title: Flying-Capacitor Bottom-Plate Sampling Scheme for Low-Power High-Resolution SAR ADCs

abstract: A new sampling scheme for successive approximation register (SAR) analog-to-digital converters (ADCs) is proposed in this paper. The switching scheme eliminates two major problems of traditional SAR ADC architectures: first, the high energy demand of the input driver, because of high input capacitance; and second, the trade-off between linearity and, consequently, bottom-plate sampling, and area savings and, consequently, upper-plate sampling. The proposed sampling scheme allows reduction of the sampling capacitance to a single unit capacitor and the use of high linear bottom-plate sampling without sacrificing the double area on digital-to-analog converter (DAC). This method works with most previously published switching schemes.


title: SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution

abstract: Single image super resolution (SISR) is to reconstruct a high resolution image from a single low resolution image. The SISR task has been a very attractive research topic over the last two decades. In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the breakthroughs achieved by using CNN models, there are still some problems remaining unsolved, such as how to recover high frequency details of high resolution images. Previous CNN based models always use a pixel wise loss, such as l2 loss. Although the high resolution images constructed by these models have high peak signal-to-noise ratio (PSNR), they often tend to be blurry and lack high-frequency details, especially at a large scaling factor. In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks. In the framework, we propose a robust perceptual loss based on the discriminator of the built SRPGAN model. We use the Charbonnier loss function to build the content loss and combine it with the proposed perceptual loss and the adversarial loss. Compared with other state-of-the-art methods, our method has demonstrated great ability to construct images with sharp edges and rich details. We also evaluate our method on different benchmarks and compare it with previous CNN based methods. The results show that our method can achieve much higher structural similarity index (SSIM) scores on most of the benchmarks than the previous state-of-art methods.


title: Towards an Optimized BlockChain for IoT

abstract: There has been increasing interest in adopting BlockChain (BC), that underpins the crypto-currency Bitcoin, in Internet of Things (IoT) for security and privacy. However, BCs are computationally expensive and involve high bandwidth overhead and delays, which are not suitable for most IoT devices. This paper proposes a lightweight BC-based architecture for IoT that virtually eliminates the overheads of classic BC, while maintaining most of its security and privacy benefits. IoT devices benefit from a private immutable ledger, that acts similar to BC but is managed centrally, to optimize energy consumption. High resource devices create an overlay network to implement a publicly accessible distributed BC that ensures end-to-end security and privacy. The proposed architecture uses distributed trust to reduce the block validation processing time. We explore our approach in a smart home setting as a representative case study for broader IoT applications. Qualitative evaluation of the architecture under common threat models highlights its effectiveness in providing security and privacy for IoT applications. Simulations demonstrate that our method decreases packet and processing overhead significantly compared to the BC implementation used in Bitcoin.


title: Can You See Me Now?: How Field of View Affects Collaboration in Robotic Telepresence

abstract: Robotic telepresence systems-videoconferencing systems that allow a remote user to drive around in another location-are an emerging technology for supporting geographically-distributed teams. Thus far, many of these systems rely on affordances designed for stationary systems, such as a single, narrow-view camera to provide vision for the remote user. Teleoperation has offered some solutions to this via an augmented field-of-view, but how these solutions support task outcomes in collaborative mobile telepresence tasks has yet to be understood. To investigate this, we conducted a three condition (field-of-view: narrow (45°) vs. wide-angle (180°) vs. panoramic (360°)) between-participants controlled laboratory experiment. We asked participants (N=24) to collaborate with a confederate via a robotic telepresence system while using one of these views in a redecoration task. Our results showed that wider views supported task efficiency and fewer collisions, but were perceived as more difficult to use.


title: Now you see me, now you don't: protecting smartphone authentication from shoulder surfers

abstract: In this paper, we present XSide, an authentication mechanism that uses the front and the back of smartphones to enter stroke-based passwords. Users can switch sides during input to minimize the risk of shoulder surfing. We performed a user study (n = 32) to explore how switching sides during authentication affects usability and security of the system. The results indicate that switching the sides increases security while authentication speed stays relatively fast (≤ 4 seconds). The paper furthermore provides insights on accuracy of eyes-free input (as used in XSide) and shows how 3D printed prototype cases can improve the back-of-device interaction experience.


title: How online trust influence B2C e-Commerce adoption? An empirical study among Asian online shoppers

abstract: Though many previous studies has proved the importance of trust from various perspectives, the researches about online consumer’s trust are fragmented in nature and still it need more attention from academics. Lack of consumers trust in online systems is a critical impediment to the success of e-Commerce. Therefore it is important to explore the critical factors that affect the formation of user’s trust in online environments. The main objective of this paper is to analyze the effects of various antecedents of online trust and to predict the user’s intention to engage in online transaction based on their trust in the Information systems. This study is conducted among Asian online consumers and later the results were compared with those from Non-Asian regions. Another objective of this paper is to integrate De Lone and McLean model of IS Success and Technology Acceptance Model (TAM) for measuring the significance of online trust in e-Commerce adoption. The results of this study show that perceived security, perceived privacy, vendor familiarity, system quality and service quality are the significant antecedents of online trust in a B2C e-Commerce context.


title: A Unified Control and Power Management Scheme for PV-Battery-Based Hybrid Microgrids for Both Grid-Connected and Islanded Modes

abstract: Battery storage is usually employed in photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both ac and dc buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.


title: Market Design for HCI: Successes and Failures of Peer-to-Peer Exchange Platforms

abstract: This paper explores an HCI approach to designing markets, with a primary focus on peer-to peer exchange platforms. We draw on recent work in economics that has documented how markets function, how they can be evaluated, and what can be done to fix them when they fail. We introduce five key concepts from market design: thickness, congestion, stability, safety, and repugnance. These lend HCI an analytic vocabulary for understanding why markets may succeed or struggle. Building on prior empirical work, we apply these concepts to compare two well-known network hospitality platforms, Couchsurfing and Airbnb. As a second illustrative case, we use market design to shed light on the challenges experienced by smaller-scale peer-to-peer marketplaces for lending, renting, and selling physical goods. To conclude, we discuss how this kind of analysis can make conceptual, evaluative, and generative contributions to the study and design of exchange platforms and other socio-technical systems.


title: Emotion regulation in interpersonal problems: The role of cognitive-emotional complexity, emotion regulation goals, and expressivity.

abstract: Young, middle-aged, and older adults' emotion regulation strategies in interpersonal problems were examined. Participants imagined themselves in anger- or sadness-eliciting situations with a close friend. Factor analyses of a new questionnaire supported a 4-factor model of emotion regulation strategies, including passivity, expressing emotions, seeking emotional information or support, and solving the problem. Results suggest that age differences in emotion regulation (such as older adults' increased endorsement of passive emotion regulation relative to young adults) are partially due to older adults' decreased ability to integrate emotion and cognition, increased prioritization of emotion regulation goals, and decreased tendency to express anger.


title: Rule-Based Facial Makeup Recommendation System

abstract: Facial makeup style plays a key role in the facial appearance making it more beautiful and attractive. Choosing the best makeup style for a certain face to fit a certain occasion is a full art. Also, foretelling how the face will look like after applying the proposed makeup style requires a high imagination. To solve this problem computationally, an automatic and smart facial makeup recommendation and synthesis system is proposed in this paper. This system starts by classifying the makeup related facial traits that makeup artists consider to decide the makeup style; Then, a rule-based makeup recommendation system is built by creating a knowledge base that models the relation between the facial style attributes and makeup style attributes, taking into account the occasion such as daily makeup or heavy makeup and the desired trend with semantic text explaining logic behind the recommended style. Finally, an automatic facial makeup synthesis system is developed to apply the recommended style on the facial image as well. To this end, a new dataset with 961 different females photos collected and labeled. To evaluate the performance of the proposed system, an extensive experimental analysis is conducted on the automatic facial attributes classification, the recommendation efficiency and the synthesis accuracy under different conditions. The obtained results show the effectiveness and flexibility of the proposed fully automatic framework.


title: Deep Reinforcement Learning for Resource Allocation in V2V Communications

abstract: In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small. From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications.


