title: A Bayesian Model of Diachronic Meaning Change

abstract: Word meanings change over time and an automated procedure for extracting ::: this information from text would be useful for historical exploratory ::: studies, information retrieval or question answering. We present a ::: dynamic Bayesian model of diachronic meaning change, which infers ::: temporal word representations as a set of senses and their prevalence. ::: Unlike previous work, we explicitly model language change as a smooth, ::: gradual process. We experimentally show that this modeling decision is ::: beneficial: our model performs competitively on meaning change detection ::: tasks whilst inducing discernible word senses and their development over ::: time. Application of our model to the SemEval-2015 temporal ::: classification benchmark datasets further reveals that it performs on ::: par with highly optimized task-specific systems.


title: Tiny hand gesture recognition without localization via a deep convolutional network

abstract: Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds.


title: Performance analysis of encryption algorithms for security

abstract: With the fast progression of digital data exchange information security has become an important issue in data communication. Encryption algorithms play an important role in information security system. These algorithms use techniques to enhance the data confidentiality and privacy by making the information indecipherable which can be only be decoded or decrypted by party those possesses the associated key. But at the same time, these algorithms consume a significant amount of computing resources such as CPU time, memory, and battery power. So we need to evaluate the performance of different cryptographic algorithms to find out best algorithm to use in future. This paper provides evaluation of both symmetric (AES, DES, Blowfish) as well as asymmetric (RSA) cryptographic algorithms by taking different types of files like Binary, text and image files. A comparison has been conducted for these encryption algorithms using evaluation parameters such as encryption time, decryption time and throughput. Simulation results are given to demonstrate the effectiveness of each.


title: Conceptualizing and Testing a Social Cognitive Model of the Digital Divide

abstract: The digital divide has loomed as a public policy issue for over a decade. Yet, a theoretical account for the effects of the digital divide is currently lacking. This study examines three levels of the digital divide. The digital access divide (the first-level digital divide) is the inequality of access to information technology (IT) in homes and schools. The digital capability divide (the second-level digital divide) is the inequality of the capability to exploit IT arising from the first-level digital divide and other contextual factors. The digital outcome divide (the third-level digital divide) is the inequality of outcomes (e.g., learning and productivity) of exploiting IT arising from the second-level digital divide and other contextual factors. Drawing on social cognitive theory and computer self-efficacy literature, we developed a model to show how the digital access divide affects the digital capability divide and the digital outcome divide among students. The digital access divide focuses on computer ownership and usage in homes and schools. The digital capability divide and the digital outcome divide focus on computer self-efficacy and learning outcomes, respectively. This model was tested using data collected from over 4,000 students in Singapore. The results generate insights into the relationships among the three levels of the digital divide and provide a theoretical account for the effects of the digital divide. While school computing environments help to increase computer self-efficacy for all students, these factors do not eliminate knowledge the gap between students with and without home computers. Implications for theory and practice are discussed.


title: Design of a Wideband Dual-Polarization Full-Corporate Waveguide Feed Antenna Array

abstract: A wideband and dual-polarization antenna array with high efficiency and high isolation is proposed. In order to achieve the desired performance, a wideband radiating element and a full-corporate waveguide feed network are employed in the design. In particular, ${{E}}$ -plane waveguides are utilized for the feed-network configuration so as to suppress wave leakage between adjacent layers when the antenna is fabricated via conventional machining techniques. The operational mechanism is presented and a $\text{16} \times \text{16}$ antenna array is tested for verification. Experimental results show that the bandwidths of ${\rm VSWR} for both of the polarizations are about 17.1% (12.45–14.78 GHz) and the isolation between the two polarization ports is better than 39.5 dB. In addition, more than 70% antenna efficiency with higher than 29.4 dBi gain is achieved over the operational bandwidth.


title: 25 nm CMOS Omega FETs

abstract: Low leakage and low active-power 25 nm gate length C-MOSFETs are demonstrated for the first time with a newly proposed Omega-(/spl Omega/) shaped structure, at a conservative 17-19 /spl Aring/ gate oxide thickness, and with excellent hot carrier immunity. For 1 volt operation, the transistors give drive currents of 1440 /spl mu/A//spl mu/m and 780 /spl mu/A//spl mu/m with off state leakage currents of 8 nA//spl mu/m and 0.4 nA//spl mu/m for N-FET and P-FET, respectively. A low voltage version achieves, at 0.7 V, drive currents of 1300 /spl mu/A//spl mu/m for N-FET and 550 /spl mu/A//spl mu/m for P-FET at an off current of 1 /spl mu/A//spl mu/m. N-FET gate delay (CV/I) of 0.39 ps and P-FET gate delay of 0.88 ps exceed International Technology Roadmap for Semiconductors (ITRS) projections.


title: CR-GAN: Learning Complete Representations for Multi-view Generation

abstract: Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn "incomplete" representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning pathways collaborate and compete in a parameter-sharing manner, yielding considerably improved generalization ability to "unseen" dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. The experimental results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from "unseen" inputs in wild conditions.


title: Inhibitory activity against the fish pathogen Lactococcus garvieae produced by Lactococcus lactis TW34, a lactic acid bacterium isolated from the intestinal tract of a Patagonian fish

abstract: After enrichment of Odontesthes platensis intestinal contents, 53 lactic acid bacteria (LAB) were isolated. From the four isolates that showed inhibitory activity against Lactococcus garvieae 03/8460, strain TW34 was selected because it exerted the strongest inhibition. It also inhibited other Gram-positive bacteria, but not Gram-negative fish pathogens. Phenotypic and 16S rDNA phylogenetic analyses showed that TW34 belongs to Lactococcus lactis. In addition, TW34 showed to be sensitive to different antibiotics. The production of the inhibitory agent against L. garvieae was growth associated, and it was significantly influenced by the incubation temperature. The optimal temperature for the antimicrobial production was as low as 15 degrees C. Both acidification and hydrogen peroxide production were ruled out as the source of inhibition. In contrast, the antimicrobial activity was completely lost by treatment with proteolytic enzymes, which confirmed that the inhibitory substance was a bacteriocin. The bacteriocin was highly thermostable (121 degrees C for 15 min) and active between pH 3 and 11. It remained stable for up to 2 months when stored at 4 degrees C and up to 6 months at -20 degrees C. Our results suggest that the strain L. lactis TW34 could provide an alternative for lactococcosis control and therefore be considered for future challenge experiments with fish.


title: Mutation effects predicted from sequence co-variation

abstract: The global effects of epistasis on protein and RNA function are revealed by an unsupervised model of amino acid co-conservation in evolutionary sequence variation.


title: 3D printing of smart materials for robotics with variable stiffness and position feedback

abstract: This paper proposes a novel soft robotic design that possesses variable stiffness and position feedback. A gripper adopting this design is presented. Each finger in the gripper is based on a 3D printed substrate (driven by an air actuated soft actuator) composed of two smart materials: shape memory polymer (SMP) and conductive thermoplastic polyurethane (TPU). SMP's dramatic change of elastic modulus around its glass transition temperature (T g ) enables the finger to have variable stiffness. The conductive TPU acts as a heater to provide Joule heating for the SMP when a weakening of stiffness is needed. Furthermore, the deflection of the substrate can be estimated due to the conductive TPU's inherent piezo-resistive effect. Experiments regarding the finger's position estimation capability and variable stiffness property are conducted. A gripper is fabricated to perform grasping demonstration. The proposed robotic design has other potential applications such as metamorphic legged robots.


