title: Incremental real-time multibody VSLAM with trajectory optimization using stereo camera

abstract: Real-time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real-time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving object trajectory simultaneously. In this paper, we perform a sparse scene flow based motion segmentation using a stereo camera. The segmented objects motion models are used for accurate localization of the camera trajectory as well as the moving objects. We exploit the relationship between moving objects for improving the accuracy of the poses. We formulate the poses as a factor graph incorporating all the constraints. We achieve exact incremental solution by solving a full nonlinear optimization problem in real time. The evaluation is performed on the challenging KITTI dataset with multiple moving cars. Our method outperforms the previous baselines in outdoor navigation.


title: Design and analysis of cable-connected metallic bellows as Variable Buoyancy Modules

abstract: Design of a variable buoyancy system with metallic bellows for underwater applications is presented here. Metallic bellow with a Linear Actuator is proposed as the Variable Buoyancy module. The metallic bellow is approximated as a cylinder and modelled mathematically to study the behavior of the module underwater. The response of the system at different initial orientation and the velocity of the system at different positive buoyancy are studied. The dynamics of the module, when anchored to an underwater platformand the motion characteristics of the module with various buoyancy levels are studied. Two similar modules are then cascaded one above the other and their performance is analyzed with alternative sequence of actuation.


title: Detecting communities and their evolutions in dynamic social networks—a Bayesian approach

abstract: Although a large body of work is devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolution in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and real-life data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several state-of-the-art algorithms.


title: Enhancing Network Performance in Distributed Cognitive Radio Networks using Single-Agent and Multi-Agent Reinforcement Learning

abstract: Cognitive Radio (CR) is a next-generation wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of unlicensed users in the absence of fixed network infrastructure such as a base station. Context awareness and intelligence are the capabilities that enable each unlicensed user to observe and carry out its own action as part of the joint action on its operating environment for network-wide performance enhancement. These capabilities can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), congestion control, and scheduling. In this paper, we apply Reinforcement Learning (RL), including single-agent and multi-agent approaches, to achieve context awareness and intelligence. Firstly, we show that the RL approach achieves a joint action that provides better network-wide performance in respect to DCS in DCRNs. The multi-agent approach is shown to provide higher levels of stability compared to the single-agent approach. Secondly, we show that RL achieves high level of fairness. Thirdly, we show the effects of network density and various essential parameters in RL on the network-wide performance.


title: Hardware implementation of solar tracking system using a stepper motor

abstract: The people are switching from conventional energy sources to renewable energy sources. The use of solar photovoltaic is increasing day by day as there is an abundant amount of energy which is being received from sun on earth. The photovoltaic system when placed as a fixed structure at a particular angle is inefficient because this system is unable to extract maximum power from sun. In this paper, a single axis solar tracking system has been used. The control algorithm for solar tracker can move the panel in both the directions i.e. east to west and west to east in a step function. The stepper motor is switched on at regular intervals of half an hour owing to which there is reduction in consumption of power as compared to that of a continuously run motor in conventional solar tracking systems. The output power, obtained from the setup is higher than those reported in literature.


title: A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

abstract: Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user-feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.


title: High Voltage Tolerant Linear Regulator With Fast Digital Control for Biasing of Integrated DC-DC Converters

abstract: Integrated DC-DC converters switching above 100MHz dramatically reduce the footprint of the inductors and capacitors while improving droop response. Unfortunately, such converters utilize advanced digital CMOS processes with the maximum input voltage below 2 V. We propose a fully integrated linear regulator that enables doubling of the converter input voltage by properly biasing stacked drivers and bridge transistors. By implementing fast digital control the linear regulator meets the transient current demand of the converter without resorting to off-chip decoupling capacitors. In a 90 nm CMOS process, the 2.4V input, 1.2 V output, linear regulator occupies 0.03 mm2 for a plusmn1 A rating. A 288 ps response time and 97.5% current efficiency result in a 2.84times improvement in speed-power figure of merit over previous work


title: Genome-editing Technologies for Gene and Cell Therapy

abstract: Gene therapy has historically been defined as the addition of new genes to human cells. However, the recent advent of genome-editing technologies has enabled a new paradigm in which the sequence of the human genome can be precisely manipulated to achieve a therapeutic effect. This includes the correction of mutations that cause disease, the addition of therapeutic genes to specific sites in the genome, and the removal of deleterious genes or genome sequences. This review presents the mechanisms of different genome-editing strategies and describes each of the common nuclease-based platforms, including zinc finger nucleases, transcription activator-like effector nucleases (TALENs), meganucleases, and the CRISPR/Cas9 system. We then summarize the progress made in applying genome editing to various areas of gene and cell therapy, including antiviral strategies, immunotherapies, and the treatment of monogenic hereditary disorders. The current challenges and future prospects for genome editing as a transformative technology for gene and cell therapy are also discussed.


title: Pharmacokinetic Properties of Single- and Multiple-Dose Pitavastatin Calcium Tablets in Healthy Chinese Volunteers

abstract: Background ::: Pitavastatin is a newly developed 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor approved for the treatment of hyperlipidemia. Pharmacokinetic properties of pitavastatin have been studied previously.


title: Simple regret for infinitely many armed bandits

abstract: We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret of the learner. In this paper, we propose an algorithm aiming at minimizing the simple regret. As in the cumulative regret setting of infinitely many armed bandits, the rate of the simple regret will depend on a parameter β characterizing the distribution of the near-optimal arms. We prove that depending on β, our algorithm is minimax optimal either up to a multiplicative constant or up to a log(n) factor. We also provide extensions to several important cases: when β is unknown, in a natural setting where the near-optimal arms have a small variance, and in the case of unknown time horizon.


title: Mr. Doc: A Doctor Appointment Application System

abstract: Life is becoming too busy to get medical appointments in person and to maintain a proper health care. The main idea of this work is to provide ease and comfort to patients while taking appointment from doctors and it also resolves the problems that the patients has to face while making an appointment. The android application Mr.Doc acts as a client whereas the database containing the doctor's details, patient's details and appointment details is maintained by a website that acts as a server.


title: Tennis stroke detection and classification using miniature wearable IMU device

abstract: This paper presents work related to tennis stroke detection and classification. For arm movement acquisition a miniature wearable IMU device, positioned on the player's forearm (right above the wrist) is proposed and presented. The device uses a MEMS-based accelerometer and gyroscope with 6-DOF. For reliable and accurate tennis stroke detection the information obtained from the accelerometer data is used, and for tennis stroke classification, information from gyroscope data is extracted and processed. The proposed system is able to detect and classify three most common tennis strokes: forehand, backhand, and serve. Because of limited memory and lack of processing power, the proposed algorithms for stroke detection and classification are quite simple, but are nonetheless capable of achieving high classification rate. Overall 98.1% tennis stroke classification accuracy was achieved.


title: SSE: Semantically Smooth Embedding for Knowledge Graphs

abstract: This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into low-dimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. Two lines of embedding strategies are tested, i.e., strategies based on latent distance models and strategies based on tensor factorization techniques. We empirically evaluate SSE on two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. The results demonstrate the superiority and generality of SSE.


title: Circuit and Layout Co-Design for ESD Protection in Bipolar-CMOS-DMOS (BCD) High-Voltage Process

abstract: The n-channel lateral double-diffused metal-oxide- semiconductor (nLDMOS) devices in high-voltage (HV) technologies are known to have poor electrostatic discharge (ESD) robustness. To improve the ESD robustness of nLDMOS, a co-design method combining a new waffle layout structure and a trigger circuit is proposed to fulfill the body current injection technique in this work. The proposed layout and circuit co-design method on HV nLDMOS has successfully been verified in a 0.5-?m 16-V bipolar-CMOS-DMOS (BCD) process and a 0.35- ?m 24-V BCD process without using additional process modification. Experimental results through transmission line pulse measurement and failure analyses have shown that the proposed body current injection technique can significantly improve the ESD robustness of HV nLDMOS.


