environmental factors. A moderate degree of height variation (CV = 11–25%) was prevalent among the tested hybrids. The hybrid forms displaying the largest range in plant height within a locality were identified. The morphotypes of the hybrids were represented by undersized and medium-sized plants. The hybrids formed higher plants under conditions of sufficient moisture. The contributions of the major factors (site, year, and genotype) to the formation of plant height were assessed by three-way ANOVA. The results of this analysis demonstrate that environmental conditions of particular localities are responsible for the largest part of the explained variation in the studied variable (plant height). Two hybrid forms (♀ Hybrid × ♂Lutescens 70 and ♀ Cara × ♂Skent 3) with the least expressed variation in plant height and the highest lodging resistance were identified. Plant height is regarded as one of the indicators characterizing the environmental plasticity of genotypes under different soil and climatic conditions.


title: A self-determination theory perspective on parenting.

abstract: Parents are confronted with a fundamental but often difficult task: teaching children the values and regulations necessary to function effectively in society while also nurturing children’s drive to express themselves and to pursue their unique interests and capacities. The central socialization goal is internalization, wherein children “take in” social regulations, make them their own, and eventually self-regulate autonomously (e.g., Lepper, 1983; Schafer, 1968). When it functions optimally, internalization is beneficial for children’s learning, well-being, and psychosocial adjustment. However, because activities that need to be internalised are often not enjoyable (e.g., clean-up, homework), adults wonder how to encourage children’s engagement in such tasks without negatively affecting their selfdetermination.


title: Interactive segmentation with intelligent scissors

abstract: Abstract We present a new, interactive tool called Intelligent Scissors which we use for image segmentation. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary “snaps” to, and wraps around the object of interest. Live-wire boundary detection formulates boundary detection as an optimal path search in a weighted graph. Optimal graph searching provides mathematically piece-wise optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted.


title: Local binary patterns for multiview facial expression recognition

abstract: Research into facial expression recognition has predominantly been applied to face images at frontal view only. Some attempts have been made to produce pose invariant facial expression classifiers. However, most of these attempts have only considered yaw variations of up to 45^o, where all of the face is visible. Little work has been carried out to investigate the intrinsic potential of different poses for facial expression recognition. This is largely due to the databases available, which typically capture frontal view face images only. Recent databases, BU3DFE and multi-pie, allows empirical investigation of facial expression recognition for different viewing angles. A sequential 2 stage approach is taken for pose classification and view dependent facial expression classification to investigate the effects of yaw variations from frontal to profile views. Local binary patterns (LBPs) and variations of LBPs as texture descriptors are investigated. Such features allow investigation of the influence of orientation and multi-resolution analysis for multi-view facial expression recognition. The influence of pose on different facial expressions is investigated. Others factors are investigated including resolution and construction of global and local feature vectors. An appearance based approach is adopted by dividing images into sub-blocks coarsely aligned over the face. Feature vectors contain concatenated feature histograms built from each sub-block. Multi-class support vector machines are adopted to learn pose and pose dependent facial expression classifiers.


title: Distant Supervision for Relation Extraction beyond the Sentence Boundary

abstract: The growing demand for structured knowledge has led to great interest in relation extraction, especially in cases with limited supervision. However, existing distance supervision approaches only extract relations expressed in single sentences. In general, cross-sentence relation extraction is under-explored, even in the supervised-learning setting. In this paper, we propose the first approach for applying distant supervision to cross- sentence relation extraction. At the core of our approach is a graph representation that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences. We extract features from multiple paths in this graph, increasing accuracy and robustness when confronted with linguistic variation and analysis error. Experiments on an important extraction task for precision medicine show that our approach can learn an accurate cross-sentence extractor, using only a small existing knowledge base and unlabeled text from biomedical research articles. Compared to the existing distant supervision paradigm, our approach extracted twice as many relations at similar precision, thus demonstrating the prevalence of cross-sentence relations and the promise of our approach.


title: BrailleBand: Blind support haptic wearable band for communication using braille language

abstract: Visually impaired people are neglected from many modern communication and interaction procedures. Assistive technologies such as text-to-speech and braille displays are the most commonly used means of connecting such visually impaired people with mobile phones and other smart devices. Both these solutions face usability issues, thus this study focused on developing a user friendly wearable solution called the "BrailleBand" with haptic technology while preserving affordability. The "BrailleBand" enables passive reading using the Braille language. Connectivity between the BrailleBand and the smart device (phone) is established using Bluetooth protocol. It consists of six nodes in three bands worn on the arm to map the braille alphabet, which are actuated to give the sense of touch corresponding to the characters. Three mobile applications were developed for training the visually impaired and to integrate existing smart mobile applications such as navigation and short message service (SMS) with the device BrailleBand. The adaptability, usability and efficiency of reading was tested on a sample of blind users which reflected progressive results. Even though, the reading accuracy depends on the time duration between the characters (character gap) an average Character Transfer Rate of 0.4375 characters per second can be achieved with a character gap of 1000 ms.


title: Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

abstract: Recently, substantial research effort has focused on how to apply CNNs or RNNs to better extract temporal patterns from videos, so as to improve the accuracy of video classification. In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets. We investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we propose a local feature integration framework based on attention clusters, and introduce a shifting operation to capture more diverse signals. We carefully analyze and compare the effect of different attention mechanisms, cluster sizes, and the use of the shifting operation, and also investigate the combination of attention clusters for multimodal integration. We demonstrate the effectiveness of our framework on three real-world video classification datasets. Our model achieves competitive results across all of these. In particular, on the large-scale Kinetics dataset, our framework obtains an excellent single model accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5 accuracy on the validation set. The attention clusters are the backbone of our winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be released soon.