title: Hybrid Task Cascade for Instance Segmentation

abstract: Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at https://github.com/open-mmlab/mmdetection.


title: A rice quantitative trait locus for salt tolerance encodes a sodium transporter

abstract: Many important agronomic traits in crop plants, including stress tolerance, are complex traits controlled by quantitative trait loci (QTLs). Isolation of these QTLs holds great promise to improve world agriculture but is a challenging task. We previously mapped a rice QTL, SKC1, that maintained K+ homeostasis in the salt-tolerant variety under salt stress1, consistent with the earlier finding that K+ homeostasis is important in salt tolerance2,3. To understand the molecular basis of this QTL, we isolated the SKC1 gene by map-based cloning and found that it encoded a member of HKT-type transporters. SKC1 is preferentially expressed in the parenchyma cells surrounding the xylem vessels. Voltage-clamp analysis showed that SKC1 protein functions as a Na+-selective transporter. Physiological analysis suggested that SKC1 is involved in regulating K+/Na+ homeostasis under salt stress, providing a potential tool for improving salt tolerance in crops.


title: Compressive Rendering: A Rendering Application of Compressed Sensing

abstract: Recently, there has been growing interest in compressed sensing (CS), the new theory that shows how a small set of linear measurements can be used to reconstruct a signal if it is sparse in a transform domain. Although CS has been applied to many problems in other fields, in computer graphics, it has only been used so far to accelerate the acquisition of light transport. In this paper, we propose a novel application of compressed sensing by using it to accelerate ray-traced rendering in a manner that exploits the sparsity of the final image in the wavelet basis. To do this, we raytrace only a subset of the pixel samples in the spatial domain and use a simple, greedy CS-based algorithm to estimate the wavelet transform of the image during rendering. Since the energy of the image is concentrated more compactly in the wavelet domain, less samples are required for a result of given quality than with conventional spatial-domain rendering. By taking the inverse wavelet transform of the result, we compute an accurate reconstruction of the desired final image. Our results show that our framework can achieve high-quality images with approximately 75 percent of the pixel samples using a nonadaptive sampling scheme. In addition, we also perform better than other algorithms that might be used to fill in the missing pixel data, such as interpolation or inpainting. Furthermore, since the algorithm works in image space, it is completely independent of scene complexity.


title: Enabling Docker Containers for High-Performance and Many-Task Computing

abstract: Docker is the most popular and user friendlyplatform for running and managing Linux containers. This isproven by the fact that vast majority of containerized tools arepackaged as Docker images. A demanding functionality is toenable running Docker containers inside HPC job scripts forresearchers to make use of the flexibility offered by containersin their real-life computational and data intensive jobs. The maintwo questions before implementing such functionality are: how tosecurely run Docker containers within cluster jobs? and how tolimit the resource usage of a Docker job to the borders defined bythe HPC queuing system? This paper presents Socker, a securewrapper for running Docker containers on Slurm and similarqueuing systems. Socker enforces the execution of containerswithin Slurm jobs as the submitting user instead of root, as wellas enforcing the inclusion of containers in the cgroups assignedby the queuing system to the parent jobs. Different from otherDocker supported containers-for-hpc platform, socker uses theunderlaying Docker engine instead of replacing it. To eveluatesocker, it has been tested for running MPI Docker jobs on Slurm. It has been also tested for Many-task computing (MTC) on interconnectedclusters. Socker has proven to be secure, as well asintroducing no additional overhead to the one introduced alreadyby the Docker engine.


title: Classification of X-Ray Images Using Grid Approach

abstract: The process of medical image classification is still carried out manually using the knowledge of the physician or radiologist, which leads to inaccurate and slow process of object identification. Thus, we need an automatic system that can classify medical images, accurately and faster from query images into one of the pre-defined classes. In this research, we are dealing with the classification of medical image to the image classes that are defined in the database. We focus on managing the shape of X-ray image to perform the classification process and use the Euclidean distance and Jeffrey Divergence techniques to obtain image similarity.We use Freeman Code to represent the shape of X-ray images. This paper shows the development of the Freeman Code representation by simplifying the shape of X-ray image conducts to obtain the best recognition rate.


title: Visualization of Text Document Corpus

abstract: Visualization is commonly used in data analysis to help the user in getting an initial idea about the raw data as well as visual representation of the regularities obtained in the analysis. In similar way, when we talk about automated text processing and the data consists of text documents, visualization of text document corpus can be very useful. From the automated text processing point of view, natural language is very redundant in the sense that many different words share a common or similar meaning. For computer this can be hard to understand without some background knowledge. We describe an approach to visualization of text document collection based on methods from linear algebra. We apply Latent Semantic Indexing (LSI) as a technique that helps in extracting some of the background knowledge from corpus of text documents. This can be also viewed as extraction of hidden semantic concepts from text documents. In this way visualization can be very helpful in data analysis, for instance, for finding main topics that appear in larger sets of documents. Extraction of main concepts from documents using techniques such as LSI, can make the results of visualizations more useful. For example, given a set of descriptions of European Research projects (6FP) one can find main areas that these projects cover including semantic web, e-learning, security, etc. In this paper we describe a method for visualization of document corpus based on LSI, the system implementing it and give results of using the system on several datasets.


HCI in Business, Government, and Organizations: Information Systems


title: A Fully Autonomous Indoor Quadrotor

abstract: Recently, there has been increased interest in the development of autonomous flying vehicles. However, as most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments, where the systems cannot rely on the Global Positioning System (GPS) and, therefore, have to use their exteroceptive sensors for navigation. In this paper, we present a general navigation system that enables a small-sized quadrotor system to autonomously operate in indoor environments. To achieve this, we systematically extend and adapt techniques that have been successfully applied on ground robots. We describe all algorithms and present a broad set of experiments, which illustrate that they enable a quadrotor robot to reliably and autonomously navigate in indoor environments.


title: Load-Adaptive Modulation of a Series-Resonant Inverter for All-Metal Induction Heating Applications

abstract: A conventional induction heating (IH) system has been developed to heat the pot of ferromagnetic materials because the small resistance of nonferromagnetic materials induces large resonant current to the power switch of a series-resonant IH inverter. However, the heating capability for various materials is the most important to improve the functionality and the usability of IH products. In this paper, a load-adaptive modulation (LAM) method is proposed to heat the pot made from nonferromagnetic and ferromagnetic materials. The LAM method can change the magnitude of the input voltage of the IH coil and the operating frequency of the series-resonant IH inverter according to the resistance of the pot. The operational principle and the design method are analyzed to implement the proposed LAM method and its power control. The validity of the LAM method is experimentally verified using a 2-kW prototype series-resonant IH inverter.


title: Attributes Selection for Predicting Students' Academic Performance using Education Data Mining and Artificial Neural Network

abstract: Education Data mining plays an important role in predicting students’ performance,. It is a very promising discipline which has an imperative impact. In this paper students’ performance is evaluated and some attributes are selected which generate rules by means of association rule mining.. Artificial neural network checks accuracy of the results. A Multi-Layer Perceptron Neural Network is employed for selection of interesting features using 10 – fold cross validation.The artificial neural network selects 5 out of 8 attributes based on the accuracy obtained for correctly classified data. It is observed that in association rule mining important rules are generated using these selected attributes. The Experiment is conducted using Weka and real time data set available in the college premises.