title: Perceived development of psychological characteristics in male and female elite gymnasts

abstract: This study examined the perceived development of psychological characteristics in male and female elite gymnasts. Fourteen participants (seven females and seven males) were engaged in individual semi-structured interviews, which were subjected to an inductive-deductive analysis procedure. Results revealed the main psychological characteristics perceived to be developed by all the participants pertained to achievement motivation, performance enhancement cognitive skills (e.g., focusing, imagery), and affective and psychosomatic skills (e.g., ability to deal with anxiety; relaxation). Although all the participants perceived that their psychological characteristics were influenced by different sources (e.g., social sport environment; family; sport process) and through a variety of methods (e.g., social support; unpleasant social interactions; competitive experiences), some gender differences were suggested. Family influences were perceived as constant among females, whilst the role of competitive experiences was perceived as predominant amongst males. Results are discussed in the context of previous psychological development literature and theories of gender development.


title: Five-Factor Model personality profiles of drug users

abstract: BackgroundPersonality traits are considered risk factors for drug use, and, in turn, the psychoactive substances impact individuals' traits. Furthermore, there is increasing interest in developing treatment approaches that match an individual's personality profile. To advance our knowledge of the role of individual differences in drug use, the present study compares the personality profile of tobacco, marijuana, cocaine, and heroin users and non-users using the wide spectrum Five-Factor Model (FFM) of personality in a diverse community sample.MethodParticipants (N = 1,102; mean age = 57) were part of the Epidemiologic Catchment Area (ECA) program in Baltimore, MD, USA. The sample was drawn from a community with a wide range of socio-economic conditions. Personality traits were assessed with the Revised NEO Personality Inventory (NEO-PI-R), and psychoactive substance use was assessed with systematic interview.ResultsCompared to never smokers, current cigarette smokers score lower on Conscientiousness and higher on Neuroticism. Similar, but more extreme, is the profile of cocaine/heroin users, which score very high on Neuroticism, especially Vulnerability, and very low on Conscientiousness, particularly Competence, Achievement-Striving, and Deliberation. By contrast, marijuana users score high on Openness to Experience, average on Neuroticism, but low on Agreeableness and Conscientiousness.ConclusionIn addition to confirming high levels of negative affect and impulsive traits, this study highlights the links between drug use and low Conscientiousness. These links provide insight into the etiology of drug use and have implications for public health interventions.


title: Getting Humanoids to Move and Imitate

abstract: A behavior-based approach to structuring and controlling complex robotic systems is described that uses imitation for interaction and learning. The use of basis behaviors, or primitives, lets humanoid robots dance the Macarena, do aerobics and throw objects.


title: InverseRenderNet: Learning Single Image Inverse Rendering

abstract: We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.


title: Exploiting deep neural networks for detection-based speech recognition

abstract: In recent years deep neural networks (DNNs) - multilayer perceptrons (MLPs) with many hidden layers - have been successfully applied to several speech tasks, i.e., phoneme recognition, out of vocabulary word detection, confidence measure, etc. In this paper, we show that DNNs can be used to boost the classification accuracy of basic speech units, such as phonetic attributes (phonological features) and phonemes. This boosting leads to higher flexibility and has the potential to integrate both top-down and bottom-up knowledge into the Automatic Speech Attribute Transcription (ASAT) framework. ASAT is a new family of lattice-based speech recognition systems grounded on accurate detection of speech attributes. In this paper we compare DNNs and shallow MLPs within the ASAT framework to classify phonetic attributes and phonemes. Several DNN architectures ranging from five to seven hidden layers and up to 2048 hidden units per hidden layer will be presented and evaluated. Experimental evidence on the speaker-independent Wall Street Journal corpus clearly demonstrates that DNNs can achieve significant improvements over the shallow MLPs with a single hidden layer, producing greater than 90% frame-level attribute estimation accuracies for all 21 phonetic features tested. Similar improvement is also observed on the phoneme classification task with excellent frame-level accuracy of 86.6% by using DNNs. This improved phoneme prediction accuracy, when integrated into a standard large vocabulary continuous speech recognition (LVCSR) system through a word lattice rescoring framework, results in improved word recognition accuracy, which is better than previously reported word lattice rescoring results.


title: How stigma interferes with mental health care

abstract: Many people who would benefit from mental health services opt not to pursue them or fail to fully participate once they have begun. One of the reasons for this disconnect is stigma; namely, to avoid the label of mental illness and the harm it brings, people decide not to seek or fully participate in care. Stigma yields 2 kinds of harm that may impede treatment participation: It diminishes self-esteem and robs people of social opportunities. Given the existing literature in this area, recommendations are reviewed for ongoing research that will more comprehensively expand understanding of the stigma-care seeking link. Implications for the development of antistigma programs that might promote care seeking and participation are also reviewed.


Automatic visual quality assessment in optical fundus images


title: Skin cancer: more than skin deep.

abstract: PURPOSE ::: To provide the wound care practitioner with an updated overview of the epidemiology, clinical presentation, treatment, and prevention of skin cancer. ::: ::: ::: TARGET AUDIENCE ::: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. ::: ::: ::: OBJECTIVES ::: After participating in this educational activity, the participant should be better able to: 1. Describe the epidemiology, pathophysiology, and clinical presentation of skin cancer. 2. Discuss the diagnosis, management, and prevention of skin cancer.


An Unit Selection based Hindi Text To Speech Synthesis System Using Syllable as a Basic Unit


title: Inner symmetries of relativistic systems, the Laplace-Runge-Lenz symmetry, and the relativistic centre-of-mass

abstract: The talk concerns the inner symmetries of composite relativistic systems, their (generic) relation with the Laplace-Runge-Lenz (LRL) symmetry and the definition of the relativistic centre-of-mass. Global Lorentz-Poincare symmetry implies the existence of LRL symmetry, in a naturally generalized sense, as part of the inner symmetry of these systems, and in a manner which is independent of the internal interaction. The corresponding LRL vectors form the internal moments associated with the Lorentz boost, which, in turn, determines the centre-of-mass.


title: Bringing computational thinking to teachers' training: a workshop review

abstract: In recent years, several visual programming languages and tools are emerging, which allow young students to easily program applications. Particularly, the block-based language used by Scratch has been the standard in most school initiatives to introduce Computational thinking (CT) in courses unrelated to computing. However, CT competences are not specifically included in the curricula of many Higher Education degrees that future teachers of Primary and Secondary Education have to complete. This paper describes a workshop for teachers' training on CT. It is based on the block-based common language of Scratch, but focused on enhancing teachers' skills to develop mobile applications with a tool based on the MIT's AppInventor. This workshop provided some insights on the capabilities of future teachers in the use of programming tools1.


title: Treating dyspareunia caused by vaginal atrophy: a review of treatment options using vaginal estrogen therapy

abstract: Vulvovaginal atrophy (VVA) and dryness are common symptoms of the decline in endogenous production of estrogen at menopause and often result in dyspareunia. Yet while 10% to 40% of women experience discomfort due to VVA, it is estimated that only 25% seek medical help. The main goals of treatment for vaginal atrophy are to improve symptoms and to restore vaginal and vulvar anatomic changes. Treatment choices for postmenopausal dyspareunia resulting from vulvovaginal atrophy will depend on the underlying etiology and might include individualized treatment. A number of forms of vaginal estrogen and manner of delivery are currently available to treat moderate to severe dyspareunia caused by VVA. They all have been shown to be effective and are often the preferred treatment due to the targeted efficacy for urogenital tissues while resulting in only minimal systemic absorption. Both healthcare professionals and patients often find it difficult to broach the subject of sexual problems associated with VVA. However, with minimal effort to initiate a conversation about these problems, healthcare providers can provide useful information to their postmenopausal patients in order to help them each choose the optimal treatment for their needs and symptoms.