title: vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design

abstract: The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory usage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a significant reduction in memory requirements of DNNs. Similar experiments on VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card containing 12 GB of memory, with 18% performance loss compared to a hypothetical, oracular GPU with enough memory to hold the entire DNN.


title: B2W2: N-Way Concurrent Communication for IoT Devices

abstract: The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.


title: Cache and I/O efficent functional algorithms

abstract: The widely studied I/O and ideal-cache models were developed to account for the large difference in costs to access memory at different levels of the memory hierarchy. Both models are based on a two level memory hierarchy with a fixed size primary memory(cache) of size M, an unbounded secondary memory organized in blocks of size B. The cost measure is based purely on the number of block transfers between the primary and secondary memory. All other operations are free. Many algorithms have been analyzed in these models and indeed these models predict the relative performance of algorithms much more accurately than the standard RAM model. The models, however, require specifying algorithms at a very low level requiring the user to carefully lay out their data in arrays in memory and manage their own memory allocation. In this paper we present a cost model for analyzing the memory efficiency of algorithms expressed in a simple functional language. We show how some algorithms written in standard forms using just lists and trees (no arrays) and requiring no explicit memory layout or memory management are efficient in the model. We then describe an implementation of the language and show provable bounds for mapping the cost in our model to the cost in the ideal-cache model. These bound imply that purely functional programs based on lists and trees with no special attention to any details of memory layout can be as asymptotically as efficient as the carefully designed imperative I/O efficient algorithms. For example we describe an O(n_B logM/Bn_B)cost sorting algorithm, which is optimal in the ideal cache and I/O models.


title: The Psychological Impact of Rape Victims’ Experiences with the Legal, Medical, and Mental Health Systems

abstract: This review article examines rape victims’ experiences seeking postassault assistance from the legal, medical, and mental health systems and how those interactions impact their psychological well-being. This literature suggests that although some rape victims have positive, helpful experiences with social system personnel, for many victims, postassault help seeking becomes a “second rape,” a secondary victimization to the initial trauma. Most reported rapes are not prosecuted, victims treated in hospital emergency departments do not receive comprehensive medical care, and many victims do not have access to quality mental health services. In response to growing concerns about the community response to rape, new interventions and programs have emerged that seek to improve services and prevent secondary victimization. The contributions of rape crisis centers, restorative justice programs, and sexual assault nurse examiner programs are examined. Strategies for creating more visible and impactful roles for psychologists and allied professionals are also discussed.


title: Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

abstract: Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data. For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems. In particular, learning physics models for model-based control requires robust extrapolation from fewer samples - often collected online in real-time - and model errors may lead to drastic damages of the system. Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples. As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed. DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility. The resulting DeLaN network performs very well at robot tracking control. The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time


title: (Batch) Fully Homomorphic Encryption over Integers for Non-Binary Message Spaces

abstract: In this paper, we construct a fully homomorphic encryption (FHE) scheme over integers with the message space \(\mathbb {Z}_Q\) for any prime \(Q\). Even for the binary case \(Q=2\), our decryption circuit has a smaller degree than that of the previous scheme; the multiplicative degree is reduced from \(O(\lambda (\log \lambda )^2)\) to \(O(\lambda )\), where \(\lambda \) is the security parameter. We also extend our FHE scheme to a batch FHE scheme.


title: Learning to Identify Ambiguous and Misleading News Headlines

abstract: Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines "clickbaits" and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.


title: Bloom's taxonomy for CS assessment

abstract: Bloom's Taxonomy is difficult to apply consistently to assessment tasks in introductory programming courses. The Bloom taxonomy is a valuable tool that could enable analysis and discussion of programming assessment if it could be interpreted consistently. We discuss each of the Bloom classification categories and provide a consistent interpretation with concrete exemplars that will allow computer science educators to utilise Bloom's Taxonomy for programming assessment. Using Bloom's Taxonomy to help design examinations could greatly improve the quality of assessment in introductory programming courses.


title: An ultra-wideband microwave balun using a tapered coaxial coil structure working from kHz range to beyond 26.5 GHz

abstract: Many wideband baluns have been presented in the past using coupled lines, pure magnetic coupling or slotlines. Their limitations were set whether in high frequency or low frequency performance. Due to their lumped element bandpass representation, many of them allow just certain bandwidth. The tapered coaxial coil structure allows balun operation beyond 26 GHz and down to the kHz range through partial ferrite filling. The cable losses, cable cut-off frequency, the number of windings, the permeability of the ferrite and the minimum coil diameter limit the bandwidth. The tapering allows resonance free operation through the whole band. Many microwave devices like mixers, power amplifiers, SWR-bridges, antennas, etc. can be made more broadband with this kind of balun. A stepwise approach to the proposed structure is presented and compared to previous balun implementations. Finally, a measurement is provided and some implementation possibilities are discussed.


title: IOT Based Monitoring System in Smart Agriculture

abstract: Internet of Things (IoT) plays a crucial role in smart agriculture. Smart farming is an emerging concept, because IoT sensors capable of providing information about their agriculture fields. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. Monitoring environmental factors is the major factor to improve the yield of the efficient crops. The feature of this paper includes monitoring temperature and humidity in agricultural field through sensors using CC3200 single chip. Camera is interfaced with CC3200 to capture images and send that pictures through MMS to farmers mobile using Wi-Fi.