Diffused Intimacy: Trust and Self-Disclosure in Online Relationship


title: Parsing Universal Dependencies without training

abstract: We propose UDP, the first training-free parser for Universal Dependencies (UD). Our algorithm is based on PageRank and a small set of head attachment rules. It features two-step decoding to guarantee that function words are attached as leaf nodes. The parser requires no training, and it is competitive with a delexicalized transfer system. UDP offers a linguistically sound unsupervised alternative to cross-lingual parsing for UD, which can be used as a baseline for such systems. The parser has very few parameters and is distinctly robust to domain change across languages.


title: A survey of current integrative network algorithms

abstract: The goal of systems biology is to gain a more complete understanding of biological systems by viewing all of their components and the interactions between them simultaneously. Until recently, the most complete global view of a biological system was through the use of gene expression or protein-protein interaction data. With the increasing number of high-throughput technologies for measuring genomic, proteomic, and metabolomic data, scientists now have the opportunity to create complex network-based models for drug discovery, protein function annotation, and many other problems. Each technology used to measure a biological system inherently presents a limited view of the system. However, the combination of multiple technologies can provide a more complete picture. Much recent work has studied integrating these heterogeneous data types into single networks. Here we provide a survey of integrative network-based approaches to problems in systems biology. We focus on describing the variety of algorithms used in integrative network inference. Ultimately, the survey of current approaches leads us to the conclusion that there is an urgent need for a standard set of evaluation metrics and data sets in this field.


title: Simulation Analysis of Control System in an Innovative Magnetically-Saturated Controllable Reactor

abstract: Controllable saturation reactors are widely used in reactive power compensation. The control system of controllable saturation reactor determines adaption speed, accuracy, and stability. First, an innovative type of controllable saturation reactor is introduced. After that the control system is designed, and a self-tuning algorithm in PID controller is proposed in the paper. The algorithm tunes PID parameters automatically with different error signals caused by varied loads in power system. Then the feasibility of the above algorithm is verified by Simulink module of Matlab software. The results of simulation indicate that the control system can efficiently reduce adaption time and overshoot.


title: Quantum Optimal Transport for Tensor Field Processing

abstract: This article introduces a new notion of optimal transport (OT) between tensor fields, which are measures whose values are positive semidefinite (PSD) matrices. This "quantum" formulation of OT (Q-OT) corresponds to a relaxed version of the classical Kantorovich transport problem, where the fidelity between the input PSD-valued measures is captured using the geometry of the Von-Neumann quantum entropy. We propose a quantum-entropic regularization of the resulting convex optimization problem, which can be solved efficiently using an iterative scaling algorithm. This method is a generalization of the celebrated Sinkhorn algorithm to the quantum setting of PSD matrices. We extend this formulation and the quantum Sinkhorn algorithm to compute barycenters within a collection of input tensor fields. We illustrate the usefulness of the proposed approach on applications to procedural noise generation, anisotropic meshing, diffusion tensor imaging and spectral texture synthesis.


title: PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data

abstract: When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.


title: Organoids in cancer research

abstract: The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.


title: Magnetic needle guidance for neurosurgery: Initial design and proof of concept

abstract: A magnetic-tip steerable needle is presented with application to aiding deep brain stimulation electrode placement. The magnetic needle is 1.3mm in diameter at the tip with a 0.7mm diameter shaft, which is selected to match the size of a deep brain stimulation electrode. The tip orientation is controlled by applying torques to the embedded neodymium-iron-boron permanent magnets with a clinically-sized magnetic-manipulation system. The prototype design is capable of following trajectories under human-in-the-loop control with minimum bend radii of 100mm without inducing tissue damage and down to 30mm if some tissue damage is tolerable. The device can be retracted and redirected to reach multiple targets with a single insertion point.


title: Enterprise Cognitive Computing Applications: Opportunities and Challenges

abstract: Enterprise cognitive computing (ECC) applications are generating a great deal of excitement for organizations. However, they have yet to demonstrate a business impact on a large scale. An important reason for this is a failure to understand how such applications can contribute to a company’s business objectives and the challenges associated with implementing them. In this article, the authors provide an overview of cognitive computing applications for the enterprise. In particular, they classify opportunities for developing ECC applications and describe challenges in implementing them. Their findings are based on a study of 51 ECC application initiatives across a broad range of industries in North America, Europe, and the Asia-Pacific region. Given the lack of systematic descriptions regarding what is possible from ECC, this article should be valuable to researchers and practitioners in unpacking the black box of cognitive computing.


title: High-performance evolved packet core signaling and bearer processing on general-purpose processors

abstract: Telecommunications service providers are exploring the use of standard high-volume servers to reduce total cost of ownership while at the same time increasing flexibility, service velocity, and scalability of network functions. This article characterizes performance of general-purpose processors — specifically x86 architecture processors — for signaling and bearer processing representative of a wireless carrier’s call model for a Long Term Evolution Evolved Packet Core. A radio access network emulator was used to stimulate an Evolved Packet Core software stack running on an x86 server. The goal was to prove that standard high-volume servers can execute EPC functions per representative market call models, and that workloads can scale across bearer and control plane at line rate without acceleration technologies. A call model was developed to quantify the performance on Intel® Xeon® Processor based servers using an LTE traffic simulator and a commercial EPC software stack. The traffic models represent bidirectional real-world network traffic during different times of the day. The results were that the test EPC processed control and user plane traffic with 50,000 subscribers with a total payload of 10 Gb/s downlink + 4.8 Gb/s uplink traffic using five cores for the data plane and eight cores for the rest of the system; and the user plane throughput scaled in a single blade environment to 20 Gb/s per socket or 40 Gb/s for a dual socket blade.


title: Long-term wind speed and power forecasting using local recurrent neural network models

abstract: This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to 30 km away from the wind turbine cluster. Three types of local recurrent neural networks are employed as forecasting models, namely, the infinite impulse response multilayer perceptron (IIR-MLP), the local activation feedback multilayer network (LAF-MLN), and the diagonal recurrent neural network (RNN). These networks contain internal feedback paths, with the neuron connections implemented by means of IIR synaptic filters. Two novel and optimal on-line learning schemes are suggested for the update of the recurrent network's weights based on the recursive prediction error algorithm. The methods assure continuous stability of the network during the learning phase and exhibit improved performance compared to the conventional dynamic back propagation. Extensive experimentation is carried out where the three recurrent networks are additionally compared to two static models, a finite-impulse response NN (FIR-NN) and a conventional static-MLP network. Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.