title: Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired S.Ananthi

abstract: The ability of a reader to recognize written words correctly, virtually and effortlessly is defined as Word Recognition or Isolated Word Recognition. It will recognize each word from their shape. Speech Recognition is the operating system which enablesto convert spoken words to written text which is called as Speech to Text (STT) method. Usual Method used in Speech Recognition (SR) is Neural Network, Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). The widely used technique for Speech Recognition is HMM. Hidden Markov Model assumes that successive acoustic features of a spoken word are state independent. The occurrence of one feature is independent of the occurrence of the others state. Here each single unit of word is considered as state. Based upon the probability of the state it generates possible word sequence for the spoken word. Instead of listening to the speech, the generated sequence of text can be easily viewed. Each word is recognized from their shape. People with hearing impaired can make use of this Speech Recognition.


title: Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot

abstract: Describes new methods for rigid registration of a preoperative computed tomography (CT)-scan image to a set of intraoperative X-ray fluoroscopic images, for guiding a surgical robot to its trajectory planned from CT. Our goal is to perform the registration, i.e., compute a rotation and translation of one data set with respect to the other to within a prescribed accuracy, based upon bony anatomy only, without external fiducial markers. With respect to previous approaches, the following aspects are new: (1) the authors correct the geometric distortion in fluoroscopic images and calibrate them directly with respect to the robot by affixing to it a new calibration device designed as a radiolucent rod with embedded metallic markers, and by moving the device along two planes, while radiographs are being acquired at regular intervals; (2) the registration uses an algorithm for computing the best transformation between a set of lines in three space, the (intraoperative) X-ray paths, and a set of points on the surface of the bone (imaged preoperatively), in a statistically robust fashion, using the Cayley parameterization of a rotation; and (3) to find corresponding sets of points to the X-ray paths on the surfaces, the authors' new approach consists of extracting the surface apparent contours for a given viewpoint, as a set of closed three-dimensional nonplanar curves, before registering the apparent contours to X-ray paths. Aside from algorithms, there are a number of major technical difficulties associated with engineering a clinically viable system using anatomy and image-based registration. To detect and solve them, the authors have so far conducted two experiments with the surgical robot in an operating room (OR), using CT and fluoroscopic image data of a cadaver bone, and attempting to faithfully simulate clinical conditions. Such experiments indicate that intraoperative X-ray-based registration is a promising alternative to marker-based registration for clinical use with the authors' proposed method.


title: Multi-modality web video categorization

abstract: This paper reports a first comprehensive study and large-scale test on web video (so-called user generated video or micro video) categorization. Observing that web videos are characterized by a much higher diversity of quality, subject, style, and genres compared with traditional video programs, we focus on studying the effectiveness of different modalities in dealing with such high variation. Specifically, we propose two novel modalities including a semantic modality and a surrounding text modality, as effective complements to most commonly used low-level features. The semantic modality includes three feature representations, i.e., concept histogram, visual word vector model and visual word Latent Semantic Analysis (LSA), while text modality includes the titles, descriptions and tags of web videos. We conduct a set of comprehensive experiments for evaluating the effectiveness of the proposed feature representations over different classifiers such as Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Manifold Ranking (MR). Our experiments on a large-scale dataset with 11k web videos (nearly 450 hours in total) demonstrate that (1) the proposed multimodal feature representation is effective for web video categorization, and (2) SVM outperforms GMM and MR on nearly all the modalities.


title: DOI: 10.10170S0890060407070199 Ontology-based design information extraction and retrieval

abstract: Because of the increasing complexity of products and the design process, as well as the popularity of computer-aided documentation tools, the number of electronic and textual design documents being generated has exploded. The availability of such extensive document resources has created new challenges and opportunities for research. These include improving design information retrieval to achieve a more coherent environment for design exploration, learning, and reuse. One critical issue is related to the construction of a structured representation for indexing design documents that record engineers’ ideas and reasoning processes for a specific design. This representation should explicitly and accurately capture the important design concepts as well as the relationships between these concepts so that engineers can locate their documents of interest with less effort. For design information retrieval, we propose to use shallow natural language processing and domain-specific design ontology to automatically construct a structured and semantics-based representation from unstructured design documents. The design concepts and relationships of the representation are recognized from the document based on the identified linguistic patterns. The recognized concepts and relationships are joined to form a concept graph. The integration of these concept graphs builds an applicationspecific design ontology, which can be seen as the structured representation of the content of the corporate document repository, as well as an automatically populated knowledge base from previous designs. To improve the performance of design information retrieval, we have developed ontology-based query processing, where users’ requests are interpreted based on their domain-specific meanings. Our approach contrasts with the traditionally used keyword-based search. An experiment to test the retrieval performance is conducted by using the design documents from a product design scenario. The results demonstrate that our method outperforms the keyword-based search techniques. This research contributes to the development and use of engineering ontology for design information retrieval.


title: Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies

abstract: Abstract In real-time fleet management, vehicle routes are built in an on-going fashion as vehicle locations, travel times and customer requests are revealed over the planning horizon. To deal with such problems, a new generation of fast on-line algorithms capable of taking into account uncertainty is required. Although several articles on this topic have been published, the literature on real-time vehicle routing is still disorganized. In this paper the research in this field is reviewed and some issues that have not received attention so far are highlighted. A particular emphasis is put on parallel computing strategies.


title: Unlocking the expressivity of point lights

abstract: Small point lights (e.g., LEDs) are used as indicators in a wide variety of devices today, from digital watches and toasters, to washing machines and desktop computers. Although exceedingly simple in their output - varying light intensity over time - their design space can be rich. Unfortunately, a survey of contemporary uses revealed that the vocabulary of lighting expression in popular use today is small, fairly unimaginative, and generally ambiguous in meaning. In this paper, we work through a structured design process that points the way towards a much richer set of expressive forms and more effective communication for this very simple medium. In this process, we make use of five different data gathering and evaluation components to leverage the knowledge, opinions and expertise of people outside our team. Our work starts by considering what information is typically conveyed in this medium. We go on to consider potential expressive forms -- how information might be conveyed. We iteratively refine and expand these sets, concluding with ideas gathered from a panel of designers. Our final step was to make use of thousands of human judgments, gathered in a crowd-sourced fashion (265 participants), to measure the suitability of different expressive forms for conveying different information content. This results in a set of recommended light behaviors that mobile devices, such as smartphones, could readily employ.


title: A PSO-inspired multi-robot search algorithm independent of global information

abstract: This paper presents an algorithm that coordinates mobile robots to find the desired targets without depending on precise global information. We compare the abstract solution space in PSO and physical environment that robots will explore. According to similarities and differences between them, we introduce a PSO-inspired search algorithm to guide robots to complete the search mission. Moreover, a novel method based on Cartesian geometry for unifying relative coordinate systems will be adopted to improve system's robustness and efficiency.


title: Two-tier network anomaly detection model: a machine learning approach

abstract: Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naive Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.


title: Variation in behavioural responses of ewes towards predator-related stimuli

abstract: Thirty-two groups of six sheep, classified into three breed categories according to their weight class (L: light, one breed (n=7); M: medium light, two breeds (n=10); H: heavy, three breeds (n=15)) were tested for antipredatory behaviour towards seven stimulus regimes. Tests were performed on 2–5-years-old ewes with lambs inside standardised and fenced home pastures. Stimulus regimes were: man in rain poncho, trolley, ball on trolley (blind stimuli), stuffed wolverine on trolley, stuffed lynx on trolley, stuffed bear on trolley, and man in rain poncho with a dog on leash (carnivore stimuli). The L breed showed the longest recovery time, the longest flight distance and the tightest flocking behaviour. Significant regressions indicate that there were negative linear relationships between sheep weight and recovery time and between sheep weight and flight distance. Carnivore stimuli caused longer recovery times (P<0.001) and longer flight distances (P<0.001) than the blind stimuli. Our results confirm the hypothesis that lighter sheep breeds display stronger antipredatory reactions than heavier breeds.