title: Original Article Attractive Women Want it All: Good Genes, Economic Investment, Parenting Proclivities, and Emotional Commitment 1

abstract: The current research tests the hypothesis that women have an evolved mate value calibration adaptation that functions to raise or lower their standards in a long-term mate according to their own mate value. A woman's physical attractiveness is a cardinal component of women's mate value. We correlated observer-assessed physical attractiveness (face, body, and overall) with expressed preferences for four clusters of mate characteristics (N = 214): (1) hypothesized good-gene indicators (e.g., masculinity, sexiness); (2) hypothesized good investment indicators (e.g., potential income); (3) good parenting indicators (e.g., desire for home and children), and (4) good partner indicators (e.g., being a loving partner). Results supported the hypothesis that high mate value women, as indexed by observer-judged physical attractiveness, expressed elevated standards for all four clusters of mate characteristics. Discussion focuses on potential design features of the hypothesized mate-value calibration adaptation, and ...


title: Design of a High Gain Low Sidelobe Microstrip Antenna Array at Ku-band

abstract: Aperture coupling feed and double-layer microstrip patch structure are applied to design microstrip antenna element and subarrays of microstrip antenna array with high gain low sidelobe and backlobe at Ku-band. Parallel feed and T-junction type power divider are used to design low sidelobe array by the principle of Dolph-Chebeshev distribution. The simulate results show that the relative bandwidth of the element (VSWR 1.5) reaches 25.6%. From the measurement results of subarray: the operational bandwidth (VSWR 1.5) are not less than 0.96GHz and the gain are about 24dB, which are in good agreement with the simulate results. The 256 elements microstrip antenna array with sidelobe in E-plain is simulated.


title: The impact of perception and presence on emotional reactions: a review of research in virtual reality

abstract: Virtual reality (VR) has made its way into mainstream psychological research in the last two decades. This technology, with its unique ability to simulate complex, real situations and contexts, offers researchers unprecedented opportunities to investigate human behavior in well controlled designs in the laboratory. One important application of VR is the investigation of pathological processes in mental disorders, especially anxiety disorders. Research on the processes underlying threat perception, fear, and exposure therapy has shed light on more general aspects of the relation between perception and emotion. Being by its nature virtual, i.e., simulation of reality, VR strongly relies on the adequate selection of specific perceptual cues to activate emotions. Emotional experiences in turn are related to presence, another important concept in VR, which describes the user’s sense of being in a VR environment. This paper summarizes current research into perception of fear cues, emotion, and presence, aiming at the identification of the most relevant aspects of emotional experience in VR and their mutual relations. A special focus lies on a series of recent experiments designed to test the relative contribution of perception and conceptual information on fear in VR. This strand of research capitalizes on the dissociation between perception (bottom–up input) and conceptual information (top-down input) that is possible in VR. Further, we review the factors that have so far been recognized to influence presence, with emotions (e.g., fear) being the most relevant in the context of clinical psychology. Recent research has highlighted the mutual influence of presence and fear in VR, but has also traced the limits of our current understanding of this relationship. In this paper, the crucial role of perception on eliciting emotional reactions is highlighted, and the role of arousal as a basic dimension of emotional experience is discussed. An interoceptive attribution model of presence is suggested as a first step toward an integrative framework for emotion research in VR. Gaps in the current literature and future directions are outlined.


title: A Survey on Sentiment Analysis and Opinion Mining

abstract: With www expanding its reach to anything and everything related to our daily lives, people are becoming more and more vocal to express their views or ideas on online portals, blogs etc. So there is a million of reviews for a product. As a result, it becomes difficult to track the opinions of customers. Sentiment analysis finds the subjective information from the source data by using natural language processing. There are many techniques available to classify the polarity of opinions. This paper is an effect to provide the detailed survey of various technology and methods to provide polarity of sentiments. Lastly, the paper also compares the two tools available for sentiment analysis and highlights their effectiveness in doing so.


title: A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks

abstract: With the rapid proliferation of new technologies and services in the wireless domain, spectrum scarcity has become a major concern. The allocation of the Industrial, Medical and Scientific (ISM) band has enabled the explosion of new technologies (e.g. Wi-Fi) due to its licence-exempt characteristic. The widespread adoption of Wi-Fi technology, combined with the rapid penetration of smart phones running popular user services (e.g. social online networks) has overcrowded substantially the ISM band. On the other hand, according to a number of recent reports, several parts of the static allocated licensed bands are under-utilized. This has brought up the idea of the opportunistic use of these bands through the, so-called, cognitive radios and cognitive radio networks. Cognitive radios have enabled the opportunity to transmit in several licensed bands without causing harmful interference to licensed users. Along with the realization of cognitive radios, new security threats have been raised. Adversaries can exploit several vulnerabilities of this new technology and cause severe performance degradation. Security threats are mainly related to two fundamental characteristics of cognitive radios: cognitive capability, and reconfigurability. Threats related to the cognitive capability include attacks launched by adversaries that mimic primary transmitters, and transmission of false observations related to spectrum sensing. Reconfiguration can be exploited by attackers through the use of malicious code installed in cognitive radios. Furthermore, as cognitive radio networks are wireless in nature, they face all classic threats present in the conventional wireless networks. The scope of this work is to give an overview of the security threats and challenges that cognitive radios and cognitive radio networks face, along with the current state-of-the-art to detect the corresponding attacks. In addition, future challenges are addressed.