title: Towards a Serious Game to Help Students Learn Computer Programming

abstract: Video games are part of our culture like TV, movies, and books. We believe that this kind of software can be used to increase students' interest in computer science. Video games with other goals than entertainment, serious games, are present, today, in several fields such as education, government, health, defence, industry, civil security, and science. This paper presents a study around a serious game dedicated to strengthening programming skills. Real-Time Strategy, which is a popular game genre, seems to be the most suitable kind of game to support such a serious game. From programming teaching features to video game characteristics, we define a teaching organisation to experiment if a serious game can be adapted to learn programming.


title: An immersive 3D video-conferencing system using shared virtual team user environments

abstract: Videoconferencing is going to become attractive for geo-graphically distributed team collaboration, specifically to avoid travelling and to increase flexibility. Against this background this paper presents a next generation system - a 3D videoconference providing immersive tele-presence and natural representation of all participants in a shared virtual meeting space to enhance quality of human-centred communication. This system is based on the principle of a shared virtual table environment, which guarantees correct eye contact and gesture reproduction. The key features of our system are presented and compared to other approaches like tele-cubicles. Furthermore the current system design and details of the real-time hardware and software concept are explained.


title: Multiscale texture synthesis

abstract: Example-based texture synthesis algorithms have gained widespread popularity for their ability to take a single input image and create a perceptually similar non-periodic texture. However, previous methods rely on single input exemplars that can capture only a limited band of spatial scales. For example, synthesizing a continent-like appearance at a variety of zoom levels would require an impractically high input resolution. In this paper, we develop a multiscale texture synthesis algorithm. We propose a novel example-based representation, which we call an exemplar graph, that simply requires a few low-resolution input exemplars at different scales. Moreover, by allowing loops in the graph, we can create infinite zooms and infinitely detailed textures that are impossible with current example-based methods. We also introduce a technique that ameliorates inconsistencies in the user's input, and show that the application of this method yields improved interscale coherence and higher visual quality. We demonstrate optimizations for both CPU and GPU implementations of our method, and use them to produce animations with zooming and panning at multiple scales, as well as static gigapixel-sized images with features spanning many spatial scales.


title: Optimizing content-preserving projections for wide-angle images

abstract: Any projection of a 3D scene into a wide-angle image unavoidably results in distortion. Current projection methods either bend straight lines in the scene, or locally distort the shapes of scene objects. We present a method that minimizes this distortion by adapting the projection to content in the scene, such as salient scene regions and lines, in order to preserve their shape. Our optimization technique computes a spatially-varying projection that respects user-specified constraints while minimizing a set of energy terms that measure wide-angle image distortion. We demonstrate the effectiveness of our approach by showing results on a variety of wide-angle photographs, as well as comparisons to standard projections.


title: Secure Smart Homes: Opportunities and Challenges

abstract: The Smart Home concept integrates smart applications in the daily human life. In recent years, Smart Homes have increased security and management challenges due to the low capacity of small sensors, multiple connectivity to the Internet for efficient applications (use of big data and cloud computing), and heterogeneity of home systems, which require inexpert users to configure devices and micro-systems. This article presents current security and management approaches in Smart Homes and shows the good practices imposed on the market for developing secure systems in houses. At last, we propose future solutions for efficiently and securely managing the Smart Homes.


title: Suicide by more than 90 stab wounds including perforation of the skull

abstract: Abstract A man committed suicide in his bathroom using a small pocket knife. At the autopsy a total of 92 stab wounds on the forehead, in both temples, the anterior aspect of the neck, the back of the neck, the chest and the sides of the trunk were found. In addition, repeated stabbing had caused a large soft tissue defect on the forehead. The frontal bone showed 3 perforations but no brain injury was present and two ribs were severed in the bony part, one of which carried a star-like pattern from repeated stabbing. No major vessels were injured and the cause of death was exsanguination after a considerable survival time. The large number of stab wounds, the perforation of bone and some injury sites, especially the head and back of the neck, are extraordinary findings in suicides which were probably favoured by insufficient anatomical knowledge and the use of a short-bladed knife. A psychiatric history could not be verified.


title: Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample

abstract: A mailed questionnaire was used to obtain comparative risk judgments for 32 different hazards from a random sample of 296 individuals living in central New Jersey. The results demonstrate that an optimistic bias about susceptibility to harm-a tendency to claim that one is less at risk than one's peers—is not limited to any particular age, sex, educational, or occupational group. It was found that an optimistic bias is often introduced when people extrapolate from their past experience to estimate their future vulnerability. Thus, the hazards most likely to elicit unrealistic optimism are those associated with the belief (often incorrect) that if the problem has not yet appeared, it is unlikely to occur in the future. Optimistic biases also increase with the perceived preventability of a hazard and decrease with perceived frequency and personal experience. Other data presented illustrate the inconsistent relationships between personal risk judgments and objective risk factors.


title: Deep learning based parts of speech tagger for Bengali

abstract: This paper describes the Part of Speech (POS) tagger for Bengali Language. Here, POS tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing (NLP) applications, POS tagging is considered as the one of the basic necessary tools. Identifying the ambiguities in language lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. Different methods of automating the process have been developed and employed for Bengali. In this paper, we report about our work on building POS tagger for Bengali using the Deep Learning. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. It is observed from the experiments based on Linguistic Data Consortium (LDC) catalog number LDC2010T16 and ISBN 1-58563-561-8 corpus that 93.33% accuracy is obtained for Bengali POS tagger using the Deep Learning.


Comparison of Patient Controlled Intravenous Analgesia and Patient Controlled Regional Analgesia through Femoral Catheter after Anterior Cruciate Ligamentum Repair


title: A low-offset high-speed double-tail dual-rail dynamic latched comparator

abstract: This paper presents a new dynamic latched comparator which shows lower input-referred latch offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage was improved and the complementary version of the output-latch stage, which has bigger output drive current capability at the same area, was implemented. As a result, the circuit shows up to 25% less input-referred latch offset voltage and 44% less sensitivity of the delay versus the input voltage difference (delay/log(ΔVin)), which is about 17.2ps/decade, than the conventional double-tail latched comparator at approximately the same area and power consumption.


title: Soft and Declarative Fishing of Information in Big Data Lake

abstract: In recent years, many fields that experience a sudden proliferation of data, which increases the volume of data that must be processed and the variety of formats the data is stored in have been identified. This causes pressure on existing compute infrastructures and data analysis methods, as more and more data are considered as a useful source of information for making critical decisions in particular fields. Among these fields exist several areas related to human life, e.g., various branches of medicine, where the uncertainty of data complicates the data analysis, and where the inclusion of fuzzy expert knowledge in data processing brings many advantages. In this paper, we show how fuzzy techniques can be incorporated in big data analytics carried out with the declarative U-SQL language over a big data lake located on the cloud. We define the concept of big data lake together with the Extract, Process, and Store process performed while schematizing and processing data from the Data Lake, and while storing results of the processing. Our solution, developed as a Fuzzy Search Library for Data Lake, introduces the possibility of massively parallel, declarative querying of big data lake with simple and complex fuzzy search criteria, using fuzzy linguistic terms in various data transformations, and fuzzy grouping. Presented ideas are exemplified by a distributed analysis of large volumes of biomedical data on Microsoft Azure cloud. Results of performed tests confirm that the presented solution is highly scalable on the Cloud and is a successful step toward soft and declarative processing of data on a large scale. The solution presented in this paper directly addresses three characteristics of big data, i.e., volume , variety , and velocity , and indirectly addresses, veracity and value .