title: Effects of pomegranate juice consumption on oxidative stress in patients with type 2 diabetes: a single-blind, randomized clinical trial.

abstract: Increased free radicals production due to hyperglycemia produces oxidative stress in patients with diabetes. Pomegranate juice (PJ) has antioxidant properties. This study was conducted to determine the effects of PJ consumption in oxidative stress in type 2 diabetic patients. This study was a randomized clinical trial performed on 60, 40-65 years old diabetic patients. The patients were randomly allocated either to PJ consumption group or control. Patients in PJ group consumed 200 ml of PJ daily for six weeks. Sex distribution and the mean age were not different between two groups. After six weeks intervention, oxidized LDL and anti-oxidized LDL antibodies decreased and total serum antioxidant capacity and arylesterase activity of paraoxonase increased significantly in the PJ-treated group compared to the control group. Our data have shown that six weeks supplementation of PJ could have favorable effects on oxidative stress in patients with type 2 diabetes (T2D).


title: Machine Learning in Prediction of Stock Market Indicators Based on Historical Data and Data from Twitter Sentiment Analysis

abstract: Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.


title: Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

abstract: A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.


title: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding

abstract: Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.


On quantum algorithms


On agents negotiation in a trading company simulation


title: C.: The SMT-LIB Standard: Version 2.0

abstract: The SMT-LIB initiative is an international effort, supported by research groups worldwide, with the two-fold goal of producing an extensive on-line library of benchmarks and promoting the adoption of common languages and interfaces for SMT solvers. This paper introduces Version 2 of the SMT-LIB Standard. This is a major upgrade of the previous Version 1.2 which, in addition to simplifying and extending the languages of that version, includes a new command language for interfacing with SMT solvers.


title: Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

abstract: We present a photo-realistic training and evaluation simulator (Sim4CV) (http://www.sim4cv.org) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.


title: Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition.

abstract: Plastics constitute a large material group with a global annual production that has doubled in 15 years (245 million tonnes in 2008). Plastics are present everywhere in society and the environment, especially the marine environment, where large amounts of plastic waste accumulate. The knowledge of human and environmental hazards and risks from chemicals associated with the diversity of plastic products is very limited. Most chemicals used for producing plastic polymers are derived from non-renewable crude oil, and several are hazardous. These may be released during the production, use and disposal of the plastic product. In this study the environmental and health hazards of chemicals used in 55 thermoplastic and thermosetting polymers were identified and compiled. A hazard ranking model was developed for the hazard classes and categories in the EU classification and labelling (CLP) regulation which is based on the UN Globally Harmonized System. The polymers were ranked based on monomer hazard classifications, and initial assessments were made. The polymers that ranked as most hazardous are made of monomers classified as mutagenic and/or carcinogenic (category 1A or 1B). These belong to the polymer families of polyurethanes, polyacrylonitriles, polyvinyl chloride, epoxy resins, and styrenic copolymers. All have a large global annual production (1-37 million tonnes). A considerable number of polymers (31 out of 55) are made of monomers that belong to the two worst of the ranking model's five hazard levels, i.e. levels IV-V. The polymers that are made of level IV monomers and have a large global annual production (1-5 million tonnes) are phenol formaldehyde resins, unsaturated polyesters, polycarbonate, polymethyl methacrylate, and urea-formaldehyde resins. This study has identified hazardous substances used in polymer production for which the risks should be evaluated for decisions on the need for risk reduction measures, substitution, or even phase out.


title: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

abstract: The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weighted sums of moments, e.g. Maximum Mean Discrepancy (MMD), an explicit order-wise matching of higher order moments has not been considered before. We propose to match the higher order central moments of probability distributions by means of order-wise moment differences. Our model does not require computationally expensive distance and kernel matrix computations. We utilize the equivalent representation of probability distributions by moment sequences to define a new distance function, called Central Moment Discrepancy (CMD). We prove that CMD is a metric on the set of probability distributions on a compact interval. We further prove that convergence of probability distributions on compact intervals w.r.t. the new metric implies convergence in distribution of the respective random variables. We test our approach on two different benchmark data sets for object recognition (Office) and sentiment analysis of product reviews (Amazon reviews). CMD achieves a new state-of-the-art performance on most domain adaptation tasks of Office and outperforms networks trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity analysis shows that the new approach is stable w.r.t. parameter changes in a certain interval. The source code of the experiments is publicly available.


title: ACAM: Approximate Computing Based on Adaptive Associative Memory with Online Learning

abstract: The Internet of Things (IoT) dramatically increases the amount of data to be processed for many applications including multimedia. Unlike traditional computing environment, the workload of IoT significantly varies overtime. Thus, an efficient runtime profiling is required to extract highly frequent computations and pre-store them for memory-based computing. In this paper, we propose an approximate computing technique using a low-cost adaptive associative memory, named ACAM, which utilizes runtime learning and profiling. To recognize the temporal locality of data in real-world applications, our design exploits a reinforcement learning algorithm with a least recently use (LRU) strategy to select images to be profiled; the profiler is implemented using an approximate concurrent state machine. The profiling results are then stored into ACAM for computation reuse. Since the selected images represent the observed input dataset, we can avoid redundant computations thanks to high hit rates displayed in the associative memory. We evaluate ACAM on the recent AMD Southern Island GPU architecture, and the experimental results shows that the proposed design achieves by 34.7% energy saving for image processing applications with an acceptable quality of service (i.e., PSNR>30dB).


title: Generalized Graph Convolutional Networks for Skeleton-based Action Recognition

abstract: With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular joints. In this paper, we represent skeletons naturally on graphs and propose a generalized graph convolutional neural networks (GGCN) for skeleton-based action recognition, aiming to capture space-time variation via spectral graph theory. In particular, we construct a generalized graph over consecutive frames, where each joint is not only connected to its neighboring joints in the same frame strongly or weakly, but also linked with relevant joints in the previous and subsequent frames. The generalized graphs are then fed into GGCN along with the coordinate matrix of the skeleton sequence for feature learning, where we deploy high-order and fast Chebyshev approximation of spectral graph convolution in the network. Experiments show that we achieve the state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU 3D datasets.


title: Towards a model-driven engineering approach for the assessment of non-functional properties using multi-formalism

abstract: Model-driven techniques can be used to automatically produce formal models from different views of a system realised by using several modelling languages and notations. Specifications are transformed into formal models so facilitating the analysis of complex system for design, validation or verification purposes. However, no single formalism suits for representing all system’s views. In particular, the assessment of non-functional properties often requires integrated modelling approaches. The ultimate goal of the research work described in this paper is to develop a comprehensive, theoretical and practical framework able to support the development and the integration of new or existing model-driven approaches for the automatic generation of multi-formalism models. This paper defines the core theoretical ideas on which the framework is based and demonstrates their concrete applicability to the development of a multi-formalism approach for performability assessment.