Towards Neural Theorem Proving at Scale Anonymous


title: Histogram refinement for texture descriptor based image retrieval

abstract: Texture descriptors such as local binary patterns (LBP) have been successfully employed for feature extraction in image retrieval algorithms because of their high discriminating ability and computational efficiency. In this paper, we propose histogram feature refinement methods for enhancing performance of texture descriptor based content-based image retrieval (CBIR) systems. In the proposed approach for histogram refinement, each pixel in the query and database images is classified into one of the two categories based on the analysis of pixel values in its neighborhood. Local patterns corresponding to two sets of pixels are used to generate two histogram features for each image, effectively resulting in splitting of the original global histogram of texture descriptors into two based on the category of each pixel. Resulting histograms are then concatenated to form a single histogram feature. This study also explores three hybrid frameworks for histogram refinement in CBIR systems. Comparison of histogram features corresponding to query and database images are performed using the relative l1 distance metric. Performance evaluation on three publicly available benchmark image databases namely, GHIM 10000, COREL 1000 database, and Brodatz texture database shows that performances of existing texture descriptor based approaches improve considerably when the proposed histogram feature refinement is incorporated. Specifically, the average precision rate is improved by 6.02%, 5.69%, 4.79%, and 4.21% for LBP, local derivative pattern (LDP), local ternary pattern (LTP), and local tetra pattern (LTrP) descriptors, respectively on GHIM 10000 database. The proposed histogram refinement approaches also provide performance improvement for other texture descriptors considered in this study. A general framework for histogram refinement of texture descriptors.Improves retrieval performance of texture descriptor based CBIR systems.Performance improvement in CBIR validated using 9 texture descriptors.Marginal increase in average image retrieval time with 1.2s in the worst case.


title: Database security - concepts, approaches, and challenges

abstract: As organizations increase their reliance on, possibly distributed, information systems for daily business, they become more vulnerable to security breaches even as they gain productivity and efficiency advantages. Though a number of techniques, such as encryption and electronic signatures, are currently available to protect data when transmitted across sites, a truly comprehensive approach for data protection must also include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. Also, techniques for data integrity and availability specifically tailored to database systems must be adopted. In this respect, over the years, the database security community has developed a number of different techniques and approaches to assure data confidentiality, integrity, and availability. However, despite such advances, the database security area faces several new challenges. Factors such as the evolution of security concerns, the "disintermediation" of access to data, new computing paradigms and applications, such as grid-based computing and on-demand business, have introduced both new security requirements and new contexts in which to apply and possibly extend current approaches. In this paper, we first survey the most relevant concepts underlying the notion of database security and summarize the most well-known techniques. We focus on access control systems, on which a large body of research has been devoted, and describe the key access control models, namely, the discretionary and mandatory access control models, and the role-based access control (RBAC) model. We also discuss security for advanced data management systems, and cover topics such as access control for XML. We then discuss current challenges for database security and some preliminary approaches that address some of these challenges.


Major Self-Mutilations : Castration and Enucleation Prabhat


title: Classification of EEG during imagined mental tasks by forecasting with Elman Recurrent Neural Networks

abstract: The ability to classify EEG recorded while a subject performs varying imagined mental tasks may lay the foundation for building usable Brain-Computer Interfaces as well as improve the performance of EEG analysis software used in clinical settings. Although a number of research groups have produced EEG classifiers, these methods have not yet reached a level of performance that is acceptable for use in many practical applications. We assert that current approaches are limited by their ability to capture the temporal and spatial patterns contained within EEG. In order to address these problems, we propose a new generative technique for EEG classification that uses Elman Recurrent Neural Networks. EEG recorded while a subject performs one of several imagined mental tasks is first modeled by training a network to forecast the signal a single step ahead in time. We show that these models are able to forecast EEG well with an RMSE as low as 0.110. A separate model is then trained over EEG belonging to each class. Classification of previously unseen data is performed by applying each model and assigning the class label associated with the network that produced the lowest forecasting error. This approach is tested on EEG collected from two able-bodied subjects and one subject with a high-level spinal cord injury. Classification rates as high as 93.3% are achieved for a two-task problem with decisions made every second yielding a bitrate of 38.7 bits per minute.


title: Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features

abstract: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.


title: Correlates of social and emotional loneliness in older people: evidence from an English community study

abstract: Objectives: Loneliness is an important influence on quality of life in old age and has been conceptualised as consisting of two dimensions, social and emotional. This article describes analyses that sought to produce models of social and emotional loneliness in older people, using demographic, psychological and health, and social variables.Method: Older people (aged 65+, n = 1255) from the Barnsley metropolitan area of the United Kingdom were recruited randomly from within a stratified sampling frame and received a questionnaire-based interview (response rate: 68.1%). The questionnaire contained items and scales on demographic, psychological and health, and social characteristics, and a validated measure of loneliness that assesses both social and emotional loneliness.Results: Of the respondents, 7.7% were found to be severely or very severely lonely, while another 38.3% were moderately lonely. Social and emotional loneliness shared 19.36% variance. Being male, being widowed, low well-being, low self-esteem, low-income comfort, low contact with family, low contact with friends, low activity, low perceived community integration, and receipt of community care were significant predictors of social loneliness (R = 0.50, R2 = 0.25, F(18, 979) = 18.17, p < 0.001). Being widowed, low well-being, low self-esteem, high activity restriction, low-income comfort, and non-receipt of informal care were significant predictors of emotional loneliness (R = 0.55, R2 = 0.30, F (18, 973) = 23.00, p < 0.001).Conclusion: This study provides further empirical support for the conceptual separation of emotional and social loneliness. Consequently, policy on loneliness in older people should be directed to developing a range of divergent intervention strategies if both emotional and social loneliness are to be reduced.


title: Towards a Secure Mobile Edge Computing Framework for Hajj

abstract: The cloud computing paradigm faces the challenges of providing low latency, high availability, and real-time location-aware services where millions of people are mobile with respect to time and geographic location. In this paper, we propose a mobile edge computing framework that can support real-time, location-aware personalized services to a very large crowd. The framework uses a hybrid of cloud at the server end and fog computing terminals (FCTs) at the crowd edge. The concept of FCT is realized by adding a middle layer acting as a proxy between the user end and cloud infrastructure. Each FCT node covers a geographic zone and provides a subset of services and resources based on the geographic location of a mobile user. When a user moves from one FCT-covered zone to another, the secure handshaking of metadata about the user is shared with the new FCT node. The communication between mobile users’ terminals, such as smartphone and the FCT, is assumed as 4G/5G networks, while the communication between the FCT and cloud is based on a high speed, always available, and reliable Internet connection. The location of each mobile user is made secure and shared according to our novel privacy policy paradigm. The framework is designed to switch between FCT and cloud, depending on the task, network condition, geographic nearness, and resources available within the client unit. We have implemented the framework to support context-aware services to millions of pilgrims that gather together in a very small area of land each year. We will share the inspiring results that we have gathered after initial deployment.


title: A 3D-printed compact dual-circularly polarized corrugated horn with integrated septum polarizer

abstract: This paper presents a Compact Dual-Circularly Polarized Corrugated Horn with Integrated Septum Polarizer in the X-band. Usually such a complicated structure would be fabricated in parts and assembled together. However, exploiting the versatility afforded by Metal 3D-printing, a complete prototype is fabricated as a single part, enabling a compact design. Any variation due to mating tolerance of separate parts is eliminated. The prototype is designed to work from 9.5GHz to 10GHz. It has an impedance match of better than |S11|<−15dB and a gain about 13.4dBic at 9.75GHz. The efficiency is better than 95% in the operating band.


title: Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks

abstract: Cytoarchitectonic parcellations of the human brain serve as anatomical references in multimodal atlas frameworks. They are based on analysis of cell-body stained histological sections and the identification of borders between brain areas. The de-facto standard involves a semi-automatic, reproducible border detection, but does not scale with high-throughput imaging in large series of sections at microscopical resolution. Automatic parcellation, however, is extremely challenging due to high variation in the data, and the need for a large field of view at microscopic resolution. The performance of a recently proposed Convolutional Neural Network model that addresses this problem especially suffers from the naturally limited amount of expert annotations for training. To circumvent this limitation, we propose to pre-train neural networks on a self-supervised auxiliary task, predicting the 3D distance between two patches sampled from the same brain. Compared to a random initialization, fine-tuning from these networks results in significantly better segmentations. We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.


title: Classification of social networking skills for promoting personalized learning of Thai seniors

abstract: Some research proposes that age changing effects on learning performance. Although, age-related degraded perception cannot be rolled-back, it can be improved by training and learning. Since seniors can reach new technology slowly, social media is a way to support their participation in long term learning. However, the level of learning is limited by each personal background knowledge, that each senior supposes to have the learning path which mostly match with their social media knowledge. Therefore, this study proposes the method to classify groups of senior by using useful data mining algorithms. In this case, Random Forest and K-nearest neighbors algorithm (k-NN) model are used. The study conducted with 60 senior between 60–75 years old from seniors school in chiangrai province. Upon participants have done the pretest, these algorithms classifies an senior test results, and assign them into 4 appropriate groups, including Professional, Medium, Less Knowledge, and No Experience. The results show that both algorithms, Random Forest and K-nearest neighbors algorithm (k-NN) provide 95.56% and 93.33% accuracy respectively.