title: A Recurrent Neural Network for Modelling Dynamical Systems

abstract: We introduce a recurrent network architecture for modelling a general class of dynamical systems. The network is intended for modelling real-world processes in which empirical measurements of the external and state variables are obtained at discrete time points. The model can learn from multiple temporal patterns, which may evolve on different timescales and be sampled at non-uniform time intervals. We demonstrate the application of the model to a synthetic problem in which target data are only provided at the final time step. Despite the sparseness of the training data, the network is able not only to make good predictions at the final time step for temporal processes unseen in training, but also to reproduce the sequence of the state variables at earlier times. Moreover, we show how the network can infer the existence and role of state variables for which no target information is provided. The ability of the model to cope with sparse data is likely to be useful in a number of applications, including, in...


title: An 87 fJ/conversion-step 12 b 10 MS/s SAR ADC using a minimum number of unit capacitors

abstract: This work proposes a 12 b 10 MS/s 0.11 μm CMOS successive-approximation register ADC based on a C-R hybrid DAC for low-power sensor applications. The proposed C-R DAC employs a 2-step split-capacitor array of upper seven bits and lower five bits to optimize power consumption and chip area at the target speed and resolution. A VCM-based switching method for the most significant bit and reference voltage segments from an insensitive R-string for the last two least significant bits minimize the number of unit capacitors required in the C-R hybrid DAC. The comparator accuracy is improved by an open-loop offset cancellation technique in the first-stage pre-amp. The prototype ADC in a 0.11 μm CMOS process demonstrates the measured differential nonlinearity and integral nonlinearity within 1.18 LSB and 1.42 LSB, respectively. The ADC shows a maximum signal-to-noise-and-distortion ratio of 63.9 dB and a maximum spurious-free dynamic range of 77.6 dB at 10 MS/s. The ADC with an active die area of 0.34 mm2 consumes 1.1 mW at 1.0 V and 10 MS/s, corresponding to a figure-of-merit of 87 fJ/conversion-step.


title: Sparse Coding-Inspired Optimal Trading System for HFT Industry

abstract: The financial industry has witnessed an exceptionally fast progress of incorporating information processing techniques in designing knowledge-based automated systems for high-frequency trading (HFT). This paper proposes a sparse coding-inspired optimal trading (SCOT) system for real-time high-frequency financial signal representation and trading. Mathematically, SCOT simultaneously learns the dictionary, sparse features, and the trading strategy in a joint optimization, yielding optimal feature representations for the specific trading objective. The learning process is modeled as a bilevel optimization and solved by the online gradient descend method with fast convergence. In this dynamic context, the system is tested on the real financial market to trade the index futures in the Shanghai exchange center.


title: Photoperiodic Responses of the Squash Bug (Heteroptera: Coreidae): Diapause Induction and Maintenance

abstract: The squash bug, Anasa tristis DeGeer, undergoes a reproductive diapause from late summer to spring in northeastern Kansas. In the laboratory, diapause was induced in 100% of adult females reared under photoperiods shorter than 14:10 (L:D) and in a variably lower percentage of the population under all longer photoperiods. The critical photoperiod for diapause induction falls between 14:10 and 14.5:9.5; this range compares closely with prevailing natural daylengths when 50% of the adult population enters diapause in the field. Between October and March, short daylengths maintained, and long daylengths terminated, diapause in field-sampled adults. Under natural daylength at 26°C, the duration of diapause became progressively shorter with advancing sample date. In nature, the photoperiodic maintenance of diapause is completed in most of the population by late May. A prolonged diapause probably serves to prevent premature postdiapause development during the thermally variable spring conditions encountered in Kansas. Some implications of these findings for biological control and pest management programs are discussed.


title: Designing a Chatbot for diabetic patients

abstract: Artificial Intelligence chatbot is a technology that makes interaction between man and machine possible by using natural language. In this paper, we proposed an architectural design of a chatbot that will function as virtual diabetes physician/doctor. This chatbot will allow diabetic patients to have a diabetes control/management advice without the need to go to the hospital. A general history of a chatbot, a brief description of each chatbots is discussed. We proposed the design of a new technique that will be implemented in this chatbot as the key component to function as diabetes physician. Using this design, chatbot will remember the conversation path through parameter called Vpath. Vpath will allow chatbot to gives a response that is mostly suitable for the whole conversation as it specifically designed to be a virtual diabetes physician.


title: A Compact Embedding for Facial Expression Similarity

abstract: Most of the existing work on automatic facial expression analysis focuses on discrete emotion recognition, or facial action unit detection. However, facial expressions do not always fall neatly into pre-defined semantic categories. Also, the similarity between expressions measured in the action unit space need not correspond to how humans perceive expression similarity. Different from previous work, our goal is to describe facial expressions in a continuous fashion using a compact embedding space that mimics human visual preferences. To achieve this goal, we collect a large-scale faces-in-the-wild dataset with human annotations in the form: Expressions A and B are visually more similar when compared to expression C, and use this dataset to train a neural network that produces a compact (16-dimensional) expression embedding. We experimentally demonstrate that the learned embedding can be successfully used for various applications such as expression retrieval, photo album summarization, and emotion recognition. We also show that the embedding learned using the proposed dataset performs better than several other embeddings learned using existing emotion or action unit datasets.