title: Daily Acyclovir to Decrease Herpes Simplex Virus Type 2 (HSV-2) Transmission from HSV-2/HIV-1 Coinfected Persons: A Randomized Controlled Trial

abstract: BACKGROUND ::: Daily suppressive therapy with valacyclovir reduces risk of sexual transmission of herpes simplex virus type 2 (HSV-2) in HSV-2-serodiscordant heterosexual couples by 48%. Whether suppressive therapy reduces HSV-2 transmission from persons coinfected with HSV-2 and human immunodeficiency virus type 1 (HIV-1) is unknown. ::: ::: ::: METHODS ::: Within a randomized trial of daily acyclovir 400 mg twice daily in African HIV-1 serodiscordant couples, in which the HIV-1-infected partner was HSV-2 seropositive, we identified partnerships in which HIV-1-susceptible partners were HSV-2 seronegative to estimate the effect of acyclovir on risk of HSV-2 transmission. ::: ::: ::: RESULTS ::: We randomly assigned 911 HSV-2/HIV-1-serodiscordant couples to daily receipt of acyclovir or placebo. We observed 68 HSV-2 seroconversions, 40 and 28 in acyclovir and placebo groups, respectively (HSV-2 incidence, 5.1 cases per 100 person-years; hazard ratio [HR], 1.35 [95% confidence interval, .83-2.20]; P = .22). Among HSV-2-susceptible women, vaginal drying practices (adjusted HR, 44.35; P = .004) and unprotected sex (adjusted HR, 9.91; P = .002) were significant risk factors for HSV-2 acquisition; having more children was protective (adjusted HR, 0.47 per additional child; P = .012). Among HSV-2-susceptible men, only age ≤30 years was associated with increased risk of HSV-2 acquisition (P = .016). ::: ::: ::: CONCLUSIONS ::: Treatment of African HSV-2/HIV-1-infected persons with daily suppressive acyclovir did not decrease risk of HSV-2 transmission to susceptible partners. More-effective prevention strategies to reduce HSV-2 transmission from HIV-1-infected persons are needed.


title: Energy- and endurance-aware design of phase change memory caches

abstract: Phase change memory (PCM) is one of the most promising technology among emerging non-volatile random access memory technologies. Implementing a cache memory using PCM provides many benefits such as high density, non-volatility, low leakage power, and high immunity to soft error. However, its disadvantages such as high write latency, high write energy, and limited write endurance prevent it from being used as a drop-in replacement of an SRAM cache. In this paper, we study a set of techniques to design an energy- and endurance-aware PCM cache. We also modeled the timing, energy, endurance, and area of PCM caches and integrated them into a PCM cache simulator to evaluate the techniques. Experiments show that our PCM cache design can achieve 8% of energy saving and 3.8 years of lifetime compared with a baseline PCM cache having less than a hour of lifetime.


title: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

abstract: Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.


title: Clustering and Classification in Text Collections Using Graph Modularity

abstract: A new fast algorithm for clustering and classification of large collections of text documents is introduced. The new algorithm employs the bipartite graph that realizes the word-document matrix of the collection. Namely, the modularity of the bipartite graph is used as the optimization functional. Experiments performed with the new algorithm on a number of text collections had shown a competitive quality of the clustering (classification), and a record-breaking speed.


Serendipitous Information Retrieval


title: Family issues in child anxiety: Attachment, family functioning, parental rearing and beliefs

abstract: Family studies have found a large overlap between anxiety disorders in family members. In addition to genetic heritability, a range of family factors may also be involved in the intergenerational transmission of anxiety. Evidence for a relationship between family factors and childhood as well as parental anxiety is reviewed. Four groups of family variables are considered: (I) attachment; (II), aspects of family functioning, such as marital conflict, co-parenting, functioning of the family as a whole, and sibling relationships; (III) parental rearing strategies; and (IV) beliefs that parents hold about their child. The reviewed literature provides evidence for an association between each of these family factors and child anxiety. However, there is little evidence as yet that identified family factors are specific to child anxiety, rather than to child psychopathology in general. Moreover, evidence for a relationship between child anxiety and family factors is predominantly cross-sectional. Therefore, whether the identified family factors cause childhood anxiety still needs to be investigated. Further research that investigates mechanisms mediating the relationship between family factors and child anxiety is also called for. Finally, parental beliefs are identified as important predictors of parental behaviour that have largely not been investigated in relation to child anxiety disorders.


title: CONSUMER RESPONSIVENESS TO MOBILE MARKETING

abstract: Digital marketing media, e.g. internet and mobile phones, are considered powerful new opportunities to reach consumers but are dependent upon consumer responsiveness to the new media. Consumer responsiveness is a function of the perceived relevance of the marketing message and the acceptance of the medium of the message. Two empirical studies conducted in Finland showed that compared to traditional Direct Mail (DM) and commercial e-mail, the responsiveness to mobile marketing was considerably lower. The findings indicate that companies need to consider consumers' responsiveness in order to understand the marketing communication effectiveness in different traditional and new communication media.


title: On the design of a statically balanced serial robot using remote counterweights

abstract: This paper presents a 7-DOF partially statically balanced robot that has been developed for physical human robot interaction. The gravity compensation technique uses remote counterweights connected to the robot via a low-pressure hydraulic transmission. Low-friction diaphragm cylinders are used in order to provide very low residual friction. A two-stage balancing bench allows using a unique moving counterweight to adapt the balancing to a payload of up to 10 kg, that is the maximal payload of the gripper. The robot can be easily moved passively while being capable of physically assisting humans in the performance of tasks.


title: rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

abstract: The increasing computing requirements for GPUs (Graphics Processing Units) have favoured the design and marketing of commodity devices that nowadays can also be used to accelerate general purpose computing. Therefore, future high performance clusters intended for HPC (High Performance Computing) will likely include such devices. However, high-end GPU-based accelerators used in HPC feature a considerable energy consumption, so that attaching a GPU to every node of a cluster has a strong impact on its overall power consumption. In this paper we detail a framework that enables remote GPU acceleration in HPC clusters, thus allowing a reduction in the number of accelerators installed in the cluster. This leads to energy, acquisition, maintenance, and space savings.


title: AFIS: Aligning detail-pages for full schema induction

abstract: Web data extraction is an essential task for web data integration. Most researches focus on data extraction from list-pages by detecting data-rich section and record boundary segmentation. However, in detail-pages which contain all-inclusive product information in each page, so the number of data attributes need to be aligned is much larger. In this paper, we formulate data extraction problem as alignment of leaf nodes from DOM Trees. We propose AFIS, Annotation-Free Induction of Full Schema for detail pages in this paper. AFIS applies Divide-and-Conquer and Longest Increasing Sequence (LIS) algorithms to mine landmarks from input. The experiments show that AFIS outperforms RoadRunner, FivaTech and TEX (F1 0.990) in terms of selected data. For full schema evaluation (all data), AFIS also represents the highest average performance (F1 0.937) compared with TEX and RoadRunner.


title: Instance Normalization: The Missing Ingredient for Fast Stylization

abstract: It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at this https URL. Full paper can be found at arXiv:1701.02096.


title: A 2.4 GHz passive wake-up circuit for power minimization in wireless sensor nodes

abstract: Power consumption of the sensor node (SN) is a critical issue. A SN drains-out its moderately powerful batteries (AA) of 1500 mAh in just a few days, while the power requirement of wireless sensor network applications is of the order of few years. So an important question is: “how to prolong the network lifetime?”. This article presents a prototype of passive wake-up circuit (PWUC) to minimize the power consumption by the SNs during the idle listening mode. The PWUC consists of an antenna, impedance matching circuitry and rectifier. Very few authors have proposed the PWUCs and most of them operate in European frequency bands (like 868 MHz) and these cannot be deployed in the Indian context of open band. The designed PWUC operates as per Indian frequency spectrum regulations and provides an output power of 3.2 µW (output voltage: 1.8 V & output current: 1.8 µA) for an input power of −19 dBm. This amounts to conversion efficiency of 25 %. The UMC 0.18 µm CMOS process is used to realize Rectifier, while standard off-the-shelf components are used to realize matching circuitry.