A minimum storage algorithm for computing the median


title: Efficient Implementation of AES Algorithm in FPGA Device

abstract: This paper presents an efficient FPGA implementation approach of the Advanced Encryption Standard (AES) Algorithm. The architectural optimization has been incorporated which includes pipelining techniques. Speed is increased by processing multiple rounds simultaneously but at the cost of increased area. Algorithmic optimization techniques have also been used which includes exclusion of shift row stage and on the fly round key generation. The corresponding hardware realization is optimal in terms of area and offers high data throughout. An optimized code for the implementation of Rijndael algorithm for 128 bits has been developed and experimentally tested using Xilinx Virtex XC2VP70-7 device. A 119.954 MHz clock frequency is achieved which translates to a throughput of 1.18 Gbps using 6279 Slices and 5 BRAMs. The design handles both encryption and decryption and fits into a single FPGA.


title: Education data mining: Mining MOOCs videos using metadata based approach

abstract: Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.


title: Automatic Music Mood Classification of Hindi Songs

abstract: The popularity of internet, downloading and purchasing music from online music shops are growing dramatically. As an intimate relationship presents between music and human emotions, we often choose to listen a song that suits our mood at that instant. Thus, the automatic methods are needed to classify music by moods even from the uploaded music files in social networks. However, several studies on Music Information Retrieval (MIR) have been carried out in recent decades. In the present task, we have built a system for classifying moods of Hindi songs using different audio related features like rhythm, timber and intensity. Our dataset is composed of 230 Hindi music clips of 30 seconds that consist of five mood clusters. We have achieved an average accuracy of 51.56% for music mood classification on the above data.


title: AR-Drone as a Platform for Robotic Research and Education

abstract: This paper presents the AR-Drone quadrotor helicopter as a robotic platform usable for research and education. Apart from the description of hardware and software, we discuss several issues regarding drone equipment, abilities and performance. We show, how to perform basic tasks of position stabilization, object following and autonomous navigation. Moreover, we demonstrate the drone ability to act as an external navigation system for a formation of mobile robots. To further demonstrate the drone utility for robotic research, we describe experiments in which the drone has been used. We also introduce a freely available software package, which allows researches and students to quickly overcome the initial problems and focus on more advanced issues.


title: Bayesian Optimization in High Dimensions via Random Embeddings

abstract: Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple and applies to domains with both categorical and continuous variables. The experiments demonstrate that REMBO can effectively solve high-dimensional problems, including automatic parameter configuration of a popular mixed integer linear programming solver.


title: Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS

abstract: It is generally accepted that cognitive deficits in schizophrenia are related to functional outcome. However, support for longitudinal relationships between cognition and functional outcome has not been as well documented. The current paper presents a review of 18 recently published longitudinal studies (minimum 6-month follow up) of the relationships between cognition and community outcome in schizophrenia. Results from these studies reveal considerable support for longitudinal associations between cognition and community outcome in schizophrenia. These studies demonstrate that cognitive assessment predict later functional outcome and provide a rationale for psychopharmacological interventions for cognitive deficits in schizophrenia. Although the relationships between cognition and community outcome are well-supported, it is clear that community functioning is also affected by a host of factors apart from cognition that are usually not considered in clinical trial studies (e.g., psychosocial rehabilitation and educational/vocational opportunities). In the second part of the paper, we consider intervening steps between cognitive performance measures and community outcome. These steps are apt to have important implications for clinical trials of cognition-enhancing agents in schizophrenia.


title: Recovery of underwater visibility and structure by polarization analysis

abstract: Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.


title: Anomaly Detection Techniques for Database Protection Against Insider Threats (Invited Paper)

abstract: In this paper, we propose techniques for detecting anomalies in user accesses by learning profiles of normal access patterns of users based on both the syntactic and semantic features of past users queries stored in database logs. New accesses are checked upon these profiles and deviations are considered anomalous accesses which may be indications of potential insider attacks. We consider two scenarios. The first scenario is when the monitored database employs role-based access control (RBAC). In this scenario, we build profiles of roles rather than individual users, this makes our approach usable for databases which have a large user population. In this case, we use the naive Bayesian classification to detect anomalies. We also employ multilabeling classification to account for the case when roles have common access patterns. The second scenario is when RBAC is not used. In this case, we detect anomalies using the COBWEB clustering algorithm. We provide extensive evaluation for our techniques. Results show that our techniques are effective.


title: Underwater 3D reconstruction based on laser line scanning

abstract: This paper proposes an underwater 3D reconstruction method based on laser line scanning. 3D reconstruction of underwater target is an important technology in ocean development. Due to the complicated underwater environment, underwater 3D reconstruction is still a challenge. The traditional underwater object detection is mainly based on acoustic technology, but acoustic technology can't meet the demands of high-precision underwater reconstruction. Laser line scanning is a popular technology which can be applied into complex underwater environments. It has stable antijamming capability and high-precision scanning capability which can help us to finish 3D reconstruction delicately. In our method, We combine some advanced methods to finish this reconstruction. There are two important links. The first one is calibration method called direct calibration. This method can get the relationship between 2D pix coordinate and 3D world coordinate directly, so we don't need to calculate complex camera parameters. The second one is extraction of line laser. Getting clear images and processing them well is beneficial to extract the center line of the laser, so we use smoothing to reduce the interferences first, then direction template technology combined with gray centroid method is used to get the information of center line. In order to verify the correctness of this methods, experiment was done on the land firstly and got a good result. Then the experiment was done in a sink. We use a disk and a keyboard as the target. With our adjustment, we have got a clear shape of the underwater object. At the same time, structure from motion is used as a compared method.


