title: Domain transform for edge-aware image and video processing

abstract: We present a new approach for performing high-quality edge-preserving filtering of images and videos in real time. Our solution is based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line. This transform preserves the geodesic distance between points on these curves, adaptively warping the input signal so that 1D edge-preserving filtering can be efficiently performed in linear time. We demonstrate three realizations of 1D edge-preserving filters, show how to produce high-quality 2D edge-preserving filters by iterating 1D-filtering operations, and empirically analyze the convergence of this process. Our approach has several desirable features: the use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization. We demonstrate the versatility of our domain transform and edge-preserving filters on several real-time image and video processing tasks including edge-preserving filtering, depth-of-field effects, stylization, recoloring, colorization, detail enhancement, and tone mapping.


title: Neural Networks and Financial Trading and the Efficient Markets Hypothesis

abstract: The efficient markets hypothesis asserts that the price of an asset reflects all of the information that can be obtained from past prices of the asset. A direct corollary of this hypothesis is that stock prices follow a random walk, and that any profits derived from timing the market are due entirely to chance. In the absence of any ability to predict the market, the most appropriate strategy---according to proponents of the efficient markets hypothesis---is to buy and hold. In this paper we describe a methodology by which neural networks can be trained indirectly, using a genetic algorithm based weight optimisation procedure, to determine buy and sell points for financial commodities traded on a stock exchange. In order to test the significance of the returns achieved using this methodology, we compare the returns on four financial price series with returns achieved on random walk data derived from each of these series using a bootstrapping procedure. These bootstrapped samples contain exactly the same distribution of daily returns as the original series, but lack any serial dependence present in the original. Our results indicate that on some price series the return achieved is significantly greater than that which can be achieved on the bootstrapped samples. This lends support to the claim that some financial time series are not entirely random, and that---contrary to the predictions of the efficient markets hypothesis---a trading strategy based solely on historical price data can be used to achieve returns better than those achieved using a buy-and-hold strategy.


title: On the Potential of Bluetooth Low Energy Technology for Vehicular Applications

abstract: With the increasing number of sensors in modern vehicles, using an intra-vehicular wireless sensor network (IVWSN) is a possible solution for the automotive industry for addressing the potential issues that arise from additional wiring harness. Such a solution could help car manufacturers develop vehicles that have better fuel economy and performance, in addition to supporting new applications. However, which wireless technology should be used for maximizing the benefits of IVWSNs is still an open issue. In this article we propose to use a new wireless technology known as Bluetooth Low Energy (BLE) and outline a new architecture for IVWSN. Based on a comprehensive study that encompasses an example application, it is shown that BLE is an excellent option that can be used in IVWSNs for certain applications mainly due to its good performance and low-power, lowcomplexity, and low-cost attributes.


title: Large Adjustable Speed Drives

abstract: Today, high energy costs and the availability of large (Example: 3000A, 4000V) solid- state devices are forcing industry to look at new and different adjustable speed drives. This paper introduces three different types of adjustable able speed drives for use on large fans and mixed flow pumps. The three systems are: (1) the current source inverter; (2) the wound rotor slip recovery system; and (3) the synchro-converter or brushless D.C. system.


Bayesian time series models and scalable inference


title: A fast malware detection algorithm based on objective-oriented association mining

abstract: Objective-oriented association (OOA) mining has been successfully applied in malware detection. One problem of OOA mining is that the number of association rules is very large, and many of the rules are redundant and have little capacity to distinguish malware from benign files. This circumstance seriously affects the running speed of OOA for malware detection. In this paper, an API (Application Programming Interface)-based association mining method is proposed for detecting malware. To increase the detection speed of the OOA, different strategies are presented: to improve the rule quality, criteria for API selection are proposed to remove APIs that cannot become frequent items; to find association rules that have strong discrimination power, we define the rule utility to evaluate the association rules; and to improve the detection accuracy, a classification method based on multiple association rules is adopted. The experiments show that the proposed strategies can significantly improve the running speed of OOA. In our experiments the time cost for data mining is reduced by thirty-two percent, and the time cost for classification is reduced by fifty percent.


title: Lichen sclerosus of the oral mucosa: a case report.

abstract: Lichen sclerosus or lichen sclerosus et atrophicus is a chronic inflammatory disease predominantly affecting the genital mucosa and skin. Clinically, it is characterized by white atrophic plaques in the anogenital region. The lesions are generally asymptomatic, but may cause discomfort with itching and pain. Extragenital mucosal involvement is very unusual, and lesions limited to the oral mucosa are even less frequent. Knowledge of such lesions is important in order to establish a differential diagnosis with other white oral lesions, and histological confirmation is required. We present the case of a 31-year-old woman with a well delimited, pearly white lesion located in the upper gingival mucosa, lip mucosa and adjacent skin. The lesion had led to loss of periodontal attachment of the affected tooth (2.3), causing pain in response to tooth brushing. The biopsy confirmed lichen sclerosus, and treatment was provided in the form of intralesional corticoid injections, followed by improvement of the mucosal lesion, though without recovery of the periodontal loss.


title: Activity recognition using inertial sensors and a 2-D convolutional neural network

abstract: Activity recognition has a growing interest in many fields like biomedical engineering, game development or for sports training. Sensors are attached to a human body to track body movement, physiological signals or environmental variables and these informations are interpreted by algorithms. The finding of characteristics of sensor data for the classification problem plays an important role and is commonly done by hand and with a lot of expertise by the researcher. In this paper, a 2-D Convolutional Neural Network (CNN) is used to extract features of images, which are fed in a Support Vector Machine (SVM) for classification. The key idea are spectrograms produced from 1-D signals of inertial sensors. To evaluate the proposed system, two datasets are used.


title: A Reinforcement Learning Approach to Online Clustering

abstract: A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.


title: Plan-based character diversity

abstract: Non-player character diversity enriches game environments increasing their replay value. We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.


title: Research of spectral characteristics of ubiquinone solution and explore of the solvent effect on the experimental results

abstract: This work is dedicated to the research of the clinical diagnosis method of determining the antioxidant status of the human body. The existing methods for determining the level of antioxidants in connection with biological functions of the human antioxidant system were studied. Antioxidants in the human body, in the form of solutions were chosen as a research objects. The technique of experimental studies of ubiquinone solutions in oil and alcohol by spectroscopic method was offered. The experimental results connected with the optical density and color characteristics confirm the possibility of applying the clinical diagnosis method of estimation antioxidant balance. Also, it was found that this method can be applicable to the quality control of medicines for treating diseases provoked by oxidative stress, which means that this method may be developed not only for using in the clinic.


title: Factors affecting the success of Open Source Software

abstract: With the rapid rise in the use of Open Source Software (OSS) in all types of applications, it is important to know which factors can lead to OSS success. OSS projects evolve and transform over time; therefore success must be examined longitudinally over a period of time. In this research, we examine two measures of project success: project popularity and developer activity, of 283 OSS projects over a span of 3 years, in order to observe changes over time. A comprehensive research model of OSS success is developed which includes both extrinsic and intrinsic attributes. Results show that while many of the hypothesized relationships are supported, there were marked differences in some of the relationships at different points in time lending support to the notion that different factors need to be emphasized as the OSS project unfolds over time.


title: Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA

abstract: Complete database for assessing the quality of foreground detection strategies.Indoor and outdoor sequences with many categories addressing different challenges.All the sequences are fully annotated at both pixel and object levels.Information concerning stationary foreground objects.Sequences recorded with static and moving cameras. Display Omitted A public, complete, compact, and well structured database is proposed, which allows to test moving object detection strategies. The database is composed of many real indoor and outdoor sequences organized in different categories, each of one covering a specific challenge. In contrast to other databases, the proposed one is fully annotated at both pixel and object levels. Therefore, it is suitable for strategies exclusively focused on the detection of moving objects and also for those that integrate tracking algorithms in their detection approaches. Additionally, it contains sequences recorded with static and moving cameras and it also provides information about the moving objects remaining temporally static.To test its usefulness, the database has been used to assess the quality of some outstanding moving object detection methods.