title: Studies of electromechanical transients in FREMM frigates integrated power system using a time-domain simulator

abstract: The new simulator of the FREMM (European Multi Mission Frigates) electric Integrated Power System (IPS) is treated in this paper. The simulator, recently delivered by the University of Trieste to the Italian Navy, is utilized to study relevant electromechanical transients of the on board IPS. In particular, this simulator is employed in verifying some automated sequences and restore maneuvers, which are hard to be analyzed through on board tests. Other uses will be the support in crew training and the study of different control strategies. The developed IPS simulator has shown to be a tool able to help navy engineers and electrical staff in understanding some relevant behaviors in electrical transients, permitting the simulation of the system in a cheap but accurate way. A validating campaign of the numerical results is now on the way, referring to commissioning and sea trials results of the first built ship of the class.


title: Leader Election in a Smartphone Peer-to-Peer Network

abstract: In this paper, we study the fundamental problem of leader election in the mobile telephone model: a recently introduced variation of the classical telephone model modified to better describe the local peer-to-peercommunication services implemented in many popular smartphone operating systems. In more detail, the mobile telephone model differs from the classical telephone model in three ways: (1) each devicecan participate in at most one connection per round; (2) the network topology can undergo a parameterizedrate of change; and (3) devices can advertise a parameterized number of bits to their neighbors in each round before connection attempts are initiated. We begin by describing and analyzing a new leader election algorithm in this model that works under the harshest possible parameter assumptions: maximum rate of topology changes and no advertising bits. We then apply this result to resolve an open question from [Ghaffari, 2016] on the efficiency of PUSH-PULL rumor spreading under these conditions. We then turn our attention to the slightly easier case where devices can advertise a single bit in each round. We demonstrate a large gap in time complexity between these zero bit and one bit cases. In more detail, we describe and analyze a new algorithm that solves leader election with a time complexitythat includes the parameter bounding topology changes. For all values of this parameter, this algorithm is faster than the previous result, with a gap that grows quickly as the parameter increases (indicating lower rates of change). We conclude by describing and analyzing a modified version of this algorithmthat does not require the assumptionthat all devices start during the same round. This new version has a similar time complexity (the rounds required differ only by a polylogarithmic factor),but now requires slightly larger advertisement tags.


title: An optimized DNA based encryption scheme with enforced secure key distribution

abstract: More and more sensitive information is transmitted and stored in computer networks. Security has become a critical issue. As traditional cryptographic systems are now vulnerable to attacks, DNA based cryptography has been identified as a promising technology because of the vast parallelism and extraordinary information density. While a body of research has proposed the DNA based encryption algorithm, no research has provided solutions to distribute complex and long secure keys. This paper introduces a Hamming code and a block cipher mechanism to ensure secure transmission of a secure key. The research overcomes the limitation on the length of the secure key represented by DNA strands. Therefore it proves that real biological DNA strands are useful for encryption computing. To evaluate our method, we apply the block cipher mechanism to optimize a DNA-based implementation of a conventional symmetric encryption algorithm, described as “yet another encryption algorithm”. Moreover, a maximum length matching algorithm is developed to provide immunity against frequency attacks.


title: Liking for happy- and sad-sounding music: Effects of exposure

abstract: We examined liking for happy- and sad-sounding music as a function of exposure, which varied both in quantity (number of exposures) and in quality (focused or incidental listening). Liking ratings were higher for happy than for sad music after focused listening, but similar after incidental listening. In the incidental condition, liking ratings increased linearly as a function of exposure. In the focused condition, liking ratings were an inverted U-shaped function of exposure, with initial increases in liking (after 2 exposures) followed by decreases (after 8 or 32 exposures). The results documented that: (1) sad music is liked as much as happy music in some instances; (2) frequency of exposure causes both familiarity (positive) and over-familiarity (negative) effects; and (3) effects of exposure on liking differ for focused and incidental listening.


title: Survey on 3D Hand Gesture Recognition

abstract: Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.


title: The SURE-LET Approach to Image Denoising

abstract: We propose a new approach to image denoising, based on the image-domain minimization of an estimate of the mean squared error--Stein's unbiased risk estimate (SURE). Unlike most existing denoising algorithms, using the SURE makes it needless to hypothesize a statistical model for the noiseless image. A key point of our approach is that, although the (nonlinear) processing is performed in a transformed domain--typically, an undecimated discrete wavelet transform, but we also address nonorthonormal transforms--this minimization is performed in the image domain. Indeed, we demonstrate that, when the transform is a "tight" frame (an undecimated wavelet transform using orthonormal filters), separate subband minimization yields substantially worse results. In order for our approach to be viable, we add another principle, that the denoising process can be expressed as a linear combination of elementary denoising processes--linear expansion of thresholds (LET). Armed with the SURE and LET principles, we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient. Quite remarkably, the very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential.


title: An End-to-End Machine Learning System for Harmonic Analysis of Music

abstract: We present a new system for the harmonic analysis of popular musical audio. It is focused on chord estimation, although the proposed system additionally estimates the key sequence and bass notes. It is distinct from competing approaches in two main ways. First, it makes use of a new improved chromagram representation of audio that takes the human perception of loudness into account. Furthermore, it is the first system for joint estimation of chords, keys, and bass notes that is fully based on machine learning, requiring no expert knowledge to tune the parameters. This means that it will benefit from future increases in available annotated audio files, broadening its applicability to a wider range of genres. In all of three evaluation scenarios, including a new one that allows evaluation on audio for which no complete ground truth annotation is available, the proposed system is shown to be faster, more memory efficient, and more accurate than the state-of-the-art.