Origin of the bentonite and barite deposits on Milos Island (Central Aegean, Greece)


A Study on Skin Optics


title: Seasonality of reproduction in sheep

abstract: Abstract Unlike most domestic livestock species, sheep are widely known as an animal with marked seasonality of breeding activity. The annual cycle of daily photoperiod has been identified as the determinant factor of this phenomenon, while environmental temperature, nutritional status, social interactions, lambing date and lactation period are considered to modulate it. The aim of this paper is to review the current state of knowledge of the reproductive seasonality in sheep. Following general considerations concerning the importance of seasonal breeding as a reproductive strategy for the survival of species, the paper describes the manifestations of seasonality in both the ram and the ewe. Both determinant and modulating factors are developed and special emphasis is given to the neuroendocrine base of photoperiodic regulation of seasonal breeding. Other aspects such as the role of melatonin, the involvement of thyroid hormones and the concept of photorefractoriness are also reviewed.


title: Supervised Fine Tuning for Word Embedding with Integrated Knowledge

abstract: Learning vector representation for words is an important research field which may benefit many natural language processing tasks. Two limitations exist in nearly all available models, which are the bias caused by the context definition and the lack of knowledge utilization. They are difficult to tackle because these algorithms are essentially unsupervised learning approaches. Inspired by deep learning, the authors propose a supervised framework for learning vector representation of words to provide additional supervised fine tuning after unsupervised learning. The framework is knowledge rich approacher and compatible with any numerical vectors word representation. The authors perform both intrinsic evaluation like attributional and relational similarity prediction and extrinsic evaluations like the sentence completion and sentiment analysis. Experiments results on 6 embeddings and 4 tasks with 10 datasets show that the proposed fine tuning framework may significantly improve the quality of the vector representation of words.


A subdivision scheme for hexahedral meshes


title: Friend transfer: Cold-start friend recommendation with cross-platform transfer learning of social knowledge

abstract: The emergence of various and disparate social media platforms has opened opportunities for the research on cross-platform media analysis. This provides huge potentials to solve many challenging problems which cannot be well explored in one single platform. In this paper, we investigate into cross-platform social relation and behavior information to address the cold-start friend recommendation problem. In particular, we conduct an in-depth data analysis to examine what information can better transfer from one platform to another and the result demonstrates a strong correlation for the bidirectional relation and common contact behavior between our test platforms. Inspired by the observations, we design a random walk-based method to employ and integrate these convinced social information to boost friend recommendation performance. To validate the effectiveness of our cross-platform social transfer learning, we have collected a cross-platform dataset including 3,000 users with recognized accounts in both Flickr and Twitter. We demonstrate the effectiveness of the proposed friend transfer methods by promising results.


title: Estimation of NAND Flash Memory Threshold Voltage Distribution for Optimum Soft-Decision Error Correction

abstract: As the feature size of NAND flash memory decreases, the threshold voltage signal becomes less reliable, and its distribution varies significantly with the number of program-erase (PE) cycles and the data retention time. We have developed parameter estimation algorithms to find the means and variances of the threshold voltage distribution that is modeled as a Gaussian mixture. The proposed methods find the best-fit parameters by minimizing the squared Euclidean distance between the measured threshold voltage values and those obtained from the Gaussian mixture model. For the parameter estimation, the gradient descent (GD) and the Levenberg-Marquardt (LM) based methods are employed. The developed algorithms are applied to both simulated and real NAND flash memory. It is also demonstrated that error correction with the estimated mean and variance values yields much better performance when compared to the method that only updates the mean.


title: AlViz - A Tool for Visual Ontology Alignment

abstract: We introduce a multiple-view tool called AlViz, which supports the alignment of ontologies visually. Ontologies play an important role for interoperability between organizations and for the semantic Web because they aim at capturing domain knowledge in a generic way and provide a consensual understanding of a domain. Alignment is the process where for each entity in first ontology we try to find a corresponding entity in the second ontology with the same or the closest meaning. Existing ontology alignment tools do not adequately provide a way for users to analyze the results. While many alignment tools generate lists of mappings it is difficult to analyze these alignments without examining every pairwise correspondence in the output files and even then it is an overwhelming task. We propose the use of visualization techniques to facilitate user understanding of the ontology alignment results. AlViz is implemented as a tab plug-in for Protege


title: Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

abstract: It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.


title: Analyzing Compositionality-Sensitivity of NLI Models

abstract: Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.


title: Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

abstract: Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.


title: Single Image Super-resolution With Detail Enhancement Based on Local Fractal Analysis of Gradient

abstract: In this paper, we propose a single image super-resolution and enhancement algorithm using local fractal analysis. If we treat the pixels of a natural image as a fractal set, the image gradient can then be regarded as a measure of the fractal set. According to the scale invariance (a special case of bi-Lipschitz invariance) feature of fractal dimension, we will be able to estimate the gradient of a high-resolution image from that of a low-resolution one. Moreover, the high-resolution image can be further enhanced by preserving the local fractal length of gradient during the up-sampling process. We show that a regularization term based on the scale invariance of fractal dimension and length can be effective in recovering details of the high-resolution image. Analysis is provided on the relation and difference among the proposed approach and some other state of the art interpolation methods. Experimental results show that the proposed method has superior super-resolution and enhancement results as compared to other competitors.


title: Cautious caregivers: gender stereotypes and the sexualization of men nurses' touch

abstract: Aim. The aim of this research was to explore the experience of men nurses and the ways in which gender relations structure different work experiences for women and men in the same profession. ::: ::: Background. Men are now entering the nursing profession in record numbers and challenging the notion that men are inappropriate in caregiver roles or incapable of providing compassionate and sensitive care. A limitation of the current state of knowledge regarding caring and men nurses is that it is primarily focused on men nursing students, not practising nurses. Little is known about men nurses' practices of caring and how such practices reflect the gendered nature of nursing and nurses' caring work. ::: ::: Methods. The theme of men nurses as cautious caregivers emerged from data that were collected in two rounds of semi-structured interviews with eight men nurses practising in Nova Scotia, Canada. Thematic analysis, informed by feminist theory and masculinity theory, was used as the method for analysing the data. ::: ::: Findings. For men nurses, the stereotype of men as sexual aggressors is compounded by the stereotype that men nurses are gay. These stereotypes sexualize men nurses' touch and create complex and contradictory situations of acceptance, rejection and suspicion of men as nurturers and caregivers. They also situate men nurses in highly stigmatized roles in which they are subject to accusations of inappropriate behaviour. For men nurses, this situation is lived as a heightened sense of vulnerability and the continual need to be cautious while touching and caring for patients. Ultimately, this situation impacts on the ability of men nurses to do the caring work they came into nursing to do.


title: Why do some patients keep hurting their back? Evidence of ongoing back muscle dysfunction during remission from recurrent back pain

abstract: Approximately thirty-four percent of people who experience acute low back pain (LBP) will have recurrent episodes. It remains unclear why some people experience recurrences and others do not, but one possible cause is a loss of normal control of the back muscles. We investigated whether the control of the short and long fibres of the deep back muscles was different in people with recurrent unilateral LBP from healthy participants. Recurrent unilateral LBP patients, who were symptom free during testing, and a group of healthy volunteers, participated. Intramuscular and surface electrodes recorded the electromyographic activity (EMG) of the short and long fibres of the lumbar multifidus and the shoulder muscle, deltoid, during a postural perturbation associated with a rapid arm movement. EMG onsets of the short and long fibres, relative to that of deltoid, were compared between groups, muscles, and sides. In association with a postural perturbation, short fibre EMG onset occurred later in participants with recurrent unilateral LBP than in healthy participants (p=0.022). The short fibres were active earlier than long fibres on both sides in the healthy participants (p<0.001) and on the non-painful side in the LBP group (p=0.045), but not on the previously painful side in the LBP group. Activity of deep back muscles is different in people with a recurrent unilateral LBP, despite the resolution of symptoms. Because deep back muscle activity is critical for normal spinal control, the current results provide the first evidence of a candidate mechanism for recurrent episodes.


title: How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks

abstract: Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). ::: In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non--linearly encoded in the embedding space, which questions the cosine-based, unsupervised, evaluation methods. All results and analysis scripts are available online.


title: Safety, liveness and fairness in temporal logic

abstract: In this paper we present syntactic characterization of temporal formulas that express various properties of interest in the verification of concurrent programs. Such a characterization helps us in choosing the right techniques for proving correctness with respect to these properties. The properties that we consider include safety properties, liveness properties and fairness properties. We also present algorithms for checking if a given temporal formula expresses any of these properties.