title: Monocular visual-inertial fusion with online initialization and camera-IMU calibration

abstract: There have been increasing demands in developing micro aerial vehicles with vision-based autonomy for search and rescue missions in complex environments. In particular, the monocular visual-inertial system (VINS), which consists of only an inertial measurement unit (IMU) and a camera, forms a great lightweight sensor suite due to its low weight and small footprint. In this work, we address two challenges for rapid deployment of monocular VINS — the initialization problem and the calibration problem. We propose a methodology that is able to initialize velocity, gravity, visual scale, and camera-IMU extrinsic calibration on-the-fly. Our approach does not require any prior knowledge about the mechanical configuration of the system. It is a significant step towards plug-and-play and highly customizable visual navigation for mobile robots. We show through online experiment that our method leads to accurate calibration of camera-IMU transformation with errors of 0.02 m in position and 2 degrees in rotation.


title: Classification hierarchies for product data modeling”, Production Planning and Control

abstract: Abstraction is an essential element in data modelling that appears mainly in one of the following forms: generalisation, classification or aggregation. In the design of complex products classification hierarchies can be found product families that are viewed as classes of product types, while product types are seen as classes of product instances. So far data models, if they support levels of classification, do not support the inheritance of product properties the way product family hierarchies are supposed to. This paper proposes an interpretation of the member of construct that enables one to define inheritance in classification hierarchies. Unified modelling language (UML) static structures are used as data modelling language. A textual notation is added that enables the introduction of data structures and corresponding data contents in the course of an analysis.


title: Ischemic Stroke detection using Image processing and ANN

abstract: Ischemic stroke is a condition in which brain stops working due to lack of blood supply resulting in death of brain cells. Magnetic Resonance Imaging is widely used to detect Ischemic Strokes in brain. This paper gives an automated algorithm to detect the stroke using Image processing techniques. The algorithm consists of six phases. Data in the form of MRI images is collected in first phase. The preprocessing is performed including filtering on the raw data collected. Midline is traced in third phase for acquiring a symmetrical image. It is followed by bifurcation of image in fourth phase. Finally the image quality matrix is formed for texture analysis in fifth phase and neural network is applied in sixth phase for classification of normal and infected brain. The advantage is that the strokes can be detected in its early stage. The algorithm proposed is simple, efficient and less time consuming. The efficiency is found to be 98%.


Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation


title: Robust Direct Visual Odometry Estimation for a Monocular Camera Under Rotations

abstract: The objective of this letter is to enhance the accuracy of the monocular visual odometry estimation. The proposed approach estimates pose of the camera via direct approach, by minimizing a novel photometric residue between the current image and warped version of the consecutive image. The novel photometric residue incorporates orientation information accessible from complementary sensor to overcome the inalienable nonlinearity of pose estimation, accordingly lessening the susceptibility to trajectory noise. In addition, this letter introduces a homography-based rotation motion estimation embedded into the photometric residue to reduce tracking failure amid rotations, which could be attributed to lesser number of features with parallax. The proposed methodology permits to integrate Gaussian mixture based prior into visual odometry estimation to cater for changes in motion type, from general to rotation and other way round. Comprehensive experiments have been conducted to exhibit the efficacy of the proposed solution over the best in class visual odometry and visual-inertial systems.


title: Analysis of multi-phase permanent-magnet synchronous machines

abstract: Large AC machines are sometimes fed by multiple inverters. This paper presents the complete steady-state analysis of the PM synchronous machine with multiplex windings, suitable for driving by multiple independent inverters. Machines with 4, 6 and 9 phases are covered in detail. Particular attention is given to the magnetic interactions not only between individual phases, but between channels or groups of phases. This is of interest not only for determining performance and designing control systems, but also for analysing fault tolerance. It is shown how to calculate the necessary self- and mutual inductances and how to reduce them to a compact dq-axis model without loss of detail.


title: Apache Hadoop YARN: yet another resource negotiator

abstract: The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agora---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler. In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.


title: TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning

abstract: Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the planner. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games, with deeper trees often outperforming shallower ones. We also present a qualitative analysis that sheds light on the trees learned by TreeQN.


title: Inequity aversion resolves intertemporal social dilemmas.

abstract: Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas. Models based on behavioral economics are only able to explain this phenomenon for unrealistic stateless matrix games. Recently, multi-agent reinforcement learning has been applied to generalize social dilemma problems to temporally and spatially extended Markov games. However, this has not yet generated an agent that learns to cooperate in social dilemmas as humans do. A key insight is that many, but not all, human individuals have inequity averse social preferences. This promotes a particular resolution of the matrix game social dilemma wherein inequity-averse individuals are personally pro-social and punish defectors. Here we extend this idea to Markov games and show that it promotes cooperation in several types of sequential social dilemma, via a profitable interaction with policy learnability. In particular, we find that inequity aversion improves temporal credit assignment for the important class of intertemporal social dilemmas. These results help explain how large-scale cooperation may emerge and persist.


title: Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

abstract: With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.


title: Generative Attention Model with Adversarial Self-learning for Visual Question Answering

abstract: Visual question answering (VQA) is arguably one of the most challenging multimodal understanding problems as it requires reasoning and deep understanding of the image, the question, and their semantic relationship. Existing VQA methods heavily rely on attention mechanisms to semantically relate the question words with the image contents for answering the related questions. However, most of the attention models are simplified as a linear transformation, over the multimodal representation, which we argue is insufficient for capturing the complex nature of the multimodal data. In this paper we propose a novel generative attention model obtained by adversarial self-learning. The proposed adversarial attention produces more diverse visual attention maps and it is able to generalize the attention better to new questions. The experiments show the proposed adversarial attention leads to a state-of-the-art VQA model on the two VQA benchmark datasets, VQA v1.0 and v2.0.


title: Maximum entropy inverse reinforcement learning in continuous state spaces with path integrals

abstract: In this paper, we consider the problem of inverse reinforcement learning for a particular class of continuous-time stochastic systems with continuous state and action spaces, under the assumption that both the cost function and the optimal control policy are parametric with known basis functions. Our goal is to produce a cost function for which a given policy, observed in experiment, is optimal. We proceed by enforcing a constraint on the relationship between input noise and input cost that produces a maximum entropy distribution over the space of all sample paths. We apply maximum likelihood estimation to approximate the parameters of this distribution (hence, of the cost function) given a finite set of sample paths. We iteratively improve our approximation by adding to this set the sample path that would be optimal given our current estimate of the cost function. Preliminary results in simulation provide empirical evidence that our algorithm converges.


title: Auto-categorization methods for digital archives

abstract: Archivists and records managers would benefit from a greater understanding of the use and effectiveness of various machine learning methods, especially in the related context of electronic discovery. However, the binary classification methods used in advance search techniques in the e-discovery space may or may not prove efficacious where the information task involves sorting records into multiple categories. A survey of the landscape of machine learning methods reveals areas of potential weakness, which in turn serve as a starting point for future research in the computational archives space.