Stacking with Auxiliary Features for Visual Question Answering


Shopping as a Social Activity: Understanding People's Categorical Item Sharing Preferences on Social Networks


title: Sub2Vec: Feature Learning for Subgraphs

abstract: Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction. However, most of the work focuses on distributed representations of nodes that are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we formulate subgraph embedding problem based on two intuitive properties of subgraphs and propose Sub2Vec, an unsupervised algorithm to learn feature representations of arbitrary subgraphs. We also highlight the usability of Sub2Vec by leveraging it for network mining tasks, like community detection and graph classification. We show that Sub2Vec gets significant gains over state-of-the-art methods. In particular, Sub2Vec offers an approach to generate a richer vocabulary of meaningful features of subgraphs for representation and reasoning.


title: Using phrases and document metadata to improve topic modeling of clinical reports

abstract: Display Omitted A topic model is proposed that incorporates document metadata and phrase information.The model is applied to a clinical report dataset to capture medical information.Comparison with existing models shows improved clinical document representation.Document classification shows the model's ability to capture clinical information. Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.


title: Convex principal feature selection

abstract: A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-sparse coefficients may be distributed over several features. Feature selection is an NP-hard combinatorial optimization problem. This paper relaxes and re-formulates the feature selection problem as a convex continuous optimization problem that minimizes a mean-squared-reconstruction error (a criterion optimized by PCA) and considers feature redundancy into account (an important property in PCA and feature selection). We call this new method Convex Principal Feature Selection (CPFS). Experiments show that CPFS performed better than SPCA in selecting features that maximize variance or minimize the mean-squaredreconstruction error.


title: How Taxing is Corruption on International Investors?

abstract: This paper studies the effect of corruption on foreign direct investment. The sample covers bilateral investment from fourteen source countries to forty-five host countries during 1990-91. There are three central findings. (1) A rise in either the tax rate on multinational firms or the corruption level in a host country reduces inward foreign direct investment (FDI). An increase in the corruption level from that of Singapore to that of Mexico is equivalent to raising the tax rate by over twenty percentage points. (2) There is no support for the hypothesis that corruption has a smaller effect on FDI into East Asian host countries. (3) American investors are averse to corruption in host countries, but not necessarily more so than average OECD investors, in spite of the U.S. Foreign Corrupt Practices Act of 1977. On the other hand, there is some weak support for the hypothesis that Japanese investors may be somewhat less sensitive to corruption. Neither American nor Japanese investors treat corruption in East Asia any differently from that in other parts of the world. There are other interesting and sensible findings. For example, consistent with theories that emphasize the importance of networks in trade and investment, sharing a common linguistic tie between the source and host countries and geographic proximity between the two are associated with a sizable increase in the bilateral FDI flow.


title: Genetic Algorithm Based Clustering: A Survey

abstract: This survey gives state-of-the-art of genetic algorithm (GA) based clustering techniques. Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis etc. To enhance the performance of clustering algorithms, Genetic Algorithms (GAs) is applied to the clustering algorithm. GAs are the best-known evolutionary techniques. The capability of GAs is applied to evolve the proper number of clusters and to provide appropriate clustering. This paper present some existing GA based clustering algorithms and their application to different problems and domains.


title: A review of 3D/2D registration methods for image-guided interventions

abstract: Registration of pre- and intra-interventional data is one of the key technologies for image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiology. In this paper, we survey those 3D/2D data registration methods that utilize 3D computer tomography or magnetic resonance images as the pre-interventional data and 2D X-ray projection images as the intra-interventional data. The 3D/2D registration methods are reviewed with respect to image modality, image dimensionality, registration basis, geometric transformation, user interaction, optimization procedure, subject, and object of registration.


title: Action anticipation and motor resonance in elite basketball players

abstract: Using a combination of behavioral measures and transcranial magnetic stimulation (TMS), this study finds that elite basketball players are better at predicting whether a free basketball throw will land in the basket or out and that they also have higher TMS-evoked motor potentials for when the ball misses its mark.


title: Cross-lingual sentiment transfer with limited resources

abstract: We describe two transfer approaches for building sentiment analysis systems without having gold labeled data in the target language. Unlike previous work that is focused on using only English as the source language and a small number of target languages, we use multiple source languages to learn a more robust sentiment transfer model for 16 languages from different language families. Our approaches explore the potential of using an annotation projection approach and a direct transfer approach using cross-lingual word representations and neural networks. Whereas most previous work relies on machine translation, we show that we can build cross-lingual sentiment analysis systems without machine translation or even high quality parallel data. We have conducted experiments assessing the availability of different resources such as in-domain parallel data, out-of-domain parallel data, and in-domain comparable data. Our experiments show that we can build a robust transfer system whose performance can in some cases approach that of a supervised system.


In Vitro and In Vivo Anticancer Activity of Root Extracts of Sansevieria liberica Gerome and Labroy (Agavaceae)


The Role of Epigenetic Change in Autism Spectrum Disorders


title: Using kNN Model for Automatic Feature Selection

abstract: This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications – those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation based on inductive information in each representative obtained bykNN model. We have evaluated the performance of the proposed kNN model-based feature selection method on toxicity dataset Phenols with two different endpoints. Experimental results indicate that the proposed feature selection method has a significant improvement in the classification accuracy for the trial dataset.


title: Training Deep Networks with Structured Layers by Matrix Backpropagation

abstract: Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fields, and from the simplicity and scalability of the gradient-descent training procedure based on backpropagation. An open problem is the inclusion of layers that perform global, structured matrix computations like segmentation (e.g. normalized cuts) or higher-order pooling (e.g. log-tangent space metrics defined over the manifold of symmetric positive definite matrices) while preserving the validity and efficiency of an end-to-end deep training framework. In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures. At the heart of our methodology is the development of the theory and practice of backpropagation that generalizes to the calculus of adjoint matrix variations. The proposed matrix backpropagation methodology applies broadly to a variety of problems in machine learning or computational perception. Here we illustrate it by performing visual segmentation experiments using the BSDS and MSCOCO benchmarks, where we show that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take advantage of such global layers.


title: Deep but lightweight neural networks for fish detection

abstract: The explosive growth of the underwater images make the demand for automatic accurate object detection more and more urgent. In this paper, we introduced a deep but lightweight neural network to detect fishes. It achieved the state-of-the-art accuracy for fish detection on the dataset of ImageCLEF, which includes 24,277 fish images belonging to 12 classes. Compared with the common used detection network, such as Faster R-CNN, we change the structure of convolution layers by using some building blocks including concatenated ReLU, Inception, and HyperNet. The final network obtained best results of 89.95% mAP(mean average precision), 7.25% higher than the Faster R-CNN network on the same dataset.


title: Simulated augmented reality windshield display as a cognitive mapping aid for elder driver navigation

abstract: A common effect of aging is decline in spatial cognition. This is an issue for all elders, but particularly for elder drivers. To address this driving issue, we propose a novel concept of an in-vehicle navigation display system that displays navigation information directly onto the vehicle's windshield, superimposing it on the driver's view of the actual road. An evaluation of our simulated version of this display shows that it results in a significant reduction in navigation errors and distraction-related measures compared to a typical in-car navigation display for elder drivers. These results help us understand how context-sensitive information and a simulated augmented reality representation can be combined to minimize the cognitive load in translating between virtual/information spaces and the real world.


title: Keeping up with the jammers: Observe-and-adapt algorithms for studying mutually adaptive opponents

abstract: Abstract Securing the wireless medium is essential to provide the ubiquitous wireless services that we desire. Many studies have explored adaptive attackers and defenders but few have explored the interaction when both players adapt. In this work, we explore the design of an adaptive defender and attacker using an observe-and-adapt strategy. We simulate these algorithms and explore the interaction of adaptive players in two different jamming games. We show that when only one player adapts they improve their performance but when both players adapt the outcome it is often reflective of biases in the game.