A Speeded up Robust Scale-Invariant Feature Transform Currency Recognition Algorithm


title: Neural Code Completion

abstract: Code completion, an essential part of modern software development, yet can bechallenging for dynamically typed programming languages. In this paper we ex-plore the use of neural network techniques to automatically learn code completionfrom a large corpus of dynamically typed JavaScript code. We show differentneural networks that leverage not only token level information but also structuralinformation, and evaluate their performance on different prediction tasks. Wedemonstrate that our models can outperform the state-of-the-art approach, whichis based on decision tree techniques, on both next non-terminal and next terminalprediction tasks by 3.8 points and 0.5 points respectively. We believe that neuralnetwork techniques can play a transformative role in helping software developersmanage the growing complexity of software systems, and we see this work as afirst step in that direction.


title: Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

abstract: In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.


Amplification Hell: Revisiting Network Protocols for DDoS Abuse


title: Erythromelanosis follicularis faciei et colli: Relationship with keratosis pilaris

abstract: Erythromelanosis follicularis faciei et colli (EFF) is an unusual condition characterized by the triad of hyperpigmentation, follicular plugging and erythema of face and neck. This is less common in women and familial case reports are few. We report EFF in three siblings in an Indian family, two of whom are females. The possibility of this condition being genetically related to keratosis pilaris as well as being a variant of keratosis rubra pilaris is also discussed.


title: Intergenerational Mapping and the Cultural Politics of Memory

abstract: A key objective of the paper is to rejuvenate our understanding of time. The focus is on how the knowledge and memories of times past are transmitted collectively and intergenerationally, and why this is important for children's political formation. The objective is pursued through a short philosophical discussion of time, memory and forgetting, drawing primarily on the work of Bernard Stiegler and others investigating these questions with an emphasis on contemporary technologies. The study and philosophical framing also engage a larger geo-economic and historical context in which all subjects, including children, are faced with critical challenges related to the recent development of new technologies of control; these formulate drives and/or a temporal consciousness of 'hyper' attention that renders deep attention and the formation of long-term desire increasingly difficult. In the face of this historical moment, the paper calls attention to the importance of thinking about time holistically as well as h...


OFDM Transmission Simulation using GNU Octave


title: Solving optimization problems using black hole algorithm

abstract: Various meta-heuristic optimization approaches have been recently created and applied in different areas. Many of these approaches are inspired by swarm behaviors in the nature. This paper studies the solving optimization problems using Black Hole Algorithm (BHA) which is a population-based algorithm. Since the performance of this algorithm was not tested in mathematical functions, we have studied this issue using some standard functions. The results of the BHA are compared with the results of GA and PSO algorithms which indicate that the performance of BHA is better than the other two mentioned algorithms.


title: Image Super-Resolution Via Sparse Representation

abstract: This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework.


A dynamic priority reservation queue scheme for handover in mobile cellular systems supporting multi-rate traffics


title: A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection

abstract: Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis-driven evaluation of feature space evasion attacks on ML-based PDF malware detectors. Specific to our case study, we find that a) feature space evasion models are in general not adequate in representing real attacks, b) such models can be significantly improved by identifying conserved features (features that are invariant in real attacks) whenever these exist, and c) ML hardened using the improved feature space models remains robust to alternative attacks, in contrast to ML hardened using a very powerful class of problem space attacks, which does not.


title: Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery

abstract: Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic and precise road extraction. From an original image, it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. In this paper, we propose an approach for automatic road extraction based on a fully convolutional neural network of U-net family. This network consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net. Based on validation results, leaderboard and our own experience this network shows superior results for the DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network uses moderate memory that allows using just one GTX 1080 or 1080ti video cards to perform whole training and makes pretty fast predictions.


title: A daily diary investigation of the link between television watching and positive affect

abstract: Past research has shown a negative relationship between time spent watching television (TV) and several indicators of hedonic well-being—including positive affect (PA). However, cross-sectional designs employed in most of these studies do not allow for inferences regarding the direction of the link between TV watching and PA. Present research aimed to address this gap by using daily diary data from a large national sample of U.S. adults (N = 1668, age = 33–83 years). Respondents reported time spent watching TV as well as PA for eight consecutive days. Results of multilevel modeling analyses showed that duration of TV watching on the previous day did not significantly predict changes in PA on the next day. However, PA on the previous day significantly predicted decreases in duration of TV watching the following day. The results held after controlling for factors known to predict duration of TV watching and PA (i.e., age, gender, income level, employment status, marital status, health status, and personality traits). The present research goes beyond past cross-sectional work by shedding light on the direction of the link between TV watching and PA. Our findings indicate that watching TV does not seem to diminish PA—as critics of TV proclaim—but individuals seem to watch TV to a greater extent whenever they experience reduced PA.


Work engagement in nursing : an integrative review of the literature


title: A high speed and area efficient Booth recoded Wallace tree multiplier for fast arithmetic circuits

abstract: A Wallace tree multiplier using Booth Recoder is proposed in this paper. It is an improved version of tree based Wallace tree multiplier architecture. This paper aims at additional reduction of latency and area of the Wallace tree multiplier. This is accomplished by the use of Booth algorithm and compressor adders. The coding is done in Verilog HDL and synthesized for Xilinx Virtex 6 FPGA device. The result shows that the proposed architecture is around 67 percent faster than the existing Wallace-tree multiplier, 53 percent faster than the Vedic multiplier, 22 percent faster than the radix-8 Booth multiplier, 18 percent faster than the radix-16 Booth Multiplier. In terms of area also, the proposed multiplier is much efficient.


title: A Study on Immersion and VR Sickness in Walking Interaction for Immersive Virtual Reality Applications

abstract: This study analyzes walking interaction to enhance the immersion and minimize virtual reality (VR) sickness of users by conducting experiments. In this study, the walking interaction is composed of three steps using input devices with a simple structure that can be easily used by anyone. The first step consists of a gamepad control method, which is the most popular but has low presence. The second step consists of a hand-based walking control interface, which is mainly used for interaction in VR applications. The last step consists of a march-in-place detection simulator that interacts with the legs—the key body parts for walking. Four experiments were conducted to determine the degree of direct expression of intention by users in the course of walking interactions that can improve immersion, presence, and prevent VR sickness in VR applications. With regard to the experiments in this study, survey experiments were conducted for general users using the Wilcoxon test, a presence questionnaire, and simulator sickness questionnaire (SSQ). In addition, the technical performance of the VR scenes used in the experiment was analyzed. The experimental results showed that higher immersion was achieved when interactions that felt closer to real walking were provided in VR. Furthermore, it was found that even control methods with a simple structure could be used for walking interactions with minimal VR sickness. Finally, a satisfactory presence was found in VR if the user was able interact using his or her own legs.


title: Hierarchical Eigen Template Matching for Target Detection

abstract: The template matching is the basic technique of the object detection such as the defect inspection and OCR (Optical Character Recognition). It has a problem which requires a lot of computational time for detecting the XY displacement and the rotational angle of the target object. We have proposed the eigen template method for this problem. The proposed method can efficiently calculate correlation between images. However, when an input image is complicated, the method failed to detect the target object correctly. Additionally, the computational time of the method remains large still. In this paper, we propose the edge based eigen template method with robustness for the complex image compared with the previous method. Moreover, we describe an efficient matching algorithm for the eigen template method using the narrowing down process. In the evaluation experiments, we show that the proposed method can detect the target object faster and more robust to the complex image than the previous methods.