title: The Little Engine that Could: Regularization by Denoising (RED)

abstract: Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led some to believe that existing methods are touching the ceiling in terms of noise removal performance. Can we leverage this impressive achievement to treat other tasks in image processing? Recent work has answered this question positively, in the form of the Plug-and-Play Prior ($P^3$) method, showing that any inverse problem can be handled by sequentially applying image denoising steps. This relies heavily on the ADMM optimization technique in order to obtain this chained denoising interpretation. Is this the only way in which tasks in image processing can exploit the image denoising engine? In this paper we provide an alternative, more powerful, and more flexible framework for achieving the same goal. As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regulariza...


title: A local search approximation algorithm for k-means clustering

abstract: In k-means clustering we are given a set of n data points in d-dimensional space ℜd and an integer k, and the problem is to determine a set of k points in ℜd, called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the extremely high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance.We consider the question of whether there exists a simple and practical approximation algorithm for k-means clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9+e)-approximation algorithm. We show that the approximation factor is almost tight, by giving an example for which the algorithm achieves an approximation factor of (9-e). To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with Lloyd's algorithm, this heuristic performs quite well in practice.


title: A survey of context data distribution for mobile ubiquitous systems

abstract: The capacity to gather and timely deliver to the service level any relevant information that can characterize the service-provisioning environment, such as computing resources/capabilities, physical device location, user preferences, and time constraints, usually defined as context-awareness, is widely recognized as a core function for the development of modern ubiquitous and mobile systems. Much work has been done to enable context-awareness and to ease the diffusion of context-aware services; at the same time, several middleware solutions have been designed to transparently implement context management and provisioning in the mobile system. However, to the best of our knowledge, an in-depth analysis of the context data distribution, namely, the function in charge of distributing context data to interested entities, is still missing. Starting from the core assumption that only effective and efficient context data distribution can pave the way to the deployment of truly context-aware services, this article aims at putting together current research efforts to derive an original and holistic view of the existing literature. We present a unified architectural model and a new taxonomy for context data distribution by considering and comparing a large number of solutions. Finally, based on our analysis, we draw some of the research challenges still unsolved and identify some possible directions for future work.


title: A survey of robot learning from demonstration

abstract: We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.


title: A Flexible Fabrication Approach Toward the Shape Engineering of Microscale Soft Pneumatic Actuators

abstract: The recent developments of soft robots have inspired a variety of applications that require compliance for safety and softness for biomimetics. With such prevalence and advantages of soft robots, researchers are diverting their interests toward applications like micromanipulation of biological tissue. However, the progress has been thwarted by the difficulty in producing soft robots in miniature scale. In this paper, we present a new kind of microscale soft pneumatic actuators (SPA) made from streamlined and standardized fabrication procedure with customizable bending modalities inspired by shape engineering. Preliminary mathematical models are given to interpret width-based shape engineering for customization and to compare the bending angle and radius of curvature measured from the characterization experiments. The fabricated SPA was tested on the sciatic nerve of a rat in order to show its future potentials in biomedical field. Ultimately, this paper will contribute to the diversification of SPA fabrication and customization, as well as biomedical applications that demand smaller dimensions and higher precision.


title: Array hybrid multiplier versus modified booth multiplier: comparing area and power consumption of layout implementations of signed radix-4 architectures

abstract: In this paper, we describe the fully automated custom layout implementations of two architectures for signed multiplication. Performance comparisons between the two, namely in terms of their power consumption and area estimation are provided for 8 and 16-bit operands. The first architecture consists of a signed array multiplier that uses a radix-4 hybrid encoding to reduce the partial product lines and switching activity in the data buses. This new arithmetic operand encoding was recently proposed in (Costa et al., 2004), however only results at the logic level were presented. The second architecture implemented was the widely used modified Booth multiplier (Khater et al., 1996). The layout of both multipliers was generated by an automatic layout synthesis tool called TROPIC (MOraes, 1999). We compare the layout implementations in terms of area and power, as well as provide comparisons to first-order area estimates done in the logic design phase. The results show that the new hybrid array multiplier can be significantly more efficient, with close to 30% power savings.


title: Behind the scenes of the BPMN extension mechanism principles, problems and options for improvement

abstract: The Business Process Model and Notation (BPMN) is a standard for modeling business processes that is widely used and accepted both in academia and industry due to its well-defined meta model, its large set of concepts and its extensibility. BPMN is one of very few modeling languages that provides an integrated extension mechanism. However, the mechanism is not often implemented in research articles or in professional practice. We suppose, that both syntactical and methodical aspects within the BPMN extension mechanism may cause misunderstandings and uncertainty regarding its implementation. Therefore, we conducted an in-depth analysis of the extension mechanism in order to rationally figure out problematic aspects. These aspects are consolidated and compared to two existing BPMN extension methods. Based on that, a range of further research topics is finally derived.


title: Inner Source Definition, Benefits, and Challenges

abstract: Inner Source (IS) is the use of open source software development practices and the establishment of an open source-like culture within organizations. The organization may still develop proprietary software but internally opens up its development. A steady stream of scientific literature and practitioner reports indicates the interest in this research area. However, the research area lacks a systematic assessment of known research work: No model exists that defines IS thoroughly. Various case studies provide insights into IS programs in the context of specific organizations but only few publications apply a broader perspective. To resolve this, we performed an extensive literature survey and analyzed 43 IS related publications plus additional background literature. Using qualitative data analysis methods, we developed a model of the elements that constitute IS. We present a classification framework for IS programs and projects and apply it to lay out a map of known IS endeavors. Further, we present qualitative models summarizing the benefits and challenges of IS adoption. The survey provides the first broad review of IS literature and systematic arrangement of IS research results.


Craniofrontonasal Dysplasia : A Report of Two Chinese Families and Literature Review


title: Neural network-based autonomous allocation of resources in virtual networks

abstract: Network virtualisation has received attention as a way to allow for sharing of physical network resources. Sharing resources involves mapping of virtual nodes and links onto physical nodes and links respectively, and thereafter managing the allocated resources to ensure efficient resource utilisation. In this paper, we apply artificial neural networks for a dynamic, decentralised and autonomous allocation of physical network resources to the virtual networks. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. The proposed approach is evaluated by comparison with two static resource allocation schemes and a reinforcement learning-based approach.


title: An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds

abstract: This paper aims to propose a new optimal hierarchical clustering approach to 3D mobile light detection and ranging (LiDAR) point clouds. The hierarchical clustering is performed on unorganized point clouds based on a proximity matrix that consists of a distance term and a direction term. In the dissimilarity calculation of two clusters, a pair of points from each of two clusters is selected, respectively, and Euclidean distances between the points are employed to define the distance term. The direction term is obtained by the differences of normal vectors at chosen points. The main contribution is that the cluster combination in the hierarchical clustering is optimized by a point-based graph model. The cluster combination is formulated as a problem of matching, optimized by finding the minimum-cost perfect matching in a bipartite graph. The results show that the proposed hierarchical clustering method succeeds in segmenting object from point clouds without any human-computer interaction and outperforms the state-of-the-art segmentation approaches in terms of completeness and correctness.


title: A Multi-sensor Traffic Scene Dataset with Omnidirectional Video

abstract: The development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. These datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds. This paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. The dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. The software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.


title: Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions

abstract: In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image recognition applications as compared to using standard deep learning methods. The proposed framework has recently been deployed as the key kernel for several image recognition competitions organized by Kaggle. The performance is promising as our final private scores were ranked 4 out of 2293 teams for fish recognition on the challenge "The Nature Conservancy Fisheries Monitoring" and 3 out of 834 teams for cervix recognition on the challenge "Intel &MobileODT Cervical Cancer Screening", and several others. We believe that by sharing the solutions, we can further promote the applications of deep learning techniques.