title: The MIMIC Code Repository: enabling reproducibility in critical care research

abstract: Objective ::: Lack of reproducibility in medical studies is a barrier to the generation of a robust knowledge base to support clinical decision-making. In this paper we outline the Medical Information Mart for Intensive Care (MIMIC) Code Repository, a centralized code base for generating reproducible studies on an openly available critical care dataset.


title: Personality predictors of academic outcomes: Big five correlates of GPA and SAT scores.

abstract: The authors examined relations between the Big Five personality traits and academic outcomes, specifically SAT scores and grade-point average (GPA). Openness was the strongest predictor of SAT verbal scores, and Conscientiousness was the strongest predictor of both high school and college GPA. These relations replicated across 4 independent samples and across 4 different personality inventories. Further analyses showed that Conscientiousness predicted college GPA, even after controlling for high school GPA and SAT scores, and that the relation between Conscientiousness and college GPA was mediated, both concurrently and longitudinally, by increased academic effort and higher levels of perceived academic ability. The relation between Openness and SAT verbal scores was independent of academic achievement and was mediated, both concurrently and longitudinally, by perceived verbal intelligence. Together, these findings show that personality traits have independent and incremental effects on academic outcomes, even after controlling for traditional predictors of those outcomes.


title: DISCRIMINATIVE TOPIC MINING FOR SOCIAL SPAM DETECTION

abstract: Won the Best Paper Award in the Pacific Asia Conference on Information Systems (PACIS) 2014, Chengdu, China.


title: Functional optimization properties of median filtering

abstract: In this letter, we use a new approach for studying the properties of median filtering. Specifically, using threshold decomposition, it is shown that median filtering operation minimizes a two-term cost function of the output state of the median filter. The first term of the cost function measures the smoothness between the median filter output and its neighbor points within the operation window, and the second term measures the discrepancy between the filter output and its original signal. The results from this study have provided us with a new tool to analyze and understand some of the properties of the median filtering operation, including weighted median filtering.<<ETX>>


title: A Discrete Approach to Multiresolution Curves and Surfaces

abstract: Subdivision surfaces have been widely adopted in modeling in part because they introduce a separation between the surface and the underlying basis functions. This separation allows for simple general-topology subdivision schemes. Multiresolution representations based on subdivision, however, incongruently return to continuous functional spaces in their construction and analysis. In this paper, we propose a discrete multiresolution framework applicable to many subdivision schemes and based only on the subdivision rules. Noting that a compact representation can only afford to store a subset of the detail information, our construction enforces a constraint between adjacent detail terms. In this way, all detail information is recoverable for reconstruction, and a decomposition approach is implied by the constraint. Our framework is demonstrated with case studies in Dyn-Levin-Gregory curves and Catmull-Clark surfaces, each of which our method produces results on par with earlier methods. It is further shown that our construction can be interpreted as biorthogonal wavelet systems.


title: Sedimentary geochemical record of human-induced environmental changes in the Lake Brunnsviken watershed, Sweden

abstract: Environmental changes in Lake Brunnsviken, its watershed, and the greater Stockholm region since the middle of the nineteenth century have left interpretable geochemical imprints in the bottom sediments. These humaninduced perturbations within the lake’s watershed included agriculture, urbanization, sewage and industrial disposal, and water column aeration. Smaller d 15 Ntotal values, high organic carbon mass accumulation rates, low C : N ratios, and larger d 13 Corg values identify periods of increased nutrient delivery and elevated primary productivity in the lake. C : S ratios that change from high to low trace the transition from an oxic hypolimnion to an anoxic one during the periods of high productivity. Accumulations of redox-sensitive trace elements increase during the anoxic period and are further magnified during a time of industrial waste discharge into the lake. A recent decrease in black carbon concentrations in sediments reflects the conversion from wood and coal to cleaner forms of energy.


title: Identification of Sensor Requirements for a Quality Data-based Risk Management in Multimodal Supply Chains

abstract: Abstract Rising volatility in globally distributed supply networks leads to an increasing need of supply chain event management. Event-driven control requires sensor-based, real-time quality data. The article presents necessary capabilities of sensors and sensor networks to realize such event-driven control mechanisms. Based on a failure modes and effect analysis of automotive supply chains, the sensor requirements are identified and discussed. A discrete event simulation evaluates the effects of quality data for event-driven control. In addition to the real-time ability, further capabilities regarding communication, energy, granularity, identification and data storage are identified. The derived capabilities are the basis to develop event-driven architectures.


title: An Embedded Support Vector Machine

abstract: In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java Virtual Machine named Java Optimized Processor.


title: A theoretical argument for complex-valued convolutional networks

abstract: A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with several complex-valued vectors followed by (2) taking the absolute value of every entry of the resulting vectors followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as “data-driven multiscale windowed power spectra,” “data-driven multiscale windowed absolute spectra,” “data-driven multiwavelet absolute values,” or (in their most general configuration) “data-driven nonlinear multiwavelet packets.” Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max. pooling, etc., do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). This note develops “data-driven multiscale windowed spectra” for certain stochastic processes that are common in the modeling of time series (such as audio) and natural images (including patterns and textures). We motivate the construction of such multiscale spectra in the form of “local averages of multiwavelet absolute values” or (in the most general configuration) “nonlinear multiwavelet packets” and connect these to certain “complex-valued convolutional networks.” A textbook treatment of all concepts and terms used above and below is given by [12]. Further information is available in the original work of [7], [15], [5], [4], [19], [16], [9], [20], and [18], for example. The work of [8], [13], [17], [2], and [3] also develops complex-valued convolutional networks (convnets). Renormalization group theory and its connection to convnets is discussed by [14]; this connection is incredibly insightful, though we leave further discussion to the cited work. Our exposition relies on nothing but the basic signal processing treated by [12]. For simplicity, we first limit consideration to the special case of a doubly infinite sequence of nonnegative random variables Xk, where k ranges over the integers. This input data will be the result of convolving an unmeasured independent and identically distributed (i.i.d.) sequence Zk, where k ranges over the integers, with an unknown sequence of real numbers fk, where k ranges over the integers (this latter sequence is known as a “filter,” whereas the i.i.d. sequence is known as “white noise”):