DESIGN AND SIMULATION OF MULTIBAND PLANAR INVERTED-F ANTENNA FOR MOBILE PHONE APPLICATIONS


title: Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

abstract: This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in realtime by learning from both human demonstrations and interventions. We implement two components of the Cycle-of Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.


title: Classification of EEG signals using neural network and logistic regression

abstract: Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.


title: Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance

abstract: The quality of word representations is frequently assessed using correlation with human judgements of word similarity. Here, we question whether such intrinsic evaluation can predict the merits of the representations for downstream tasks. We study the correlation between results on ten word similarity benchmarks and tagger performance on three standard sequence labeling tasks using a variety of word vectors induced from an unannotated corpus of 3.8 billion words, and demonstrate that most intrinsic evaluations are poor predictors of downstream performance. We argue that this issue can be traced in part to a failure to distinguish specific similarity from relatedness in intrinsic evaluation datasets. We make our evaluation tools openly available to facilitate further study.


title: Securing BYOD: A Study of Framing and Neutralization Effects on Mobile Device Security Policy Compliance

abstract: Because of the recent increase of mobile device adoption in the workplace, and the numerous security risks inherent in mobile device use, there is a growing need for organizations to craft mobile device security policies that will have a positive effect on employees’ intention to comply. Using prospect theory, developed by Kahneman and Tversky (1979), and the framing effects associated with this theory, as well as neutralization theory proposed by Sykes and Matza (1957), this study seeks to develop a theoretical framework which can aid organizations in developing content for mobile device security policies and increase employee’s intention to comply.


title: Strengths of character and posttraumatic growth

abstract: How are strengths of character related to growth following trauma? A retrospective Web-based study of 1,739 adults found small, but positive associations among the number of potentially traumatic events experienced and a number of cognitive and interpersonal character strengths. It was concluded that growth following trauma may entail the strengthening of character.


title: FPGA based implementation of deep neural networks using on-chip memory only

abstract: Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM. The execution time and energy consumption of the developed system is compared with a GPU based implementation. Since the capacity of memory in FPGA is limited, only 3-bit weights are used for this implementation, and training based fixed-point weight optimization is employed. The implementation using Xilinx XC7Z045 is tested for the MNIST handwritten digit recognition benchmark and a phoneme recognition task on TIMIT corpus. The obtained speed is about one quarter of a GPU based implementation and much better than that of a PC based one. The power consumption is less than 5 Watt at the full speed operation resulting in much higher efficiency compared to GPU based systems.


title: Using Measures for Verifying and Improving Requirement Models in MDD Processes

abstract: It is well known that an appropriate requirement specification is essential for the success of software development projects. In the model-driven development context, the requirement models can be used as entry artifacts for aligning the final software products with the stakeholders' needs. Thus, it is necessary to solve any modeling issue that may prevent the correct translation of the input requirement artifacts, i.e., the requirement models need to be verified for assuring the completeness of the design (MDD-oriented) models generated. In this paper, we face this issue by presenting an approach to integrate specific measures that automate the verification of a goal-oriented requirement approach -- the i framework -- in the context of a MDD development process.


FINDING STRUCTURE WITH RANDOMNESS : PROBABILISTIC ALGORITHMS FOR CONSTRUCTING


title: Effects of a neuromuscular controller on a powered ankle exoskeleton during human walking

abstract: Wearable devices to assist abnormal gaits require controllers that interact with the user in an intuitive and unobtrusive manner. To design such a controller, we investigated a bio-inspired walking controller for orthoses and prostheses. We present (i) a Simulink neuromuscular control library derived from a computational model of reflexive neuromuscular control of human gait with a central pattern generator (CPG) extension, (ii) an ankle reflex controller for the Achilles exoskeleton derived from the library, and (iii) the mechanics and energetics of healthy subjects walking with an actuated ankle orthosis using the proposed controller. As this controller was designed to mimic human reflex patterns during locomotion, we hypothesize that walking with this controller would lead to lower energetic costs, compared to walking with the added mass of the device only, and allow for walking at different speeds without explicit control. Preliminary results suggest that the neuromuscular controller does not disturb walking dynamics in both slow and normal walking cases and can also reduce the net metabolic cost compared to transparent mode of the device. Reductions in tibialis anterior and soleus activity were observed, suggesting the controller could be suitable, in future work, for augmenting or replacing normal walking functions. We also investigated the impedance patterns generated by the neuromuscular controller. The validity of the equivalent variable impedance controller, particularly in stance phase, can facilitate serving subject-specific features by linking impedance measurement and neuromuscular controller.


title: A Study of Various Clustering Algorithms on Retail Sales Data

abstract: Data mining is the process of extraction of Hidden knowledge from the databases. Clustering is one the important functionality of the data mining Clustering is an adaptive methodology in which objects are grouped together, based on the principle of optimizing the inside class similarity and minimizing the class-class similarity. Various clustering algorithms have been developed resulting in a better performance on datasets for clustering. The paper discusses the four major clustering algorithms: KMeans, Density based, Filtered, Farthest First clustering algorithm and comparing the performances of these principle clustering algorithms on the aspect of correctly class wise cluster building ability of algorithm .The results are tested on datasets of retail sales using WEKA interface and compute the correctly cluster building instances in proportion with incorrectly formed cluster. A comparison of these four algorithms is given on the basis of percentage of incorrectly classified instances.


title: Comparison of ECC and RSA Algorithm in Resource Constrained Devices

abstract: Nowadays, the usage of resource constrained devices is increasing and these devices are primarily working with sensitive data. Consequently, data security has become crucial for both producers and users. Limitation of resources is deemed as the major issue that makes these devices vulnerable. Attackers might exploit these limitations to get access to the valuable data. Therefore, carefully chosen and practically tested encryption algorithm must be applied to increase the device efficiency and mitigate the risk of sensitive data loss. This study will compare elliptic curve cryptography (ECC) algorithm with Key size of 160-bit and Rivest-Shamir-Adleman (RSA) algorithm with Key size of 1024-bit. As a result of this study utilizing ECC in resource constrained devices has advantages over RSA but ECC needs continues enhancement to satisfy the limitations of newly designed chips.