title: Fair Client Puzzles from the Bitcoin Blockchain

abstract: Client puzzles have been proposed as a mechanism for proving legitimate intentions by providing "proofs of work", which can be applied to discourage malicious usage of resources. A typical problem of puzzle constructions is the difference in expected solving time on different computing platforms. We call puzzles which can be solved independently of client computing resources fair client puzzles. ::: ::: We propose a construction for client puzzles requiring widely distributed computational effort for their solution. These puzzles can be solved using the mining process of Bitcoin, or similar cryptocurrencies. Adapting existing definitions, we show that our puzzle construction satisfies formal requirements of client puzzles under reasonable assumptions. We describe a way of transforming our client puzzles for use in denial of service scenarios and demonstrate a practical construction.


title: Synthetic aperture imaging algorithms : with application to wide bandwidth sonar

abstract: This thesis contains the complete end-to-end simulation, development, implementation, and calibration of the wide bandwidth, low-Q, Kiwi-SAS synthetic aperture sonar (SAS). Through the use of a very stable towfish, a new novel wide bandwidth transducer design, and autofocus procedures, high-resolution diffraction limited imagery is produced. As a complete system calibration was performed, this diffraction limited imagery is not only geometrically calibrated, it is also calibrated for target cross-section or target strength estimation. Is is important to note that the diffraction limited images are formed without access to any form of inertial measurement information. Previous investigations applying the synthetic aperture technique to sonar have developed processors based on exact, but inefficient, spatial-temporal domain time-delay and sum beamforming algorithms, or they have performed equivalent operations in the frequency domain using fast-correlation techniques (via the fast Fourier transform (FFT)). In this thesis, the algorithms used in the generation of synthetic aperture radar (SAR) images are derived in their wide bandwidth forms and it is shown that these more efficient algorithms can be used to form diffraction limited SAS images. Several new algorithms are developed; accelerated chirp scaling algorithm represents an efficient method for processing synthetic aperture data, while modified phase gradient autofocus and a low-Q autofocus routine based on prominent point processing are used to focus both simulated and real target data that has been corrupted by known and unknown motion or medium propagation errors.


title: The Crowdfunding Game

abstract: The recent success of crowdfunding for supporting new and innovative products has been overwhelming with over 34 Billion Dollars raised in 2015. In many crowdfunding platforms, firms set a campaign threshold and contributions are collected only if this threshold is reached. During the campaign, consumers are uncertain as to the ex-post value of the product, the business model viability, and the seller's reliability. Consumer who commit to a contribution therefore gambles. This gamble is effected by the campaign's threshold. Contributions to campaigns with higher thresholds are collected only if a greater number of agents find the offering acceptable. Therefore, high threshold serves as a social insurance and thus in high-threshold campaigns, potential contributors feel more at ease with contributing. We introduce the crowdfunding game and explore the contributor's dilemma in the context of experience goods. We discuss equilibrium existence and related social welfare, information aggregation and revenue implications.


title: Comparison of binding sites for wheat germ agglutinin on Raji lymphoblastoid cells and their isolated nuclei and plasma membranes.

abstract: Raji lymphoblastoid cells and the cell nuclei and plasma membranes isolated by the glycerol-lysis technique [Jett, M., Seed, T., & Jamieson, G. A. (1977) J. Biol. Chem. 252, 2134-2142] have been examined for their ability to bind wheat germ agglutinin. Intact cells and isolated nuclei showed similarities (i) in the total number of binding sites (3.38 X 10(6) and 4.06 X 10(8), respectively), indicating at least a 2-fold higher receptor density on the nuclei, (ii) in the ratios of the number of high-affinity sites and low-affinity sites (1.05 and 1.07), and (iii) in the apparent association constants at the high-affinity sites (28 nM and 48 nM) and at the low-affinity site (116 nM and 370 nM). Isolated plasma membranes had a similar number of total binding sites calculated on an equivalent cell basis (2.01 X 10(6)) but showed differences in the ratio of high- to low-affinity sites (1.5) and in their apparent association constants (3 nM and 22 nM). These results suggest similarities in the lectin receptors on the outer surface of lymphoblastoid cells and the cell nuclei. The differences obtained with isolated membranes may be due to inversion of the membrane vesicles or to their decreased rigidity as compared with the intact cell.


title: Congestion Control Design for Multipath Transport Protocols: A Survey

abstract: High-quality services over wired and wireless networks require high bitrate network content delivery. Multipath transport protocols utilize multiple paths to support increased throughput for network applications. Ubiquitous mobile devices with multiple interfaces such as WiFi and 4G/5G cellular, and data centers supporting big data analysis and cloud-computing, motivate adoption of multipath transmission in current and future network architectures. Congestion control mechanisms play a fundamental role in these multipath transport protocols. Diverse approaches have been proposed in the literature, differing in terms of their goals, principles, performance, and mostly in how various issues are addressed in their design. This paper presents a survey of congestion control solutions for multipath transport protocols and discusses the multipath congestion control design in order to address the need for some desirable properties including TCP-friendliness, load balancing, stability, and Pareto optimality. Existing window-based and rate-based multipath congestion control algorithms are investigated and categorized based on their theoretical algorithm design. Next, this paper discusses the congestion control mechanisms used in diverse multipath transport protocols in the context of different network scenarios. Finally, the trends in the future multipath congestion control design are presented.


title: Compact Directive Ultra-Wideband Rectangular Waveguide Based Antenna for Radar and Communication Applications

abstract: A new directive ultra-wideband antenna based on rectangular waveguide aperture antennas is presented. Conventional rectangular aperture antennas are limited by the single mode operation of the waveguide as well as relatively long and narrowband resonant feeds. To overcome these limitations, we developed a new rectangular waveguide antenna feeding structure that can establish an aperture field distribution very similar to that of the fundamental waveguide mode with a relatively short waveguide section and over two octaves of bandwidth. The overall antenna size is 0.55 λm × 0.27λm × 0.18 λm where λm is wavelength at the minimum operating frequency. Due to the planar aperture of the antenna, a polarizer could be added to the antenna to ensure good cross-polarization performance with fabrication tolerances. The proposed feed requires a balun structure, and thus an integrated balun design is presented. It is found that a standard balun would interact with the antenna structure and thus a modified design was implemented. The antenna was fabricated and measured. The antenna had VSWR <; 2.5 from 1.08 to 4.9 GHz and the measured gain showed almost constant aperture gain which increases from 5.3 dBi at the low frequency end to 11 dBi at the high frequency edge.


Key Factors Affecting User Experience of Mobile Crowdsourcing Applications


title: Distinct mechanisms regulate slow-muscle development

abstract: Vertebrate muscle development begins with the patterning of the paraxial mesoderm by inductive signals from midline tissues [1, 2]. Subsequent myotome growth occurs by the addition of new muscle fibers. We show that in zebrafish new slow-muscle fibers are first added at the end of the segmentation period in growth zones near the dorsal and ventral extremes of the myotome, and this muscle growth continues into larval life. In marine teleosts, this mechanism of growth has been termed stratified hyperplasia [3]. We have tested whether these added fibers require an embryonic architecture of muscle fibers to support their development and whether their fate is regulated by the same mechanisms that regulate embryonic muscle fates. Although Hedgehog signaling is required for the specification of adaxial-derived slow-muscle fibers in the embryo [4, 5], we show that in the absence of Hh signaling, stratified hyperplastic growth of slow muscle occurs at the correct time and place, despite the complete absence of embryonic slow-muscle fibers to serve as a scaffold for addition of these new slow-muscle fibers. We conclude that slow-muscle-stratified hyperplasia begins after the segmentation period during embryonic development and continues during the larval period. Furthermore, the mechanisms specifying the identity of these new slow-muscle fibers are different from those specifying the identity of adaxial-derived embryonic slow-muscle fibers. We propose that the independence of early, embryonic patterning mechanisms from later patterning mechanisms may be necessary for growth.