title: NEDINE: AN INTELLIGENT GLOBAL NEWS PUBLISHING AND DISTRIBUTION SERVICE BASED ON WEB SERVICES AND PEER-TO-PEER TECHNOLOGIES

abstract: This invention relates to a process for polymerizing vinyl monomers by the emulsion polymerization technique using a new emulsifier system to produce latices or paste resins which have improved plastisol bloom properties and with little or no polymer buildup in the reactor. The polymerization process is conducted in an aqueous medium using a free radical producing polymerization catalyst in the presence of an emulsifier system comprising a sulfonated type soap selected from sulfonated phthalates and sulfonated benzoates.


title: Improved direct back EMF detection for sensorless brushless DC (BLDC) motor drives

abstract: Improved back EMF detection circuits for low voltage/low speed and high voltage sensorless BLDC motor drives are presented in this paper. The improvements are based on the direct back EMF sensing method from our previous research work described in reference, which describes a technique for directly extracting phase back EMF information without the need to sense or re-construct the motor neutral. The reference method is not sensitive to switching noise and requires no filtering, achieving much better performance than traditional back EMF sensing scheme. A complementary PWM (synchronous rectification) is proposed to reduce the power dissipation in power devices for low voltage applications. In order to further extend the sensorless BLDC system to lower speed, a pre-conditioning circuit is proposed to amplify the back EMFs at very low speed. As a result, the brushless DC motor can run at lower speed with the improved back EMF sensing scheme. On the other hand, another improved detection circuit is presented for high voltage applications to overcome the delaying problem caused by large sensing resistors. The detailed circuit models are analyzed and experimental results verify the analysis.


title: Cloud Databases for Internet-of-Things Data

abstract: The Internet of Things (IoT) is posing new challenges and opportunities for data management and analysis techniques. One of the major problems is how to handle an increasing amount of data, with a variety of data types and data sources, in order to meet application-specific performance requirements. In this article, we address the suitability of different types of databases for storing and accessing IoT data in the cloud. Specifically, we compare the performance of SQL (i.e., MySQL) and NoSQL databases (i.e., MongoDB, CouchDB, and Redis) in a cloud environment by using different types of IoT data, namely, sensor readings and multimedia. We conducted a performance evaluation of the considered database systems through extensive experiments in the cloud. Our results show that SQL and NoSQL databases are both viable for IoT data, even though there is a high variability in the performance of the different NoSQL databases considered.


title: Contextual deep CNN based hyperspectral classification

abstract: In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral image. The initial spatial and spectral feature maps obtained from applying the variable size convolutional filters are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through fully convolutional layers that eventually predict the corresponding label of each pixel vector. The proposed approach is tested on the Indian Pines data and performance comparison shows enhanced classification performance of the proposed approach over the current state of the art.


title: Component-Based Representation in Automated Face Recognition

abstract: This paper presents a framework for component-based face alignment and representation that demonstrates improvements in matching performance over the more common holistic approach to face alignment and representation. This work is motivated by recent evidence from the cognitive science community demonstrating the efficacy of component-based facial representations. The component-based framework presented in this paper consists of the following major steps: 1) landmark extraction using Active Shape Models (ASM), 2) alignment and cropping of components using Procrustes Analysis, 3) representation of components with Multiscale Local Binary Patterns (MLBP), 4) per-component measurement of facial similarity, and 5) fusion of per-component similarities. We demonstrate on three public datasets and an operational dataset consisting of face images of 8000 subjects, that the proposed component-based representation provides higher recognition accuracies over holistic-based representations. Additionally, we show that the proposed component-based representations: 1) are more robust to changes in facial pose, and 2) improve recognition accuracy on occluded face images in forensic scenarios.


title: Towards an Open Government Data Success Model : A case study from Indonesia

abstract: Governments around the world are following the global open government movement by initiating Open Government Data (OGD) programs. They are motivated by the claim that it will generate economic and social value. However, the success of an OGD initiative will only be realized when OGD is used and when the use results in the creation of public value. Even though a plethora of academic research has examined various aspects of OGD initiatives, the question regarding how OGD generates public value remains unaddressed. This paper aims to develop an OGD success evaluation model that offers a systematic way of understanding how OGD creates public value. For this purpose, we first present and explore public value generation through an OGD initiative in the context of Indonesia’s 2014 presidential election. Subsequently, we propose an OGD success evaluation model that explicates how service, information and system quality affect the intention to use OGD and user satisfaction, which in turn lead to the creation of public values. This paper contributes to the literature by applying the Information Systems Success Model of DeLone and McLean in the context of OGD. It extends their model by adding the concepts of social media quality. Since the model is conceptual, further empirical research is needed to test the model.


title: Failure Diagnosis for Distributed Systems Using Targeted Fault Injection

abstract: This paper introduces a novel approach to automating failure diagnostics in distributed systems by combining fault injection and data analytics. We use fault injection to populate the database of failures for a target distributed system. When a failure is reported from production environment, the database is queried to find “matched” failures generated by fault injections. Relying on the assumption that similar faults generate similar failures, we use information from the matched failures as hints to locate the actual root cause of the reported failures. In order to implement this approach, we introduce techniques for (i) reconstructing end-to-end execution flows of distributed software components, (ii) computing the similarity of the reconstructed flows, and (iii) performing precise fault injection at pre-specified executing points in distributed systems. We have evaluated our approach using an OpenStack cloud platform, a popular cloud infrastructure management system. Our experimental results showed that this approach is effective in determining the root causes, e.g., fault types and affected components, for 71-100 percent of tested failures. Furthermore, it can provide fault locations close to actual ones and can easily be used to find and fix actual root causes. We have also validated this technique by localizing real bugs that occurred in OpenStack.


title: Ability of a beta-casein phosphopeptide to modulate the precipitation of calcium phosphate by forming amorphous dicalcium phosphate nanoclusters.

abstract: The ability of casein in the form of colloidal-sized casein micelles to modulate the phase separation of calcium phosphate during milk secretion is adapted to produce nanometre-sized particles of calcium phosphate stabilized by a casein phosphopeptide (nanoclusters). The nanoclusters were prepared from an undersaturated solution of salts and the peptide by raising the pH homogeneously from about 5.5 to 6.7 with urea plus urease. Chemical analysis and IR spectroscopy showed that they comprise an amorphous dicalcium phosphate bound to the phosphopeptide. Multinuclear NMR spectroscopy of the cluster solutions showed that the small ions and free peptide in the solution were in a state of dynamic exchange with the nanoclusters. The peptide is linked to the calcium phosphate through its sequence of phosphorylated residues, but, in a proportion of adsorbed conformational states, the termini retain the conformational freedom of the unbound peptide. The ability of casein to form nanoclusters in milk suggests a more general mechanism for avoiding pathological calcification and regulating calcium flow in tissues and biological fluids exposed to or containing high concentrations of calcium.


title: The impact of computerized accounting system on financial reporting in the ministry of local government of Rwanda

abstract: The study focuses on the impact of computerized accounting system on financial reporting in the Ministry of Local Government (MINALOC) in Rwanda by evaluating : the nature of computerized accounting system used by the Ministry, how computerization of accounting has affected the generation of financial reports, and the extent to which computerized accounting contributes to financial reporting in the MINALOC. The study will help the management of the Ministry to understand the importance of using accounting software packages in preparing quality and reliable financial reports. End users of computerized accounting system will also benefit from quick financial transactions and services facilitated by the use of accounting software packages available in the Ministry. A survey questionnaire was administered on a sample of 65 respondents randomly selected from a population of 110. From the findings, 98% of the respondents admit that the Ministry uses both cash based and accrual based system of computerized accounting, 38% of the respondents agreed that computerized accounting improves accountability, while 31% of the respondents maintain that the system provides financial statements on time. With increased improvements and versions of accounting packages, the study recommends that finance and accounting staff should have constant and continuous training by the authorized dealers of the packages so that they remain well equipped with the knowledge and experience of the package. There is also need for more internal audit reviews to appraise and check the strength of the instituted controls within the accounting system in the Ministry of Local government of Rwanda.


title: Combinational Logic Design Using Six-Terminal NEM Relays

abstract: This paper presents techniques for designing nanoelectromechanical relay-based logic circuits using six-terminal relays that behave as universal logic gates. With proper biasing, a compact 2-to-1 multiplexer can be implemented using a single six-terminal relay. Arbitrary combinational logic functions can then be implemented using well-known binary decision diagram (BDD) techniques. Compared to a CMOS-style implementation using four-terminal relays, the BDD-based implementation can result in lower area without major impact on performance metrics such as delay, and energy (when the relays are scaled to small dimensions). Although it is possible to implement any combinational circuit with a single mechanical delay, the relay count can be significantly reduced for complex logic functions by allowing multiple mechanical delays.