title: Making the Most of Your Company's Knowledge: A Strategic Framework

abstract: This paper develops a framework of four strategies for managing knowledge. Companies can leverage their knowledge throughout the organisation, expand their knowledge further based on existing expertise, appropriate knowledge from partners and other organisations, and develop completely new expertise by probing new technologies or markets. The two core processes of knowledge creation and transfer are central to the execution of these strategies, as is the company's domains of knowledge. The framework is based on conceptualisation about knowledge management practices at Unilever, a multinational fast-moving consumer goods company.


title: A cloud interoperability broker (CIB) for data migration in SaaS

abstract: Cloud computing is becoming increasingly popular. Information technology market leaders, e.g., Microsoft, Google, and Amazon, are extensively shifting toward cloud-based solutions. However, there is isolation in the cloud implementations provided by the cloud vendors. Limited interoperability can cause one user to adhere to a single cloud provider; thus, a required migration of an application or data from one cloud provider to another may necessitate a significant effort and/or full-cycle redevelopment to fit the new provider's standards and implementation. The ability to move from one cloud vendor to another would be a step toward advancing cloud computing interoperability and increasing customer trust. This study proposes a cloud broker solution to fill the interoperability gap between different software-as-a-service providers. The proposed cloud broker was implemented and tested on a real enterprise application dataset. The migration process was completed and it worked correctly, according to a specified mapping model.


title: A Deadline Constrained Critical Path Heuristic for Cost-Effectively Scheduling Workflows

abstract: Effective use of elastic heterogeneous cloud resources represents a unique multi-objective scheduling challenge with respect to cost and time constraints. In this paper we introduce a novel deadline constrained scheduling algorithm, Deadline Constrained Critical Path (DCCP), that manages the scheduling of workloads on dynamically provisioned cloud resources. The DCCP algorithm consists of two stages: (i) task prioritization, and (ii) task assignment, and builds upon the concept of Constrained Critical Paths to execute a set of tasks on the same instance in order to fulfil our goal of reducing data movement between instances. We evaluated the normalized cost and success rate of DCCP and compared these results with IC-PCP. Overall, DCCP schedules with lower cost and exhibits a higher success rate in meeting deadline constraints.


title: Cloud-Assisted Safety Message Dissemination in VANET–Cellular Heterogeneous Wireless Network

abstract: In vehicular ad hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, the integrated VANET–cellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, whereas rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies. Specifically, we propose a novel Cloud-assisted Message Downlink dissemination Scheme (CMDS), with which the safety messages in the cloud server are first delivered to the suitable mobile gateways on relevant roads with the help of cloud computing (where gateways are buses with both cellular and VANET interfaces), and then being disseminated among neighboring vehicles via vehicle-to-vehicle (V2V) communication. To evaluate the proposed scheme, we mathematically analyze its performance and conduct extensive simulation experiments. Numerical results confirm the efficiency of CMDS in various urban scenarios.


title: A Pilot Study of the Efficacy of Heart Rate Variability (HRV) Biofeedback in Patients with Fibromyalgia

abstract: Fibromyalgia (FM) is a non-inflammatory rheumatologic disorder characterized by musculoskeletal pain, fatigue, depression, cognitive dysfunction and sleep disturbance. Research suggests that autonomic dysfunction may account for some of the symptomatology of FM. An open label trial of biofeedback training was conducted to manipulate suboptimal heart rate variability (HRV), a key marker of autonomic dysfunction. Methods: Twelve women ages 18–60 with FM completed 10 weekly sessions of HRV biofeedback. They were taught to breathe at their resonant frequency (RF) and asked to practice twice daily. At sessions 1, 10 and 3-month follow-up, physiological and questionnaire data were collected. Results: There were clinically significant decreases in depression and pain and improvement in functioning from Session 1 to a 3-month follow-up. For depression, the improvement occurred by Session 10. HRV and blood pressure variability (BPV) increased during biofeedback tasks. HRV increased from Sessions 1–10, while BPV decreased from Session 1 to the 3 month follow-up. Conclusions: These data suggest that HRV biofeedback may be a useful treatment for FM, perhaps mediated by autonomic changes. While HRV effects were immediate, blood pressure, baroreflex, and therapeutic effects were delayed. This is consistent with data on the relationship among stress, HPA axis activity, and brain function.


Automatic personalization based on Web usage mining


title: Graphene Photonics, Plasmonics, and Optoelectronics

abstract: We review the characteristics of the optical excitations of graphene involving interband, intraband, and collective (plasmon) electronic excitations. We then discuss the different mechanisms by which photon energy can be converted to an electrical current in graphene. Finally, we review applications of graphene as transparent conductive screens, as photodetectors and light modulators at different wavelength ranges.