The apertium bilingual dictionaries on the web of data


Bioinformatics Track Predicting beneficial alleles using comparative genomics


title: System level analysis of fast, per-core DVFS using on-chip switching regulators

abstract: Portable, embedded systems place ever-increasing demands on high-performance, low-power microprocessor design. Dynamic voltage and frequency scaling (DVFS) is a well-known technique to reduce energy in digital systems, but the effectiveness of DVFS is hampered by slow voltage transitions that occur on the order of tens of microseconds. In addition, the recent trend towards chip-multiprocessors (CMP) executing multi-threaded workloads with heterogeneous behavior motivates the need for per-core DVFS control mechanisms. Voltage regulators that are integrated onto the same chip as the microprocessor core provide the benefit of both nanosecond-scale voltage switching and per-core voltage control. We show that these characteristics provide significant energy-saving opportunities compared to traditional off-chip regulators. However, the implementation of on-chip regulators presents many challenges including regulator efficiency and output voltage transient characteristics, which are significantly impacted by the system-level application of the regulator. In this paper, we describe and model these costs, and perform a comprehensive analysis of a CMP system with on-chip integrated regulators. We conclude that on-chip regulators can significantly improve DVFS effectiveness and lead to overall system energy savings in a CMP, but architects must carefully account for overheads and costs when designing next-generation DVFS systems and algorithms.


title: The importance of selenium to human health

abstract: The essential trace mineral, selenium, is of fundamental importance to human health. As a constituent of selenoproteins, selenium has structural and enzymic roles, in the latter context being best-known as an antioxidant and catalyst for the production of active thyroid hormone. Selenium is needed for the proper functioning of the immune system, and appears to be a key nutrient in counteracting the development of virulence and inhibiting HIV progression to AIDS. It is required for sperm motility and may reduce the risk of miscarriage. Deficiency has been linked to adverse mood states. Findings have been equivocal in linking selenium to cardiovascular disease risk although other conditions involving oxidative stress and inflammation have shown benefits of a higher selenium status. An elevated selenium intake may be associated with reduced cancer risk. Large clinical trials are now planned to confirm or refute this hypothesis. In the context of these health effects, low or diminishing selenium status in some parts of the world, notably in some European countries, is giving cause for concern.


A Parametric Human Body Modeling Tool


title: Endurance prediction and error Reduction in NAND flash using machine learning

abstract: NAND flash is rapidly becoming the media of choice for data storage, due in part to its speed and low power consumption. However, flash wears out through repeated program-erase (P-E) cycling, causing the raw bit error rate (RBER) to increase. Error correction codes (ECCs) are used to detect and correct errors in a sector of data called a codeword. An uncorrectable error occurs when the number of bit errors in a codeword exceeds a certain level, meaning the data cannot be recovered. This research uses machine learning to predict how far each sector of a NAND device can be cycled before it becomes uncorrectable by ECC, thereby enabling the controller to better manage the flash. We characterise a number of flash metrics to investigate their suitability for use as inputs to the prediction model, and determine the impact of each metric on the accuracy of the model. We show for the first time that it is possible to reliably make such predictions, achieving an overall accuracy of greater than 99% when the model was tested on unseen data.


title: What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication

abstract: Purpose ::: ::: ::: ::: ::: Enterprise social media platforms provide new ways of sharing knowledge and communicating within organizations to benefit from the social capital and valuable knowledge that employees have. Drawing on social dilemma and self-determination theory, the purpose of this paper is to understand what factors drive employees’ participation and what factors hamper their participation in enterprise social media. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Based on a literature review, a unified research model is derived integrating demographic, individual, organizational and technological factors that influence the motivation of employees to share knowledge. The model is tested using statistical methods on a sample of 114 respondents in Denmark. Qualitative data are used to elaborate and explain quantitative findings. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The findings pinpoint towards the general drivers and barriers to knowledge sharing within organizations. The significant drivers to knowledge sharing are: enjoy helping others, monetary rewards, management support, management encourages and motivates knowledge sharing behavior and knowledge sharing is recognized. The significant identified barriers are: change of behavior, lack of trust and lack of time. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The proposed knowledge sharing framework helps to understand what factors impact engagement on social media. Furthermore, the article suggests different types of interventions to overcome the social dilemma of knowledge sharing. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The study contributes to an understanding of factors leading to the success or failure of enterprise social media drawing on self-determination and social dilemma theory.


title: Histamine intolerance and dietary management: A complete review.

abstract: BACKGROUND ::: Present in several types of food, bioactive amines are described as organic bases of low molecular weight, which constitute a potential health risk. An awareness of amine levels in foods today is therefore important in relation to food safety and patient care. This review aims to emphasise the need to unify the information on the content of biogenic amines in foods and prevent patients' misunderstanding. ::: ::: ::: METHODS ::: Selective literature search for relevant publications in PubMed and other scientific data bases combined with further data from the World Wide Web on histamine and other amines content in foods. ::: ::: ::: RESULTS ::: Available reference sources do not reflect a homogeneous consensus, and the variation between foods makes it impossible for dieticians to accurately estimate amines content to correctly advise patients. ::: ::: ::: CONCLUSIONS ::: To achieve the goal of collecting reliable information, all methods and tools used in analytical studies should be standardised and information exposed to patients should be verified.


title: Linguistic Template Extraction for Recognizing Reader-Emotion

abstract: Previous studies on emotion classification mainly focus on the emotional state of the writer. By contrast, our research emphasizes emotion detection from the readers' perspective. The classification of documents into reader-emotion categories can be applied in several ways, and one of the applications is to retain only the documents that trigger desired emotions to enable users to retrieve documents that contain relevant contents and at the same time instill proper emotions. However, current information retrieval (IR) systems lack the ability to discern emotions within texts, and the detection of reader's emotion has yet to achieve a comparable performance. Moreover, previous machine learning-based approaches generally use statistical models that are not in a human-readable form. Thereby, it is difficult to pinpoint the reason for recognition failures and understand the types of emotions that the articles inspired on their readers. In this paper, we propose a flexible emotion template-based approach (TBA) for reader-emotion detection that simulates such process in a human perceptive manner. TBA is a highly automated process that incorporates various knowledge sources to learn an emotion template from raw text that characterize an emotion and are comprehensible for humans. Generated templates are adopted to predict reader's emotion through an alignment-based matching algorithm that allows an emotion template to be partially matched through a statistical scoring scheme. Experimental results demonstrate that our approach can effectively detect reader's emotions by exploiting the syntactic structures and semantic associations in the context, while outperforming currently well-known statistical text classification methods and the stat-of-the-art reader-emotion detection method.


title: Deep Learning Object Detection Methods for Ecological Camera Trap Data

abstract: Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. Ecological camera traps are a common approach for monitoring an ecosystem's animal population, as they provide continual insight into an environment without being intrusive. However, the analysis of camera trap images is expensive, labour intensive, and time consuming. Recent advances in the field of deep learning for object detection show promise towards automating the analysis of camera trap images. Here, we demonstrate their capabilities by training and comparing two deep learning object detection classifiers, Faster R-CNN and YOLO v2.0, to identify, quantify, and localize animal species within camera trap images using the Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data sets. When trained on large labeled datasets, object recognition methods have shown success. We demonstrate their use, in the context of realistically sized ecological data sets, by testing if object detection methods are applicable for ecological research scenarios when utilizing transfer learning. Faster R-CNN outperformed YOLO v2.0 with average accuracies of 93.0\% and 76.7\% on the two data sets, respectively. Our findings show promising steps towards the automation of the labourious task of labeling camera trap images, which can be used to improve our understanding of the population dynamics of ecosystems across the planet.


title: A platform surveillance monitoring system using image processing for passenger safety in railway station

abstract: In this paper, we propose a platform surveillance monitoring system using image processing technology for passenger safety in railway station. The proposed system monitors almost entire length of the track line in the platform by using multiple cameras, and determines in real-time whether a human or dangerous obstacle is in the preset monitoring area by using image processing technology. According to the experimental results, we verity system performance in real condition. Detection of train state and object is conducted robustly by using proposed image processing algorithm. Moreover, to deal with the accident immediately, the system provides local station, central control room and train with the video information and alarm message.