title: Associations of diet quality with cognition in children – the Physical Activity and Nutrition in Children Study

abstract: Evidence on the associations of dietary patterns with cognition in children is limited. Therefore, we investigated the associations of the Baltic Sea Diet Score (BSDS) and the Dietary Approaches to Stop Hypertension (DASH) score with cognition in children. The present cross-sectional study sample included 428 children aged 6-8 years (216 boys and 212 girls). The BSDS and the DASH score were calculated using data from 4 d food records, higher scores indicating better diet quality. Cognition was assessed by the Raven's Coloured Progressive Matrices (CPM) score, a higher score indicating better cognition. Among all children, the BSDS (standardised regression coefficient β = 0·122, P =0·012) and the DASH score (β = 0·121, P =0·015) were directly associated with the Raven's CPM score. Among boys, a lower BSDS (β = 0·244, P< 0·001) and a lower DASH score (β = 0·202, P= 0·003) were related to a lower Raven's CPM score. Boys in the lowest quartile of the BSDS (22·5 v. 25·3, P= 0·029) and the DASH score (22·4 v. 25·7, P= 0·008) had a lower Raven's CPM score than those in the highest quartile of the corresponding score. Among girls, the BSDS or the DASH score were not associated with cognition. In conclusion, a poorer diet quality was associated with worse cognition in children, and the relationship was stronger in boys than in girls.


title: Clone: Solving Weighted Max-SAT in a Reduced Search Space

abstract: We introduce a new branch-and-bound Max-SAT solver, Clone, which employs a novel approach for computing lower bounds. This approach allows Clone to search in a reduced space. Moreover, Clone is equipped with novel techniques for learning from soft conflicts. Experimental results show that Clone performs competitively with the leading Max-SAT solver in the broadest category of this year's Max-SAT evaluation and outperforms last year's leading solvers.


title: Big Other: Surveillance Capitalism and the Prospects of an Information Civilization

abstract: This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The institutionalizing practices and operational assumptions of Google Inc. are the primary lens for this analysis as they are rendered in two recent articles authored by Google Chief Economist Hal Varian. Varian asserts four uses that follow from computer-mediated transactions: ‘data extraction and analysis,’ ‘new contractual forms due to better monitoring,’ ‘personalization and customization,’ and ‘continuous experiments.’ An examination of the nature and consequences of these uses sheds light on the implicit logic of surveillance capitalism and the global architecture of computer mediation upon which it depends. This architecture produces a distributed and largely uncontested new expression of power that I christen: ‘Big Other.’ It is constituted by unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behavior while producing new markets of behavioral prediction and modification. Surveillance capitalism challenges democratic norms and departs in key ways from the centuries-long evolution of market capitalism.


title: Strategies for increasing the operating frequency range of vibration energy harvesters: a review

abstract: This paper reviews possible strategies to increase the operational frequency range of vibration-based micro-generators. Most vibration-based micro-generators are spring-mass-damper systems which generate maximum power when the resonant frequency of the generator matches the frequency of the ambient vibration. Any difference between these two frequencies can result in a significant decrease in generated power. This is a fundamental limitation of resonant vibration generators which restricts their capability in real applications. Possible solutions include the periodic tuning of the resonant frequency of the generator so that it matches the frequency of the ambient vibration at all times or widening the bandwidth of the generator. Periodic tuning can be achieved using mechanical or electrical methods. Bandwidth widening can be achieved using a generator array, a mechanical stopper, non-linear (e.g. magnetic) springs or bi-stable structures. Tuning methods can be classified into intermittent tuning (power is consumed periodically to tune the device) and continuous tuning (the tuning mechanism is continuously powered). This paper presents a comprehensive review of the principles and operating strategies for increasing the operating frequency range of vibration-based micro-generators presented in the literature to date. The advantages and disadvantages of each strategy are evaluated and conclusions are drawn regarding the relevant merits of each approach.


title: Low Voltage Analog Circuit Design Techniques: A Tutorial SUMMARY

abstract: SUMMARY Low voltage (LV) analog circuit design techniques are addressed in this tutorial. In particular, (i) technology considerations; (ii) transistor model capable to provide performance and power tradeoffs; (iii) low voltage implementation techniques capable to reduce the power supply requirements, such as bulk-driven, floating-gate, and self-cascode MOSFETs; (iv) basic LV building blocks; (v) multi-stage frequency compensation topologies; and


title: Testing Software Design Modeled by Finite-State Machines

abstract: We propose a method of testing the correctness of control structures that can be modeled by a finite-state machine. Test results derived from the design are evaluated against the specification. No "executable" prototype is required. The method is based on a result in automata theory and can be applied to software testing. Its error-detecting capability is compared with that of other approaches. Application experience is summarized.


PM Synchronous Machine PMSM Performance Improvement


title: Bias-Induced Healing of $V_{\text {min}}$ Failures in Advanced SRAM Arrays

abstract: A novel bias-induced healing of address specific failing bits in VLSI SRAM functional arrays is demonstrated for the first time in advanced CMOS nodes. Aging effects due to bias temperature instability (BTI) resulting in device shifts are a well-known reliability problem in advanced CMOS technologies. We propose and demonstrate a method of exploiting the BTI mechanism and the addressing capability of SRAM to recover bits that fail during stress. Recovery of failures using this method is demonstrated in both 20- and 14-nm technology nodes in VLSI SRAM arrays. This method introduces the possibility of self-healing SRAM arrays and overcomes many of the limitations of the conventional industry voltage-guard-band approach.


title: Urban Sprawl Mapping and Land Use Change Detection Using Remote Sensing and GIS

abstract: Urban growth identification, quantification, and the knowledge of rate and trends of growth helps in determining the changes associated with land use and land cover properties and in regional planning with better infrastructure in environmentally sound way. Spatial and temporal technologies such as Remote Sensing, Geographic Information System (GIS) and Global Positioning System (GPS) are very helpful in such type of studies. This paper focuses on urban growth pattern analyses being carried out for urban sprawl in Varanasi city. Various GIS layers such as built-up area; agricultural land; vegetation/forest land; water bodies/wet land; brick kiln; rivers; DLW (industrial area); BHU (educational area), road network, city boundary etc. were generated using data such as the Survey of India toposheet and Remote sensing imagery. Spatial changes in built-up area and the pattern of sprawl were studied using GIS. This study revealed that in Varanasi there is remarkable decrease in agricultural areas and tremendous growth in Built-ups. About 37.57% of agricultural land is transformed to other land use features. Rate of change agricultural land to other land use is observed 210.80ha/yr. There is no brick kiln in city area during the 2010 while in 1976; it covered the total area of 143.14ha. Built-ups increased by 7325.72ha, with change rate of 215.46 ha/yr. Study showed decreased in water bodies by 15.13 ha. There is no change in the area of BHU, DLW, River Ganga and Varuna.