Multi-objective non-linear programming problem based on Neutrosophic Optimization Technique and its application in Riser Design Problem


title: The smashed filter for compressive classification and target recognition

abstract: The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. We propose here a framework for compressive classification that operates directly on the compressive measurements without first reconstructing the image. We dub the resulting dimensionally reduced matched filter the smashed filter. The first part of the theory maps traditional maximum likelihood hypothesis testing into the compressive domain; we find that the number of measurements required for a given classification performance level does not depend on the sparsity or compressibility of the images but only on the noise level. The second part of the theory applies the generalized maximum likelihood method to deal with unknown transformations such as the translation, scale, or viewing angle of a target object. We exploit the fact the set of transformed images forms a low-dimensional, nonlinear manifold in the high-dimensional image space. We find that the number of measurements required for a given classification performance level grows linearly in the dimensionality of the manifold but only logarithmically in the number of pixels/samples and image classes. Using both simulations and measurements from a new single-pixel compressive camera, we demonstrate the effectiveness of the smashed filter for target classification using very few measurements.


title: Semiotic Schemas: A Framework for Grounding Language in Action and Perception

abstract: A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent's physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.


title: Prediction of students performance using Educational Data Mining

abstract: Data mining plays an important role in the business world and it helps to the educational institution to predict and make decisions related to the students' academic status. With a higher education, now a days dropping out of students' has been increasing, it affects not only the students' career but also on the reputation of the institute. The existing system is a system which maintains the student information in the form of numerical values and it just stores and retrieve the information what it contains. So the system has no intelligence to analyze the data. The proposed system is a web based application which makes use of the Naive Bayesian mining technique for the extraction of useful information. The experiment is conducted on 700 students' with 19 attributes in Amrita Vishwa Vidyapeetham, Mysuru. Result proves that Naive Bayesian algorithm provides more accuracy over other methods like Regression, Decision Tree, Neural networks etc., for comparison and prediction. The system aims at increasing the success graph of students using Naive Bayesian and the system which maintains all student admission details, course details, subject details, student marks details, attendance details, etc. It takes student's academic history as input and gives students' upcoming performances on the basis of semester.


title: Isolated DC-DC Converter for Bidirectional Power Flow Controlling with Soft-Switching Feature and High Step-Up/Down Voltage Conversion

abstract: In this paper, a novel isolated bidirectional DC-DC converter is proposed, which is able to accomplish high step-up/down voltage conversion. Therefore, it is suitable for hybrid electric vehicle, fuel cell vehicle, energy backup system, and grid-system applications. The proposed converter incorporates a coupled inductor to behave forward-and-flyback energy conversion for high voltage ratio and provide galvanic isolation. The energy stored in the leakage inductor of the coupled inductor can be recycled without the use of additional snubber mechanism or clamped circuit. No matter in step-up or step-down mode, all power switches can operate with soft switching. Moreover, there is a inherit feature that metal–oxide–semiconductor field-effect transistors (MOSFETs) with smaller on-state resistance can be adopted because of lower voltage endurance at primary side. Operation principle, voltage ratio derivation, and inductor design are thoroughly described in this paper. In addition, a 1-kW prototype is implemented to validate the feasibility and correctness of the converter. Experimental results indicate that the peak efficiencies in step-up and step-down modes can be up to 95.4% and 93.6%, respectively.


title: Adaptive Document Retrieval for Deep Question Answering

abstract: State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents -- as used in prior research -- suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.


title: Vision based automatic target scoring system for mobile shooting range

abstract: This paper describes a system for automatic target scoring which applied in mobile shooting range setting. Target scoring is used for training precision and reaction shooting skill for military organization. The problems addressed are acquisition system, bulls eye area detection, bullet hole detection and automated scoring. The method used in this system is different than other system previously developed which is based on frame differencing or subtraction. This system is using single frame detection with classification-based filtering and tracking bullet holes in association with previous frame to detect novel shot while keeping track of previously detected shot.


Coseismic Displacement of M8.0 Sichuan Earthquake Derived by ALOS Radar Interferometry


title: RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions

abstract: The design of a wearable upper extremity therapy robot RUPERT IVtrade (Robotic Upper Extremity Repetitive Trainer) device is presented. It is designed to assist in repetitive therapy tasks related to activities of daily living which has been advocated for being more effective for functional recovery. RUPERTtrade has five actuated degrees of freedom driven by compliant and safe pneumatic muscle actuators (PMA) assisting shoulder elevation, humeral external rotation, elbow extension, forearm supination and wrist/hand extension. The device is designed to extend the arm and move in a 3D space with no gravity compensation, which is a natural setting for practicing day-to-day activities. Because the device is wearable and lightweight, the device is very portable; it can be worn standing or sitting for performing therapy tasks that better mimic activities of daily living. A closed-loop controller combining a PID-based feedback controller and a iterative learning controller (ILC)-based feedforward controller is proposed for RUPERT for passive repetitive task training. This type of control aids in overcoming the highly nonlinear nature of the plant under control, and also helps in adapting easily to different subjects for performing different tasks. The system was tested on two able-bodied subjects to evaluate its performance.


title: Effective tracking of the players and ball in indoor soccer games in the presence of occlusion

abstract: Object tracking is one of the major subjects in machine vision and plays a main role in detection of major events in indoor soccer matches. In this paper, a novel approach for tracking the ball and players is proposed. In this method, the ground lines are segmented and eliminated using a fast and effective method. Then, the remaining non-field pixels are considered and labeled as players and the ball. A fast level set contour is used to track players and the ball. The proposed method can track players and the ball in presence of occlusion. Experiments show that the proposed method is robust to occlusion and different field colors.