title: Auctions for task allocation to robots

abstract: We present an auction-based method for dynamic allocation of tasks to robots. The robots operate in a 2D environment for which they have a map. Tasks are locations in the map that have to be visited by the robots, in any order. Unexpected obstacles and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are rebid every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall time for task completion. We present experimental results that we have obtained in simulation using Player/Stage.


title: Dwell Time Prediction Model for Minimizing Unnecessary Handovers in Heterogenous Wireless Networks, Considering Amoebic Shaped Coverage Region

abstract: Over the years, vertical handover necessity estimation has attracted the interest of numerous researchers. Despite the attractive benefits of integrating different wireless platforms, mobile users are confronted with the issue of detrimental handover. This paper used extensive geometric and probability analysis in modelling the coverage area of a WLAN cell. Thus, presents a realistic and novel model with an attempt to minimize unnecessary handover and handover failure of a mobile node (MN) traversing the WLAN cell from a third generation (3G) network. The dwell time is estimated along with the threshold values to ensure an optimal handover decision by the MN, while the probability of unnecessary handover and handover failure are kept within tolerable bounds. Monte-Carlo simulations were carried out to show the behavior of the proposed model. Results were validated by comparing this model with existing models for unnecessary handover minimization.


title: Learning mid-level features for recognition

abstract: Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature extractors, our approach aims to facilitate the design of better recognition architectures.


title: Profile View Lip Reading

abstract: In this paper, we introduce profile view (PV) lip reading, a scheme for speaker-dependent isolated word speech recognition. We provide historic motivation for PV from the importance of profile images in facial animation for lip reading, and we present feature extraction schemes for PV as well as for the traditional frontal view (FV) approach. We compare lip reading results for PV and FV, which demonstrate a significant improvement for PV over FV. We show improvement in speech recognition with the integration of audio and visual features. We also found it advantageous to process the visual features over a longer duration than the duration marked by the endpoints of the speech utterance.


title: Predicting Learning Behavior of Students using Classification Techniques

abstract: The main objective of any educational organization is to provide quality education and improve the overall performance of an institution by looking at individual performances. One way to analyze learners' performances is to identify the areas of weakness and guide their students to a better future. Although data mining has been successful in many areas, its use in student performance analysis is still relatively new, i.e. the knowledge is hidden in educational data set and it is extracted using data mining techniques. This paper discusses about a learning model for predicting student performance using classification techniques. Also the paper shows the comparative performance analysis of J48, Naive Bayesian classifier and Random forest algorithm.


title: GPS/IMU Data Fusion using Multisensor Kalman Filtering: Introduction of Contextual Aspects.

abstract: The aim of this article is to develop a GPS/IMU multisensor fusion algorithm, taking context into consideration. Contextual variables are introduced to define fuzzy validity domains of each sensor. The algorithm increases the reliability of the position information. A simulation of this algorithm is then made by fusing GPS and IMU data coming from real tests on a land vehicle. Bad data delivered by GPS sensor are detected and rejected using contextual information thus increasing reliability. Moreover, because of a lack of credibility of GPS signal in some cases and because of the drift of the INS, GPS/INS association is not satisfactory at the moment. In order to avoid this problem, the authors propose to feed the fusion process based on a multisensor Kalman filter directly with the acceleration provided by the IMU. Moreover, the filter developed here gives the possibility to easily add other sensors in order to achieve performances required.


title: Speech To Text Engine For Jawi Language

abstract: This paper focused on the development of speech translation to special character that is Malay speech to Jawi text engine. Jawi is a unique character derived from Arabic but it is read in Malay language. There are not many research can be found on speech technology developed for Jawi and this research would be useful to researcher who wish to venture its benefit to many related ICT applications. The use of Zero Crossing Rate (ZCR) as a robust algorithm for accurate automatic detection of speech signal syllable boundary has been discussed. The combination of Linear Predictive Coding (LPC) and Artificial Neural Network (ANN) are used in this research to extract and classify the speech signals with backpropagation training method. This paper also, discussed on the use of Jawi Unicode in the final character tagging process to represent each of the Jawi character existed in the spoken word. As there are no standard lists of Jawi Unicode published, in this research, the existing of Jawi Unicode table produced by previous research is further investigated and enhanced in order to have better accuracy in Jawi character1phoneme representation. This list is based on the combination of Traditional Arabic and other scripts. A prototype educational learning tool was also, developed to enable school children to recognize and read Jawi text, check their pronunciation, and learn from their mistakes independently.


title: A Novel High Efficiency High Power Interleaved Coupled-Inductor Boost DC-DC Converter for Hybrid and Fuel Cell Electric Vehicle

abstract: In this paper an efficient high power high-step up dc- dc converter is presented for interfacing distributed energy storage elements like, fuel cells, batteries, and ultracapacitors with the high voltage dc bus in Electrical Vehicles (EV) and Hybrid Electric Vehicles (HEV). In the proposed converter, coupled-inductor boost converters are interleaved and a boost converter is used to clamp the switch voltage of the interleaved converters at a lower voltage. Leakage energy of all the interleaved converters are gathered in one clamping capacitor and discharged to the output by the clamping boost converter. The interleaving and low voltage clamping of coupled-inductor boost converters enable to achieve high efficiency. Design and analysis of the proposed converter are presented. Finally simulation results are provided for verification of the proposed converter.


title: Generating Clues for Gender based Occupation De-biasing in Text

abstract: Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically. However, these AI models also learn gender, racial and ethnic biases present in the training data. In this paper, we present the first system that discovers the possibility that a given text portrays a gender stereotype associated with an occupation. If the possibility exists, the system offers counter-evidences of opposite gender also being associated with the same occupation in the context of user-provided geography and timespan. The system thus enables text de-biasing by assisting a human-in-the-loop. The system can not only act as a text pre-processor before training any AI model but also help human story writers write stories free of occupation-level gender bias in the geographical and temporal context of their choice.


title: Control of a Quadrotor Helicopter Using Dual Camera Visual Feedback

abstract: In this paper we propose a vision-based stabilization and output tracking control method for a model helicopter. A novel two-camera method is introduced for estimating the full six-degrees-of-freedom pose of the helicopter. One of these cameras is located on-board the helicopter, and the other camera is located on the ground. Unlike previous work, these two cameras are set to see each other. The pose estimation algorithm is compared in simulation to other methods and is shown to be less sensitive to errors on feature detection. In order to build an autonomous helicopter, two methods of control are studied: one using a series of mode-based, feedback linearizing controllers and the other using a backstepping-like control law. Various simulations demonstrate the implementation of these controllers. Finally, we present flight experiments where the proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote-controlled helicopter.


title: Patient-reported outcomes item selection for bladder cancer patients in chemo- or immunotherapy

abstract: Selection of specific patient-reported outcomes (PROs) for cancer patients requires careful consideration to the purpose and population at aim. Here we report the process of choosing which items to include in a bladder cancer population in chemo- or immunotherapy based on the Patient-Reported Outcomes Version of the Common Terminology Criteria of Adverse Events (PRO-CTCAE). Initial PRO-CTCAE symptoms were chosen through 1) medical record audit 2) patient interviews 3) summary of product characteristics from European Medicines Agency and Food and Drug Administration for the applied chemotherapies, and 4) toxicity reporting from Phase 2 and 3 trials for immunotherapies applied in patients with urothelial cancer. The selected questions were applied in a prospective cohort of 78 bladder cancer patients receiving chemo- or immunotherapy at Rigshospitalet and Herlev Hospital, Denmark. Symptoms tested in this population were selected for the final module if they appeared in ≥3 of the following groupings a) the most prevalent PRO-CTCAE symptoms grade ≥ 2 overall during treatment b) the PRO-CTCAE symptoms reported in conjunction with hospital admissions or mentioned in focus group interviews discussing which symptoms were prevalent in this patient group with specialized c) nurses or d) physicians. The authors also included symptoms in the final module if they were present in two of the above groups and defined as actionable by clinicians. From the initial selection of PRO-CTCAE symptoms, a total of 45 PRO-CTCAE symptoms explored by 84 PRO-CTCAE questions were retrieved. Through the second selection process based on the described criteria, the study group agreed on 15 PRO-CTCAE symptoms explored by 30 PRO-CTCAE items to be appropriate and relevant for the bladder population during medical oncological treatment. The selection of disease specific PROs in a bladder cancer population was feasible. The process revealed several steps of selection needed in order to reach a final module for clinical application.


title: ERP implementation: lessons from a case study

abstract: Many organizations have moved from stand‐alone business information systems applications to integrated enterprise‐wide systems, enterprise resource planning (ERP). The implementation of ERP packages has created an opportunity to re‐engineer business processes within and beyond the organizational scope. Most notably, SAP R/3 has been widely implemented to create value‐oriented business processes that enable a high level of integration, improve communication within internal and external business networks, and enhance the decision‐making process. Though many organizations have reported dramatic improvements from SAP R/3 implementation, others have experienced difficulties in getting the R/3 modules aligned with other business components and systems. This paper describes a case study of a failed implementation of SAP R/3 to re‐engineer the business processes of a major manufacturer. Lessons in terms of factors that led to failure and their future implications are discussed in the light of the contrasting expe...