Y 2004 Theory of resistor networks: The two-point resistance


title: Centroid estimation based on MSER detection and Gaussian Mixture Model

abstract: This paper proposes a sub-pixel centroid estimation algorithm for star field image recorded by CCD sensor. To handle the connected as well as partially overlapped star spots in a star region, we involve the the MSER (Maximally Stable Extremal Region) detector to discover the star spots and related characteristics of latent star spots in a star region. After extracting the star regions, we will estimate the locations of centroids in each star region simultaneously using the GMM (Gaussian Mixture Model) and Expectation-maximization (EM) algorithm, instead of segment each star spots in a star region. Using our algorithm, overlapped star spots can be discovered easily and their centroids can also be estimated effectively. Experiments on simulation data sets demonstrate effectiveness and robustness of this algorithm.


title: Abstract The Role of Social Presence in Establishing Loyalty in e-Service Environments

abstract: Compared to offline shopping, the online shopping experience may be viewed as lacking human warmth and sociability as it is more impersonal, anonymous, automated and generally devoid of face-to-face interactions. Thus, understanding how to create customer loyalty in online environments (e-Loyalty) is a complex process. In this paper a model for e-Loyalty is proposed and used to examine how varied conditions of social presence in a B2C e-Services context influence e-Loyalty and its antecedents of perceived usefulness, trust and enjoyment. This model is examined through an empirical study involving 185 subjects using structural equation modeling techniques. Further analysis is conducted to reveal gender differences concerning hedonic elements in the model on e-Loyalty.


title: Investigating the Information Transfer Efficiency of a 3x3 Watch-back Tactile Display

abstract: A watch-back tactile display (WBTD) is expected to be a viable supplement to the user interface limitations of a smartwatch. However, its design requires that many design parameters such as tactor types and stimulus patterns be determined. We conducted a series of experiments to explore the design space of a WBTD consisting of 3×3 tactors. We demonstrated that tactor types and the temporal patterns and locus of a stimulus produce statistically significant effects on the efficiency of a WBTD. The experimental results can act as a practical guideline for the design of an efficient WBTD.


title: Feeding the world: genetically modified crops versus agricultural biodiversity

abstract: The growing demand for food poses major challenges to humankind. We have to safeguard both biodiversity and arable land for future agricultural food production, and we need to protect genetic diversity to safeguard ecosystem resilience. We must produce more food with less input, while deploying every effort to minimize risk. Agricultural sustainability is no longer optional but mandatory. There is still an on-going debate among researchers and in the media on the best strategy to keep pace with global population growth and increasing food demand. One strategy favors the use of genetically modified (GM) crops, while another strategy focuses on agricultural biodiversity. Here, we discuss two obstacles to sustainable agriculture solutions. The first obstacle is the claim that genetically modified crops are necessary if we are to secure food production within the next decades. This claim has no scientific support, but is rather a reflection of corporate interests. The second obstacle is the resultant shortage of research funds for agrobiodiversity solutions in comparison with funding for research in genetic modification of crops. Favoring biodiversity does not exclude any future biotechnological contributions, but favoring biotechnology threatens future biodiversity resources. An objective review of current knowledge places GM crops far down the list of potential solutions in the coming decades. We conclude that much of the research funding currently available for the development of GM crops would be much better spent in other research areas of plant science, e.g., nutrition, policy research, governance, and solutions close to local market conditions if the goal is to provide sufficient food for the world’s growing population in a sustainable way.


title: Driving with Binocular Visual Field Loss? A Study on a Supervised On-Road Parcours with Simultaneous Eye and Head Tracking

abstract: Post-chiasmal visual pathway lesions and glaucomatous optic neuropathy cause binocular visual field defects (VFDs) that may critically interfere with quality of life and driving licensure. The aims of this study were (i) to assess the on-road driving performance of patients suffering from binocular visual field loss using a dual-brake vehicle, and (ii) to investigate the related compensatory mechanisms. A driving instructor, blinded to the participants' diagnosis, rated the driving performance (passed/failed) of ten patients with homonymous visual field defects (HP), including four patients with right (HR) and six patients with left homonymous visual field defects (HL), ten glaucoma patients (GP), and twenty age and gender-related ophthalmologically healthy control subjects (C) during a 40-minute driving task on a pre-specified public on-road parcours. In order to investigate the subjects' visual exploration ability, eye movements were recorded by means of a mobile eye tracker. Two additional cameras were used to monitor the driving scene and record head and shoulder movements. Thus this study is novel as a quantitative assessment of eye movements and an additional evaluation of head and shoulder was performed. Six out of ten HP and four out of ten GP were rated as fit to drive by the driving instructor, despite their binocular visual field loss. Three out of 20 control subjects failed the on-road assessment. The extent of the visual field defect was of minor importance with regard to the driving performance. The site of the homonymous visual field defect (HVFD) critically interfered with the driving ability: all failed HP subjects suffered from left homonymous visual field loss (HL) due to right hemispheric lesions. Patients who failed the driving assessment had mainly difficulties with lane keeping and gap judgment ability. Patients who passed the test displayed different exploration patterns than those who failed. Patients who passed focused longer on the central area of the visual field than patients who failed the test. In addition, patients who passed the test performed more glances towards the area of their visual field defect. In conclusion, our findings support the hypothesis that the extent of visual field per se cannot predict driving fitness, because some patients with HVFDs and advanced glaucoma can compensate for their deficit by effective visual scanning. Head movements appeared to be superior to eye and shoulder movements in predicting the outcome of the driving test under the present study scenario.


title: PyStruct: learning structured prediction in python

abstract: Structured prediction methods have become a central tool for many machine learning applications. While more and more algorithms are developed, only very few implementations are available. ::: ::: PyStruct aims at providing a general purpose implementation of standard structured prediction methods, both for practitioners and as a baseline for researchers. It is written in Python and adapts paradigms and types from the scientific Python community for seamless integration with other projects.


title: Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN

abstract: Chest radiography (chest X-ray) is a low-cost yet effective and widely used medical imaging procedures. The lacking of qualified radiologist seriously limits the applicability of the technique. We explore the possibility of designing a computer-aided diagnosis for chest X-rays using deep convolutional neural networks. Using a real-world dataset of 16,000 chest X-rays with natural language diagnosis reports, we can train a multi-class classification model from images and preform accurate diagnosis, without any prior domain knowledge.


title: CTArcade: Computational thinking with games in school age children

abstract: a b s t r a c t We believe that children as young as ten can directly benefit from opportunities to engage in computa- tional thinking. One approach to provide these opportunities is to focus on social game play. Understand- ing game play is common across a range of media and ages. Children can begin by solving puzzles on paper, continue on game boards, and ultimately complete their solutions on computers. Through this process, learners can be guided through increasingly complex algorithmic thinking activities that are built from their tacit knowledge and excitement about game play. This paper describes our approach to teaching computational thinking skills without traditional programming—but instead by building on children's existing game playing interest and skills. We built a system called CTArcade, with an initial game (Tic- Tac-Toe), which we evaluated with 18 children aged 10-15. The study shows that our particular approach helped young children to better articulate algorithmic thinking patterns, which were tacitly present when they played naturally on paper, but not explicitly apparent to them until they used the CTArcade interface. © 2014 Elsevier B.V. All rights reserved.


title: MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

abstract: Single cell experimental techniques reveal transcriptomic and epigenetic heterogeneity among cells, but how these are related is unclear. We present MATCHER, an approach for integrating multiple types of single cell measurements. MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type. Using scM&T-seq and sc-GEM data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences. MATCHER also reveals new insights into the dynamic interplay between the transcriptome and epigenome in single embryonic stem cells and induced pluripotent stem cells.


title: Bandwidth Enhancement of a Horizontally Polarized Omnidirectional Antenna by Adding Parasitic Strips

abstract: A horizontally polarized omnidirectional antenna with enhanced impedance bandwidth is presented in this letter. The proposed antenna consists of a feeding network, four printed dipole elements with etched slots, parasitic strips, and director elements. Four identically curved and printed dipole elements are placed in a square array and fed by a feeding network with uniform magnitude and phase; thus, the proposed antenna can achieve an omnidirectional radiation. To enhance the impedance bandwidth, parasitic strips and etched slots are introduced to produce additional lower and upper resonant frequencies, respectively. By utilizing four director elements, the gain variation in the horizontal plane can be improved, especially for the upper frequency band. With the structure, a reduced size of 0.63 λ ::: L ::: × 0.63 λ ::: L ::: × 0.01 λ ::: L ::: ( λ ::: L ::: is the free-space wavelength at the lowest frequency) is obtained. The proposed antenna is designed and fabricated. Measurement results reveal that the proposed antenna can provide an impedance bandwidth of 84.2% (1.58-3.88 GHz). Additionally, the gain variation in the horizontal plane is less than 1.5 dB over the frequency band 1.58-3.50 GHz, and increased to 2.2 dB at 3.80 GHz. Within the impedance bandwidth, the cross-polarization level is less than -23 dB in the horizontal plane.


title: Notability Determination for Wikipedia

abstract: Being the ever-growing online encyclopedia, Wikipedia requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these named entities to meet "notability" standards for their article inclusion. In this paper, we evaluate named entities for their notability by using reliability and entity salience features. Evaluations of our system provide evidence for the viability of our solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.