title: Du-Parking: Spatio-Temporal Big Data Tells You Realtime Parking Availability

abstract: Realtime parking availability information is of great importance to help drivers to find a parking space faster and thus to reduce parking search traffic. While there are limited realtime parking availability systems in a city due to the expensive cost of sensor device and maintaining realtime parking information. In this paper, we estimate the realtime parking availability throughout a city using historical parking availability data reported by a limited number of existing sensors of parking lots and a variety of datasets we observed in the city, such as meteorology, events, map mobility trace data and navigation data from Baidu map, and POIs. We propose a deep-learning-based approach, called Du-Parking, which consists of three major components modeling temporal closeness, period and current general influence, respectively. More specifically, we employ long short-term memory (LSTM) to model the temporal closeness and period, and meanwhile using two fully-connected layers to model the current general factors. Our approach learns to dynamically aggregate the output of the three components, to estimate the final parking availability of given parking lot. Using the proposed approach, we have provided the realtime parking availability information in Baidu map app, in nine cities in China. We evaluated our approach in Beijing and Shenzhen. The results show the advantages of our method over two categories of baselines, including linear interpolations, and the well-known classification model like GBDT.


title: Blockchain Technology: Is this the Solution to EMR Interoperability and Security Issues in Developing Countries?

abstract: The burden of disease is higher by far in developing countries than in the developed world. Developing countries today are turning to technology as the silver bullet or remedy. Indeed, Information and Communication Technology has turned into a key-enabling tool in the enhanced healthcare management. The electronic health records or electronic medical records (EMR) a key component of medical informatics symbolize potential solutions for enhanced healthcare. However, interoperability and security of EMR systems has been the two main challenges of EMR in the healthcare industry. By analyzing existing literature using scoping review research approach this paper explored the potential use of blockchain technology in improving the interoperability and security of EMR systems for the benefit of different stakeholders in health sector in developing countries such as Kenya. To achieve our main objective, five databases were searched and 204 papers screened for inclusion. As a result of the search and screen process, we identified 25 relevant articles.


title: Skin disease identification from dermoscopy images using deep convolutional neural network

abstract: In this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images. The developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection).


Online Peer-to-Peer Lending – A Literature Review


title: Passive knee exoskeleton using torsion spring for cycling assistance

abstract: In this paper, we introduce a concept of passive knee exoskeleton for cycling assistance. Considering a knee moment and a knee angle varying with a pedal crank angle, the knee extension moment can be supported by a torsion spring storing energy from knee flexion in order to release it as the knee is extended. The reduction of knee extension effort is corresponding to the torsion spring stiffness and activation range. Exoskeleton prototypes were developed for the concept validation. A crossing four-bar mechanism was chosen for the knee joint to provide kinematic compatibility covering extreme knee flexion. Constant power cycling experiment was performed on a cycling trainer by a healthy subject wearing the exoskeletons on both legs. With the torsion spring support, the surface electromyography recorded from some major leg muscles shows the decrease of knee extensor muscle activity as the leg is moving around the pedal crank top dead center. Verifying the reduction of leg muscle fatigue over repetitive contractions in multiple subjects is our future plan of study.


title: Health monitoring using Internet of Things (IoT)

abstract: Health is the level of useful and metabolic potency of a living organism. In humans, it is the power of people or communities to adapt and self-manage once facing physical, mental, psychological and social changes. Keeping track of the health standing of the patient reception may be a troublesome task. Specially maturity patients ought to be sporadically monitored and their adored ones got to learn concerning their health standing from time to time whereas at work. During this paper, our objective is to trace patient's health with the assistance of sensors and internet. Internet is employed to tell their beloved if there is a drag. The health observation system can keep track of patient's pulse rate, eco rate of heart, pressure level rate, temperature etc. If system detects any abrupt changes in patient heartbeat or temperature, the system mechanically alerts the user concerning the patients standing over IOT and additionally shows details of heartbeat and temperature of patient live over the internet.


title: An online classification algorithm for large scale data streams: iGNGSVM

abstract: Abstract Stream Processing has recently become one of the current commercial trends to face huge amounts of data. However, normally these techniques need specific infrastructures and high resources in terms of memory and computing nodes. This paper shows how mini-batch techniques and topology extraction methods can help making gigabytes of data to be manageable for just one server using computationally costly Machine Learning techniques as Support Vector Machines. The algorithm iGNGSVM is proposed to improve the performance of Support Vector Machines in datasets where the data is continuously arriving. It is benchmarked against a mini-batch version of LibSVM, achieving good accuracy rates and performing faster than this.


title: The effects of music on time perception and performance of a driving game.

abstract: There is an established and growing body of evidence highlighting that music can influence behavior across a range of diverse domains (Miell, MacDonald, & Hargreaves 2005). One area of interest is the monitoring of "internal timing mechanisms", with features such as tempo, liking, perceived affective nature and everyday listening contexts implicated as important (North & Hargreaves, 2008). The current study addresses these issues by comparing the effects of self‐selected and experimenter‐selected music (fast and slow) on actual and perceived performance of a driving game activity. Seventy participants completed three laps of a driving game in seven sound conditions: (1) silence; (2) car sounds; (3) car sounds with self‐selected music, and car sounds with experimenter‐selected music; (4) high‐arousal (70 bpm); (5) high‐arousal (130 bpm); (6) low‐arousal (70 bpm); and (7) low‐arousal (130 bpm) music. Six performance measures (time, accuracy, speed, and retrospective perception of these), and four experience measures (perceived distraction, liking, appropriateness and enjoyment) were taken. Exposure to self‐selected music resulted in overestimation of elapsed time and inaccuracy, while benefiting accuracy and experience. In contrast, exposure to experimenter‐selected music resulted in poorest performance and experience. Increasing the tempo of experimenter‐selected music resulted in faster performance and increased inaccuracy for high‐arousal music, but did not impact experience. It is suggested that personal meaning and subjective associations connected to self‐selected music promoted increased engagement with the activity, overriding detrimental effects attributed to unfamiliar, less liked and less appropriate experimenter‐selected music. Keywords: Driver distraction;


title: Development of a work addiction scale

abstract: Andreassen, C. S., Griffiths, M. D., Hetland, J. & Pallesen, S. (2012). Development of a work addiction scale. Scandinavian Journal of Psychology 53, 265–272. ::: ::: Research into excessive work has gained increasing attention over the last 20 years. Terms such as “workaholism,”“work addiction” and “excessive work” have been used interchangeably. Given the increase in empirical research, this study presents the development of the Bergen Work Addiction Scale (BWAS), a new psychometrically validated scale for the assessment of work addiction. A pool of 14 items, with two reflecting each of seven core elements of addiction (i.e., salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems) was initially constructed. The items were then administered to two samples, one recruited by a web survey following a television broadcast about workaholism (n = 11,769) and one comprising participants in the second wave of a longitudinal internet-based survey about working life (n = 368). The items with the highest corrected item-total correlation from within each of the seven addiction elements were retained in the final scale. The assumed one-factor solution of the refined seven-item scale was acceptable (root mean square error of approximation = 0.077, Comparative Fit Index = 0.96, Tucker-Lewis Index = 0.95) and the internal reliability of the two samples were 0.84 and 0.80, respectively. The scores of the BWAS converged with scores on other workaholism scales, except for a Work Enjoyment subscale. A suggested cut-off for categorization of workaholics showed good discriminative ability in terms of working hours, leadership position, and subjective health complaints. It is concluded that the BWAS has good psychometric properties.