Word Attention for Sequence to Sequence Text Understanding


title: Cost-Benefit Analysis of China's Internet Content Regulation

abstract: As illegal and unhealthy content on the Internet has gradually increased in recent years, there have been constant calls for Internet content regulation. But any regulation comes at a cost. Based on the principles of the cost-benefit theory, this article conducts an in-depth discussion on China’s current Internet content regulation, so as to reveal its latent patterns.


title: Are we waves or are we particles? A new insight into deep semantics in natural language processing

abstract: This paper brings conceptually new, empirically based scientific approach to a deeper understanding of human mind cognition, language acquisition, modularity of language and language origin itself. The research presented provides an interactive multilingual associative experiment as an attempt to map the Cognitive Semantic Space: (CSSES) and its basic frames of the Essential Self in the Czech language, collects and compares it to the CSSES of conceptual language view in Czech, Russian, English and potentially in other languages. We attempt to merge cognitive metaphor theory with psycholinguistics and psychoanalysis applying associative experiment methodology on the Essential Self metaphors. The research has two main goals: the first is to build an Essential Self multilingual WordNet, which serves as the basic lexical resource for Artificial Intelligence describes the core of the human nature. The second is to create a multilingual 3D semantic network.


title: A novel multicriteria group decision making approach with intuitionistic fuzzy sir method

abstract: The superiority and inferiority ranking (SIR) method is a generation of the well-known PROMETHEE method, which can be more efficient to deal with multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets (IFSs), as an important extension of fuzzy sets (IFs), include both membership functions and non-membership functions and can be used to, more precisely describe uncertain information. In real world, decision situations are usually under uncertain environment and involve multiple individuals who have their own points of view on handing of decision problems. In order to solve uncertainty group MCDM problem, we propose a novel intuitionistic fuzzy SIR method in this paper. This approach uses intuitionistic fuzzy aggregation operators and SIR ranking methods to handle uncertain information; integrate individual opinions into group opinions; make decisions on multiple-criterion; and finally structure a specific decision map. The proposed approach is illustrated in a simulation of group decision making problem related to supply chain management.


title: Simulation of a 6/4 switched reluctance motor based on Matlab/Simulink environment

abstract: A Matlab/Simulink environment to simulate a 6/4-switched reluctance motor is described. From its linear model to the nonlinear model, its dynamics is described and discussed in detail. All simulations are completely documented by their block diagrams and corresponding special Matlab functions and parameters quickly develop its model to the reader. Based on the developed model, simulation studies are performed and compared with measured motor phase currents either for hysteresis and voltage control strategies, and the steady-state motor operation to validate the model.


title: DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Interestingness Measures for Knowledge Discovery

abstract: It is a well-known fact that the data mining process can generate many hundreds and often thousands of patterns from data. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth. This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the data mining process. These so-called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures that are derived from the user's beliefs or expectations of their particular problem domain. We evaluate the strengths and weaknesses of the various interestingness measures with respect to the level of user integration within the discovery process.


title: Selecting The Best ERP system for SMEs using a combination of ANP and PROMETHEE methods

abstract: ERP system selection is a complex multi-criteria decision making process.Imprecision and large-number of criteria are among the most important challenges.Use of a hybrid methodology improves the decision outcomes.ANP and PROMETHEE are collectively and synergistically used in this study.The proposed methodology produced satiating results for SMEs. Enterprise Resource Planning (ERP) system, which integrates all of the units within an organization at the information level, plays an important role for a successful enterprise. With the right ERP system, it is easier to provide coordination between the units, eliminate waste and make faster and better decisions. Adopting an ERP system is a significant investment decision for a firm, therefore a great deal of attention should be given to the selection of the right system. Since there are a large number of criteria to consider in selecting an ERP system, the process itself is regarded as a complex multi-criteria decision making problem. In this study, two prevalent multi-criteria decision making techniques, Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), are used in combination to better address the ERP selection problem. First, ANP is used to determine the weights of all criteria, and then, the obtained weights are used in the PROMETHEE method for optimal ranking of the alternative system choices. To demonstrate the viability of the proposed methodology, an application case is performed on the ERP selection problem for the Small Medium Enterprises (SMEs) in Istanbul, Turkey. The proposed hybrid methodology successfully ranked the alternatives and identified the best ERP system based on the information obtained from a number of SMEs participated in this study.


title: A first step toward understanding inter-domain routing dynamics

abstract: BGP updates are triggered by a variety of events such as link failures, resets, routers crashing, configuration changes, and so on. Making sense of these updates and identifying the underlying events is key to debugging and troubleshooting BGP routing problems. In this paper, as a first step toward the much harder problem of root cause analysis of BGP updates, we discuss if, and how, updates triggered by distinct underlying events can be separated. Specifically, we explore using PCA (Principal Components Analysis), a well known statistical multi-variate technique, to achieve this goal.We propose a method based on PCA to obtain a set of clusters from a BGP update stream; each of these is a set of entities (either prefixes or ASes) which are affected by the same underlying event. Then we demonstrate our approach using BGP data obtained by simulations and show that the method is quite effective. In addition, we perform a high level analysis of BGP data containing well known, large scale events.


title: Debugging optimized code with dynamic deoptimization

abstract: SELF's debugging system provides complete source-level debugging ( expected behavior ) with globally optimized code. It shields the debugger from optimizations performed by the compiler by dynamically deoptimizing code on demand. Deoptimization only affects the procedure activations that are actively being debugged; all other code runs at full speed. Deoptimization requires the compiler to supply debugging information at discrete interrupt points ; the compiler can still perform extensive optimizations between interrupt points without affecting debuggability. At the same time, the inability to interrupt between interrupt points is invisible to the user. Our debugging system also handles programming changes during debugging. Again, the system provides expected behavior: it is possible to change a running program and immediately observe the effects of the change. Dynamic deoptimization transforms old compiled code (which may contain inlined copies of the old version of the changed procedure) into new versions reflecting the current source-level state. To the best of our knowledge, SELF is the first practical system providing full expected behavior with globally optimized code.


title: Analysis of Temporal Noise in CMOS Photodiode Active Pixel Sensor

abstract: Temporal noise sets the fundamental limit on image sensor performance, especially under low illumination and in video applications. In a CCD image sensor, temporal noise is primarily due to the photodetector shot noise and the output amplifier thermal and 1/f noise. CMOS image sensors suffer from higher noise than CCDs due to the additional pixel and column amplifier transistor thermal and 1/f noise. Noise analysis is further complicated by the time-varying circuit models, the fact that the reset transistor operates in subthreshold during reset, and the nonlinearity of the charge to voltage conversion, which is becoming more pronounced as CMOS technology scales. The paper presents a detailed and rigorous analysis of temporal noise due to thermal and shot noise sources in CMOS active pixel sensor (APS) that takes into consideration these complicating factors. Performing time-domain analysis, instead of the more traditional frequency-domain analysis, we find that the reset noise power due to thermal noise is at most half of its commonly quoted kT/C value. This result is corroborated by several published experimental data including data presented in this paper. The lower reset noise, however, comes at the expense of image lag. We find that alternative reset methods such as overdriving the reset transistor gate or using a pMOS transistor can alleviate lag, but at the expense of doubling the reset noise power. We propose a new reset method that alleviates lag without increasing reset noise.