title: Navigation system for visually impaired people

abstract: Navigation assistance for visually impaired (NAVI) refers to systems that are able to assist or guide people with vision loss, ranging from partially sighted to totally blind, by means of sound commands. Many researchers are working to assist visually impaired people in different ways like voice based assistance, ultrasonic based assistance, camera based assistance and in some advance way researchers are trying to give transplantation of real eyes with robotic eyes which can capable enough to plot the real image over patient retina using some biomedical technologies. In other way creating a fusion of sensing technology and voice based guidance system some of the products were developed which could give better result than individual technology. There are some limitation in system like obstacle detection which could not see the object but detection the object and camera based system can't work properly in different light level so the proposed system is a fusion of color sensing sensor and the obstacle sensor along with the voice based assistance system. The main idea of the proposed system to make person aware of path he is walking and also the obstacle in the path.


title: Compact Hash Tables for High-Performance Traffic Classification on Multi-core Processors

abstract: Traffic classification is one of the kernel applications in network management. Many Machine Learning (ML) traffic classification algorithms are based on decision-trees. While most of the existing implementations of decision-trees are hardwarebased, a new trend in network applications is to use softwarebased solutions. The decision-tree used for traffic classification is highly unbalanced, it is challenging to achieve high performance on multi-core platforms. In this paper, we present a high-throughput and low-latency traffic classification engine on multi-core platforms. We convert the decision-tree used in the C4.5 algorithm into multiple compact tables. All the compact tables are searched in parallel, efficient hashing techniques are employed to reduce the processing latency. The outcomes from all the tables are merged into the final classification result. High throughput can be sustained even if we scale up (1) the number of concurrent traffic classifiers, (2) the number of decision-tree leaves and (3) the number of features examined during the classification process. We prototype our design on state-of-the-art AMD and Intel multi-core platforms. For a typical C4.5 decisiontree consisting of 92 leaf nodes and 7 flow-level features, we achieve 134:15 Million Lookups Per Second (MLPS) throughput and 238:53 ns processing latency per lookup. Even for highly unbalanced decision-trees or large decision-trees consisting of up to 2K leaf nodes, our traffic classification engine sustains high throughput and low latency without sacrificing classification accuracy (age; 98.15%). We achieve 2.7× throughput compared with the classic C4.5 decision-tree-based implementations, and at least 12× speed-up compared with the existing traffic classifiers on multi-core platforms.


title: Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

abstract: Image forgery is a major issue in today's digital publishing and printing. Now a day’s system can be used for forensic purpose to validate the authenticity of an image. In this paper we present an approach for image forgery authentication. We observe that a non morphed and non forged image shows homogeneity in non spectral domain. This homogeneity is lost when any forgery or morphing is applied on the images. We therefore apply a set of transform over the images. We combine DCT statistics, LBP features with curvelet statistics and Gabor transform of the images to represent an image in the transformed domain. CASIA image dataset with seven thousand authentic and same numbers of tempered images is used to verify the technique. We divide the dataset into equal halves to perform training and testing. Transformed images are used to train Hidden Markov model as HMM can extract probabilistic state information from a large statistical model. A test images is tested in transformed domain by HMM with log likelihood estimator. In case HMM returns an indeterminist result or multiple subset of result, the transformed test image is tested with two class SVM classifier with RBF kernel. Results show that the accuracy of the system is over 89% for 500 test instances.


title: Comparison of different feature sets for identification of variants in progressive aphasia

abstract: We use computational techniques to extract a large number of different features from the narrative speech of individuals with primary progressive aphasia (PPA). We examine several different types of features, including part-of-speech, complexity, context-free grammar, fluency, psycholinguistic, vocabulary richness, and acoustic, and discuss the circumstances under which they can be extracted. We consider the task of training a machine learning classifier to determine whether a participant is a control, or has the fluent or nonfluent variant of PPA. We first evaluate the individual feature sets on their classification accuracy, then perform an ablation study to determine the optimal combination of feature sets. Finally, we rank the features in four practical scenarios: given audio data only, given unsegmented transcripts only, given segmented transcripts only, and given both audio and segmented transcripts. We find that psycholinguistic features are highly discriminative in most cases, and that acoustic, context-free grammar, and part-of-speech features can also be important in some circumstances.


title: Unified Impedance and Admittance Control

abstract: Impedance and Admittance Control are two distinct implementations of the same control goal. It is well known that their stability and performance properties are complementary. In this paper, we present a hybrid system approach, which incorporates Impedance and Admittance Control as two extreme cases of one family of controllers. This approach allows to continuously switch and interpolate between Impedance and Admittance Control. We compare the basic stability and performance properties of the resulting controllers by means of an extensive case study of a one-dimensional system and present an experimental evaluation using the KUKA-DLR-lightweight arm.


title: CONTRAST-ENHANCED PORTAL MAGNETIC RESONANCE ANGIOGRAPHY IN DOGS WITH SUSPECTED CONGENITAL PORTAL VASCULAR ANOMALIES

abstract: Contrast-enhanced multiphase magnetic resonance angiography (CE-MRA) was used in 17 dogs with a suspected congenital portal vascular anomaly. Portal vascular anomalies were identified in 16 of the 17 dogs. Eleven had a single intrahepatic portocaval shunt (two central divisional, three right divisional, and six left divisional), one dog had a double intrahepatic portocaval shunt, one dog had a hepatic arteriovenous malformation, one dog had a complex intrahepatic porto-caval shunt. Two dogs had an extrahepatic portosystemic shunt and no shunt was identified in one dog. Total imaging time was <10 min and image quality was good to excellent in all dogs. Portal CE-MRA is a feasible, fast and non invasive technique to diagnose portal vascular anomalies in dogs, with a large field-of-view and good anatomic depiction of the abnormal vessels. Based on these results, CE-MRA is an efficient imaging technique for the diagnosis of portal vascular anomalies in dogs.


title: A 5.4-Gbit/s Adaptive Continuous-Time Linear Equalizer Using Asynchronous Undersampling Histograms

abstract: We demonstrate a new type of adaptive continuous-time linear equalizer (CTLE) based on asynchronous undersampling histograms. Our CTLE automatically selects the optimal equalizing filter coefficient among several predetermined values by searching for the coefficient that produces the largest peak value in histograms obtained with asynchronous undersampling. This scheme is simple and robust and does not require clock synchronization for its operation. A prototype chip realized in 0.13-μm CMOS technology successfully achieves equalization for 5.4-Gbit/s 231 - 1 pseudorandom bit sequence data through 40-, 80-, and 120-cm PCB traces and 3-m DisplayPort cable. In addition, we present the results of statistical analysis with which we verify the reliability of our scheme for various sample sizes. The results of this analysis are confirmed with experimental data.


title: Smart cities: A conjuncture of four forces

abstract: Despite the ongoing discussion of the recent years, there is no agreed definition of a ‘smart city’, while strategic planning in this field is still largely unexplored. Inspired by this, the purpose of this paper was to identify the forces shaping the smart city conception and, by doing so, to begin replacing the currently abstract image of what it means to be one. The paper commences by dividing the recent history of smart cities into two large sections – urban futures and the knowledge and innovation economy. The urban futures strand shows that technology has always played an important role in forward-looking visions about the city of the future. The knowledge and innovation economy strand shows that recent technological advancements have introduced a whole new level of knowledge management and innovation capabilities in the urban context. The paper proceeds to explicate the current technology push and demand pull for smart city solutions. On one hand, technology advances rapidly and creates a booming market of


title: Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization

abstract: Cloud computing services are becoming integral part of people's daily life. These services are supported by infrastructure known as Internet data center (IDC). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. Both academia and industry have paid great attention to energy management of IDCs. This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets. We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center. The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large.


title: Enforcing temporal logic specifications via reinforcement learning

abstract: We consider the problem of controlling a system with unknown, stochastic dynamics to achieve a complex, time-sensitive task. An example of this problem is controlling a noisy aerial vehicle with partially known dynamics to visit a pre-specified set of regions in any order while avoiding hazardous areas. In particular, we are interested in tasks which can be described by signal temporal logic (STL) specifications. STL is a rich logic that can be used to describe tasks involving bounds on physical parameters, continuous time bounds, and logical relationships over time and states. STL is equipped with a continuous measure called the robustness degree that measures how strongly a given sample path exhibits an STL property [4, 3]. This measure enables the use of continuous optimization problems to solve learning [7, 6] or formal synthesis problems [9] involving STL.