title: Selective Symbolic Execution

abstract: Symbolic execution is a powerful technique for analyzing program behavior, finding bugs, and generating tests, but suffers from severely limited scalability: the largest programs that can be symbolically executed today are on the order of thousands of lines of code. To ensure feasibility of symbolic execution, even small programs must curtail their interactions with libraries, the operating system, and hardware devices. This paper introduces selective symbolic execution, a technique for creating the illusion of full-system symbolic execution, while symbolically running only the code that is of interest to the developer. We describe a prototype that can symbolically execute arbitrary portions of a full system, including applications, libraries, operating system, and device drivers. It seamlessly transitions back and forth between symbolic and concrete execution, while transparently converting system state from symbolic to concrete and back. Our technique makes symbolic execution practical for large software that runs in real environments, without requiring explicit modeling of these environments.


title: Zooplankton distribution in flowing waters and its implications for sampling : case studies in the River Meuse (Belgium) and the River Moselle (France, Luxembourg)

abstract: Zooplankton spatial distribution was studied on four occasions by transect sampling on the rivers Moselle and Meuse in July 1996. Sampling sites were selected for their variety in morphology. A pseudoreplicate-based sampling scheme was adopted that allowed small-scale longitudinal vari- ations in density to be tested along with transversal position and depth. In the Moselle, zooplankton were unevenly distributed transversally, an observation tentatively linked with the complexity of the river channel and the influence of tributaries. In the Meuse, spatial heterogeneity was stronger, and depth also played a key role. Its effect was different for rotifers and microcrustaceans. The occurrence of zooplankton patches, similar to those commonly reported from lakes, was noted. The factors leading to the establishment of such distribution patterns, and their relevance to routine sampling, are briefly discussed; a short list of recommendations for sampling in large rivers is proposed.


title: Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

abstract: We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet). Given a high-performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed SynNet with a pretrained model from the SQuAD dataset on the challenging NewsQA dataset, we achieve an F1 measure of 44.3% with a single model and 46.6% with an ensemble, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-of-domain baseline of 7.6%, without use of provided annotations.


title: Assessing the maintainability of XCON-in-RIME: coping with the problems of a VERY large rule-base

abstract: XCON is a rule-based expert system that configures computer systems. Over 7 years, XCON has grown to 6,200 rules, of which approximately 50% change every year. While the performance of XCON is satisfactory, it is increasingly becoming more difficult to change. With the goal of facilitating maintenance, DEC has developed a new rule-based language, RIME, in which the successor to XCON, XCON-in-RIME, is being written. This paper evaluates the potential for enhanced maintainability of XCON-in-RIME over XCON.


title: Pictures at the ATM: exploring the usability of multiple graphical passwords

abstract: Users gain access to cash, confidential information and services at Automated Teller Machines (ATMs) via an authentication process involving a Personal Identification Number (PIN). These users frequently have many different PINs, and fail to remember them without recourse to insecure behaviours. This is not a failing of users. It is a usability failing in the ATM authentication mechanism. This paper describes research executed to evaluate whether users find multiple graphical passwords more memorable than multiple PINs. The research also investigates the success of two memory augmentation strategies in increasing memorability of graphical passwords. The results demonstrate that multiple graphical passwords are substantially more effective than multiple PIN numbers. Memorability is further improved by the use of mnemonics to aid their recall.This study will be of interest to HCI practitioners and information security researchers exploring approaches to usable security.


title: Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016

abstract: In the past few decades heart sound signals (i.e., phono-cardiograms or PCGs) have been widely studied. Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of a large and open database of heart sound recordings. The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 addresses this issue by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world. The database includes 4,430 recordings taken from 1,072 subjects, totalling 233,512 heart sounds collected from both healthy subjects and patients with a variety of conditions such as heart valve disease and coronary artery disease. These recordings were collected using heterogeneous equipment in both clinical and nonclinical (such as in-home visits). The length of recording varied from several seconds to several minutes. Additional data provided include subject demographics (age and gender), recording information (number per patient, body location, and length of recording), synchronously recorded signals (such as ECG), sampling frequency and sensor type used. Participants were asked to classify recordings as normal, abnormal, or not possible to evaluate (noisy/uncertain). The overall score for an entry was based on a weighted sensitivity and specificity score with respect to manual expert annotations. A brief description of a baseline classification method is provided, including a description of open source code, which has been provided in association with the Challenge. The open source code provided a score of 0.71 (Se=0.65 Sp=0.76). During the official phase of the competition, a total of 48 teams submitted 348 open source entries, with a highest score of 0.86 (Se=0.94 Sp=0.78).


title: PELESent: Cross-Domain Polarity Classification Using Distant Supervision

abstract: The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.


title: Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm

abstract: Abstract In recent years, due to large integration of Renewable Energy Sources (RESs) like wind turbine and photovoltaic unit into the Micro-Grid (MG), the necessity of Battery Energy Storage (BES) has increased dramatically. The BES has several benefits and advantages in the MG-based applications such as short term power supply, power quality improvement, facilitating integration of RES, ancillary service and arbitrage. This paper presents the cost-based formulation to determine the optimal size of the BES in the operation management of MG. Also, some restrictions, i.e. power capacity of Distributed Generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction should be considered as well. The suggested problem is a complicated optimization problem, the complexity of which is increased by considering the above constraints. Therefore, a robust and strong optimization algorithm is required to solve it. Herein, this paper proposes a new evolutionary technique named improved bat algorithm that is used for developing corrective strategies and to perform least cost dispatches. The performance of the approach is evaluated by one grid-connected low voltage MG where the optimal size of BES is determined professionally.


Mining Semantic Loop Idioms from Big Code


title: Optimization of a 5-kW Telecom Phase-Shift DC–DC Converter With Magnetically Integrated Current Doubler

abstract: The development of new converter systems with higher power density and/or efficiency offers many degrees of freedom for the design parameters as a large number of system component values have to be defined, where all of which are interdependent, to some extent, on another. Therefore, an automated optimization procedure, based on comprehensive analytical models and the resulting optimal design parameters, e.g., switching frequency or transformer design, to achieve the most compact and/or efficient design, is presented in this paper. In addition, the design of a volume-optimized 400/46-56-V phase-shift dc-dc converter with a current doubler rectifier and the underlying analytical models are also introduced. The power density of the converter is increased by integrating the output inductors in the transformer's core. A new lossless magnetic snubber circuit is introduced, which damps the intrinsic voltage ringing of the rectifier diodes and feeds the ringing energy to the output. The experimental results prove the analytical models and the design procedure. The 5-kW dc-dc converter prototype has a power density of 147 W/in3 (9 kW/L) and a maximum efficiency of 94.4% at 54-V output voltage and full load.


title: COCO Attributes: Attributes for People, Animals, and Objects

abstract: In this paper, we discover and annotate visual attributes for the COCO dataset. With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories – for example, rendering multi-label classifications such as “sleeping spotted curled-up cat” instead of simply “cat”. To overcome the expense of annotating thousands of COCO object instances with hundreds of attributes, we present an Economic Labeling Algorithm (ELA) which intelligently generates crowd labeling tasks based on correlations between attributes. The ELA offers a substantial reduction in labeling cost while largely maintaining attribute density and variety. Currently, we have collected 3.5 million object-attribute pair annotations describing 180 thousand different objects. We demonstrate that our efficiently labeled training data can be used to produce classifiers of similar discriminative ability as classifiers created using exhaustively labeled ground truth. Finally, we provide baseline performance analysis for object attribute recognition.


title: Review on Agile requirements engineering challenges

abstract: The popularity of agile methodology has been increased since agile offers further collaboration. Requirements engineering (RE) is one of the software development process in agile approaches. The features of face to face communication is one of agile way which unlike to other traditional software methods. Software developers are using the definition agile way to improve the agile way of activities. Besides, the challenge of dealing with RE practices is not much is known in agile way or model. However, many literature works have been done in RE in agile approaches and challenges faced by teamwork. The aim of this paper is to fill the gap by presenting RE practices in agile methodology also the challenges of RE activities which are faced by agile team members. 80 systematic literature reviews are conducted and 22 research papers are identified. Moreover, the reviews discuss RE in agile methodology approaches. This paper has adopted a systematic literature review and has applied a certain inclusion and exclusion criteria to detect the research studies related to RE in agile methodology to achieve the aim of this research. This research determined most of agile requirements engineering activities, also challenges of requirements engineering in agile methodology. Therefore, the research findings can be a reference to all researchers who are interested in this research area because the paper's findings clarify that RE in agile methodology need to have further attention by researchers and more practical results are needed to gain understandable information about RE practices in Agile methodology.


title: A Low Cost Home Automation System Using Wi-Fi Based Wireless Sensor Network Incorporating Internet of Things (IoT)

abstract: With the rapid increase in usage and reliance on the vivid features of smart devices, the need for interconnecting them is genuine. Many existing systems have ventured into the sphere of Home Automation but have apparently failed to provide cost-effective solutions for the same. This paper illustrates a methodology to provide a low cost Home Automation System (HAS) using Wireless Fidelity (Wi-Fi). This crystallizes the concept of internetworking of smart devices. A Wi-Fi based Wireless Sensor Network(WSN) is designed for the purpose of monitoring and controlling environmental, safety and electrical parameters of a smart interconnected home. The user can exercise seamless control over the devices in a smart home via the Android application based Graphical User Interface (GUI) on a smartphone. The overall cost of large scale implementation of this system is about INR 6000 or USD 100.


title: Real Behavior in Virtual Environments: Psychology Experiments in a Simple Virtual-Reality Paradigm Using Video Games

abstract: Abstract The purpose of this research was to illustrate the broad usefulness of simple video-game-based virtual environments (VEs) for psychological research on real-world behavior. To this end, this research explored several high-level social phenomena in a simple, inexpensive computer-game environment: the reduced likelihood of helping under time pressure and the bystander effect, which is reduced helping in the presence of bystanders. In the first experiment, participants had to find the exit in a virtual labyrinth under either high or low time pressure. They encountered rooms with and without virtual bystanders, and in each room, a virtual person requested assistance. Participants helped significantly less frequently under time pressure but the presence/absence of a small number of bystanders did not significantly moderate helping. The second experiment increased the number of virtual bystanders, and participants were instructed to imagine that these were real people. Participants helped significantly...


title: StackMap: low-latency networking with the OS stack and dedicated NICs

abstract: StackMap leverages the best aspects of kernel-bypass networking into a new low-latency Linux network service based on the full-featured TCP kernel implementation, by dedicating network interfaces to applications and offering an extended version of the netmap API as a zero-copy, low-overhead data path while retaining the socket API for the control path. For small-message, transactional workloads, StackMap outperforms baseline Linux by 4 to 80% in latency and 4 to 391% in throughput. It also achieves comparable performance with Seastar, a highly-optimized user-level TCP/IP stack for DPDK.


title: Early bilingualism enhances mechanisms of false-belief reasoning

abstract: In their first years, children’s understanding of mental states seems to improve dramatically, but the mechanisms underlying these changes are still unclear. Such ‘theory of mind’ (ToM) abilities may arise during development, or have an innate basis, developmental changes reflecting limitations of other abilities involved in ToM tasks (e.g. inhibition). Special circumstances such as early bilingualism may enhance ToM development or other capacities required by ToM tasks. Here we compare 3-yearold bilinguals and monolinguals on a standard ToM task, a modified ToM task and a control task involving physical reasoning. The modified ToM task mimicked a language-switch situation that bilinguals often encounter and that could influence their ToM abilities. If such experience contributes to an early consolidation of ToM in bilinguals, they should be selectively enhanced in the modified task. In contrast, if bilinguals have an advantage due to better executive inhibitory abilities involved in ToM tasks, they should outperform monolinguals on both ToM tasks, inhibitory demands being similar. Bilingual children showed an advantage on the two ToM tasks but not on the control task. The precocious success of bilinguals may be associated with their well-developed control functions formed during monitoring and selecting languages.


Very Effective Evolutionary Techniques for Searching Cellular Automata Rule Spaces


title: The Simulation Method of the Galvanic Coupling Intrabody Communication With Different Signal Transmission Paths

abstract: The simulation method plays an important role in the investigation of the intrabody communication (IBC). Due to the problems of the transfer function and the corresponding parameters, only the simulation of the galvanic coupling IBC along the arm has been achieved at present. In this paper, a method for the mathematical simulation of the galvanic coupling IBC with different signal transmission paths has been introduced. First, a new transfer function of the galvanic coupling IBC was derived with the consideration of the internal resistances of the IBC devices. Second, the determination of the corresponding parameters used in the transfer function was discussed in detail. Finally, both the measurements and the simulations of the galvanic coupling IBC along the different signal transmission paths were carried out. Our investigation shows that the mathematical simulation results coincide with the measurement results over the frequency range from 100 kHz to 5 MHz, which indicates that the proposed method offers the significant advantages in the theoretical analysis and the application of the galvanic coupling IBC.


title: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

abstract: Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude.


title: The Cloud2SM Project

abstract: From the past decades the monitoring of civil engineering structure became a major field of research and ::: development process in the domains of modelling and integrated instrumentation. This increasing of interest can ::: be attributed in part to the need of controlling the aging of such structures and on the other hand to the need to ::: optimize maintenance costs. From this standpoint the project Cloud2SM (Cloud architecture design for Structural ::: Monitoring with in-line Sensors and Models tasking), has been launched to develop a robust information system ::: able to assess the long term monitoring of civil engineering structures as well as interfacing various sensors and ::: data. The specificity of such architecture is to be based on the notion of data processing through physical or ::: statistical models. Thus the data processing, whether material or mathematical, can be seen here as a resource of ::: the main architecture.


title: Is smartphone addiction really an addiction?

abstract: AimsIn light of the rise in research on technological addictions and smartphone addiction in particular, the aim of this paper was to review the relevant literature on the topic of smartphone addic...


title: Flywheel energy storage systems for power systems application

abstract: The ever increasing penetration of renewable and distributed electricity generation in power systems involves to manage their increased complexity, as well as to face an increased demand for stability and power quality. From this viewpoint, the energy storage plays a key role in the reliability and power quality of the power systems. Several energy storage technologies have been recently adopted to meet the various demands of power systems. Among them, due to their advantages of rapid high round trip energy efficiency and long cycle life, flywheel energy storage systems are today used in load leveling, frequency regulation, peak shaving and transient stability. This paper reports an in-depth review of existing flywheel energy storage technologies and structures, including the subsystems and the required components. The performance metrics in designing and manufacturing of flywheel-based energy storages in power systems, along with safety and cost considerations, are also discussed. Finally, challenges and trends of flywheel energy storage solutions in future scenarios are presented.


title: Assessment of Implementation of a CAMBRA-Based Program in a Dental School Environment

abstract: Caries development is determined by a balance of protective and pathological factors, so the clinician should be able to identify and document those factors, understand their relative weight in disease development or reversal, and make recommendations to patients that will lead to risk reduction. The caries management by risk assessment (CAMBRA) protocol frames these factors into an easy-to-follow template that also guides the clinician in making recommendations. The purposes of this study were to examine implementation of the CAMBRA-based risk assessment program in a predoctoral clinic at one dental school, assess the accuracy of caries risk evaluation by the students, and evaluate the utilization of professionally applied fluoride varnish in a moderate- and high-risk patient cohort. After dental clinic patients were screened for previous caries risk status, sixty-eight moderate- or high-risk patients were invited to participate in the study. At the study visit that included four bite-wing radiographs, a new caries risk assessment (CRA) form was completed. Our results showed that students underestimated the risk in 25 percent of the cases; the underestimation occurred especially when visible cavitation or caries into dentin by radiograph was the only risk factor or when caries were not identified at the initial visit when the CRA form was completed for the first time despite the presence of other high-risk factors. Students also underestimated both risk and protective factors at the initial evaluation visit compared with the study visit. The results show that students were not rigorous enough in documenting these factors and determining the patient’s risk. In order to increase the sensitivity of risk assessment, training and recalibration for students and faculty members should be an ongoing process.


title: Quantifying Interdependent Privacy Risks with Location Data

abstract: Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users’ IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users’ locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62 percent in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.


title: Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots

abstract: Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top $K$ suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.


title: High-Efficiency Compact Circularly Polarized Microstrip Antenna With Wide Beamwidth for Airborne Communication

abstract: The letter presents a compact circularly polarized (CP) microstrip antenna for airborne communications. The antenna adopts a square radiation patch with a cross slot to reduce the antenna dimensions and improve the impedance matching. For the high radiation efficiency of the proposed antenna, the no-resistor series feeding network is utilized to construct the antenna. In addition, the proposed structure also brings a super-wide beamwidth in axial ratio (AR) radiation patterns. To confirm the performances of the novel structure, an L-band prototype of the antenna is manufactured and tested. The thickness and the side length of the antenna are $0.073{\lambda _0}$ and $0.275{\lambda _0}$ , respectively ( ${\lambda _0}$ is the wavelength in free space). The impedance bandwidth for ${\rm VSWR} \leq 2 $ reaches 22.4%, and the AR bandwidth for ${\rm AR}\leq3\ \hbox{dB}$ reaches 6.8%. In the overlap band, the radiation efficiency of more than 97% and the gains of more than 5.2 dBic are obtained. Especially, the beamwidth for ${\rm AR} \leq 3 \ \hbox{dB} $ can cover more than 180°.


title: A Distributed-Ledger Consortium Model for Collaborative Innovation

abstract: R3 has built a global consortium to focus on the application of distributed-ledger technology (DLT), which can help banks combat low return on equity and alleviate pressure on their operating costs. The authors explain the conditions that led to interest in DLT and introduce Corda, R3’s shared ledger for recording and managing financial agreements.


title: Who Can Survive in an ICT-Enabled Crowdfunding Platform?

abstract: The importance of socio-technical ecosystems is growing due to the emergence of technology-based platform businesses. However, few researchers have offered theoretical explanations of this phenomenon drawing on the ecosystem perspective. Existing studies on ecosystems have been limited to either natural or social ecosystems and have examined ecosystems as a whole. This study focuses on the survival and evolution of individual participants in the socio-technical ecosystem of a crowdfunding platform. It is hypothesized that adaptability (i.e., intra-role and inter-role exchange) and relationality (i.e., feedback and feed-forward interactivity) are positively related to the amount of funding received and the likelihood of campaign success. Empirical results from regression analysis show that the quality of intra-role and inter-role exchanges determine their influence on funding success. High relationality has a significant, positive influence on the funding received by a campaign. With this insight, this paper lays the groundwork for expanding theoretical research on socio-technical ecosystems.


title: Continuous Learning of Human Activity Models Using Deep Nets

abstract: Learning activity models continuously from streaming videos is an immensely important problem in video surveillance, video indexing, etc. Most of the research on human activity recognition has mainly focused on learning a static model considering that all the training instances are labeled and present in advance, while in streaming videos new instances continuously arrive and are not labeled. In this work, we propose a continuous human activity learning framework from streaming videos by intricately tying together deep networks and active learning. This allows us to automatically select the most suitable features and to take the advantage of incoming unlabeled instances to improve the existing model incrementally. Given the segmented activities from streaming videos, we learn features in an unsupervised manner using deep networks and use active learning to reduce the amount of manual labeling of classes. We conduct rigorous experiments on four challenging human activity datasets to demonstrate the effectiveness of our framework for learning human activity models continuously.


title: Ex Machina: Personal Attacks Seen at Scale

abstract: The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users.


title: Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

abstract: We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, image captioning or translation. The main difference is the covert and overt attention, implemented as a multi-dimensional LSTM network. Our principal contribution towards handwriting recognition lies in the automatic transcription without a prior segmentation into lines, which was crucial in previous approaches. To the best of our knowledge this is the first successful attempt of end-to-end multi-line handwriting recognition. We carried out experiments on the well-known IAM Database. The results are encouraging and bring hope to perform full paragraph transcription in the near future.


title: EMOTEX: Detecting Emotions in Twitter Messages

abstract: Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text messages of individuals to infer their emotional states. To model emotional states, we utilize the well-established Circumplex model that characterizes aective experience along two dimensions: valence and arousal. We select Twitter messages as input data set, as they provide a very large, diverse and freely avail- able ensemble of emotions. Using hash-tags as labels, our methodology trains supervised classiers to detect multiple classes of emotion on potentially huge data sets with no manual eort. We investigate the utility of several features for emotion detection, including unigrams, emoticons, negations and punctuations. To tackle the problem of sparse and high dimensional feature vectors of messages, we utilize a lexicon of emotions. We have compared the accuracy of several machine learning algorithms, including SVM, KNN, Decision Tree, and Naive Bayes for classifying Twitter messages. Our technique has an accuracy of over 90%, while demonstrating robustness across learning algorithms.


title: The double-density dual-tree DWT

abstract: This paper introduces the double-density dual-tree discrete wavelet transform (DWT), which is a DWT that combines the double-density DWT and the dual-tree DWT, each of which has its own characteristics and advantages. The transform corresponds to a new family of dyadic wavelet tight frames based on two scaling functions and four distinct wavelets. One pair of the four wavelets are designed to be offset from the other pair of wavelets so that the integer translates of one wavelet pair fall midway between the integer translates of the other pair. Simultaneously, one pair of wavelets are designed to be approximate Hilbert transforms of the other pair of wavelets so that two complex (approximately analytic) wavelets can be formed. Therefore, they can be used to implement complex and directional wavelet transforms. The paper develops a design procedure to obtain finite impulse response (FIR) filters that satisfy the numerous constraints imposed. This design procedure employs a fractional-delay allpass filter, spectral factorization, and filterbank completion. The solutions have vanishing moments, compact support, a high degree of smoothness, and are nearly shift-invariant.


title: Experimental Validation of Motor Primitive-Based Control for Leg Exoskeletons during Continuous Multi-Locomotion Tasks

abstract: An emerging approach to design locomotion assistive devices deals with reproducing desirable biological principles of human locomotion. In this paper, we present a bio-inspired controller for locomotion assistive devices based on the concept of motor primitives. The weighted combination of artificial primitives results in a set of virtual muscle stimulations. These stimulations then activate a virtual musculoskeletal model producing reference assistive torque profiles for different locomotion tasks (i.e. walking, ascending and descending stairs). The paper reports the validation of the controller through a set of experiments conducted with healthy participants. The proposed controller was tested for the first time with a unilateral leg exoskeleton assisting hip, knee, and ankle joints by delivering a fraction of the computed reference torques. Importantly, subjects performed a track involving ground-level walking, ascending and descending stairs, and several transitions between these tasks. These experiments highlighted the capability of the controller to provide relevant assistive torques and to effectively handle transitions between the tasks. Subjects displayed a natural interaction with the device. Moreover, they significantly decreased the time needed to complete the track when the assistance was provided, as compared to wearing the device with no assistance.


title: Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation

abstract: We present approximation algorithms for the metric uncapacitated facility location problem and the metric k -median problem achieving guarantees of 3 and 6 respectively. The distinguishing feature of our algorithms is their low running time: O(m log m ) and O(m log m(L + log ( n ))) respectively, where n and m are the total number of vertices and edges in the underlying complete bipartite graph on cities and facilities. The main algorithmic ideas are a new extension of the primal-dual schema and the use of Lagrangian relaxation to derive approximation algorithms.


title: On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification

abstract: Learning the deep structures and unknown cor- relations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among fre- quency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation ac- curacy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features. Index Terms— multi-class motor imagery of EEG, deep learning, convolutional neural network, augmented CSP.