title: Personal control and stress and coping processes: a theoretical analysis.

abstract: Laboratory and field research indicates that the relationships between personal control and stress, coping, and adaptational outcomes are more complex than was once assumed. Believing that an event is controllable does not always lead to a reduction in stress or to a positive outcome, and believing that an event is uncontrollable does not always lead to an increase in stress or to a negative outcome. These complex relationships involving control are examined in the context of Lazarus's cognitive theory of stress and coping. The first part of the article elaborates this theory and shows how two forms of control, generalized beliefs about control and situational appraisals of control, fit into the overall model. Situational appraisals of control are explored in this section, including the question, Control over what? which must be addressed in order to explain some of the perplexing findings. The second part of the article draws on the theoretical formulation of stress and coping to examine three important issues: (a) how believing one has control in a stressful transaction can heighten threat, (b) the relationship between control and coping, and (c) pathways through which control can affect the adaptational outcomes of stressful encounters.


title: Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review

abstract: Research in the field of Computer Supported Collaborative Learning (CSCL) is based on a wide variety of methodologies. In this paper, we focus upon content analysis, which is a technique often used to analyze transcripts of asynchronous, computer mediated discussion groups in formal educational settings. Although this research technique is often used, standards are not yet established. The applied instruments reflect a wide variety of approaches and differ in their level of detail and the type of analysis categories used. Further differences are related to a diversity in their theoretical base, the amount of information about validity and reliability, and the choice for the unit of analysis. This article presents an overview of different content analysis instruments, building on a sample of models commonly used in the CSCL-literature. The discussion of 15 instruments results in a number of critical conclusions. There are questions about the coherence between the theoretical base and the operational translation of the theory in the instruments. Instruments are hardly compared or contrasted with one another. As a consequence the empirical base of the validity of the instruments is limited. The analysis is rather critical when it comes to the issue of reliability. The authors put forward the need to improve the theoretical and empirical base of the existing instruments in order to promote the overall quality of CSCL-research.


title: DNA barcoding in Mucorales: an inventory of biodiversity

abstract: The order Mucorales comprises predominantly fast-growing saprotrophic fungi, some of which are used for the fermentation of foodstuffs but it also includes species known to cause infections in patients with severe immune or metabolic impairments. To inventory biodiversity in Mucorales ITS barcodes of 668 strains in 203 taxa were generated covering more than two thirds of the recognised species. Using the ITS sequences, Molecular Operational Taxonomic Units were defined by a similarity threshold of 99 %. An LSU sequence was generated for each unit as well. Analysis of the LSU sequences revealed that conventional phenotypic classifications of the Mucoraceae are highly artificial. The LSU- and ITS-based trees suggest that characters, such as rhizoids and sporangiola, traditionally used in mucoralean taxonomy are plesiomorphic traits. The ITS region turned out to be an appropriate barcoding marker in Mucorales. It could be sequenced directly in 82 % of the strains and its variability was sufficient to resolve most of the morphospecies. Molecular identification turned out to be problematic only for the species complexes of Mucor circinelloides, M. flavus, M. piriformis and Zygorhynchus moelleri. As many as 12 possibly undescribed species were detected. Intraspecific variability differed widely among mucorealean species ranging from 0 % in Backusella circina to 13.3 % in Cunninghamella echinulata. A high proportion of clinical strains was included for molecular identification. Clinical isolates of Cunninghamella elegans were identified molecularly for the first time. As a result of the phylogenetic analyses several taxonomic and nomenclatural changes became necessary. The genus Backusella was emended to include all species with transitorily recurved sporangiophores. Since this matched molecular data all Mucor species possessing this character were transferred to Backusella. The genus Zygorhynchus was shown to be polyphyletic based on ITS and LSU data. Consequently, Zygorhynchus was abandoned and all species were reclassified in Mucor. Our phylogenetic analyses showed, furthermore, that all non-thermophilic Rhizomucor species belong to Mucor. Accordingly, Rhizomucor endophyticus was transferred to Mucor and Rhizomucor chlamydosporus was synonymised with Mucor indicus. Lecto-, epi- or neotypes were designated for several taxa.


title: Energy production risks: what perspective should we take

abstract: The controversy over energy-production risks is in part due to uncertainties and disagree- ments over the specification of these systems and their characteristics and in part due to the lack of a solid conceptual framework for comparisons between qualitatively different types of risks. The difficulties in specifying energy risks arise primarily from the necessity of using oversimplified descriptions of energy systems and their effects. The major simplifications include the use of marginal analysis (even when inappropriate), the omission of indirect risks, and the incorrect specification of the systems to be analyzed. Comparisons between qualita- tively different risks are hampered by the lack of a solid basis for treating occupational risks relative to public risks, catastrophic risks versus chronic risks, and risks distributed differently in time. The difficult problems encountered when social values relating to risk change rapidly lead to delay and indecision. The choice of analytical simplifications and the specification of values systems for energy analysis are best made by considering the context and application of the analysis.


title: IP covert timing channels: design and detection

abstract: A network covert channel is a mechanism that can be used to leak information across a network in violation of a security policy and in a manner that can be difficult to detect. In this paper, we describe our implementation of a covert network timing channel, discuss the subtle issues that arose in its design, and present performance data for the channel. We then use our implementation as the basis for our experiments in its detection. We show that the regularity of a timing channel can be used to differentiate it from other traffic and present two methods of doing so and measures of their efficiency. We also investigate mechanisms that attackers might use to disrupt the regularity of the timing channel, and demonstrate methods of detection that are effective against them.


title: Information diffusion on Facebook: a case study of the sunflower student movement in Taiwan

abstract: On March 18, 2014, some students occupied the Taiwanese Parliament Building in order to resume the negotiation for the Cross-Strait Service Trade Agreement (CSSTA). They posted as well as shared posts on Facebook and even created fan pages on Facebook to fight against CSSTA. The influence of Facebook is constantly growing, and there are studies that focus on information diffusion in order to reach a large audience. We analyze information diffusion through message spreading such as sharing posts or commenting on posts. Most relevant studies use social connections in analysis of information diffusion. However, due to the privacy protection mechanism on Facebook, social connections in data available to the public are implicit, while explicit social connections such as friend lists are private data. We use public data collected from fan pages on Facebook to analyze information diffusion. Our method does not rely on the availability of social connections, and therefore it can be applied to other social media websites. We investigate the influence of posts on these fan pages in this special civil movement. We analyze users' reaction behaviors and develop a method to find important users who change the speed of information diffusion and thus play a crucial role.


title: Functional α7 Nicotinic ACh Receptors on Astrocytes in Rat Hippocampal CA1 Slices

abstract: Although much is known about the functional expression of the neuronal nicotinic acetylcholine receptors (nAChRs) in various neuronal populations in the brain and elsewhere, much less is known about their expression and functional relevance in glial cells. The expression of functional nAChRs has been reported for cultured astrocytes; however, previous work has failed to detect nAChR-mediated responses in astrocytes in acute slices. In the current study, functional α7 nAChRs on astrocytes in the CA1 region of the rat hippocampus were studied in situ using whole-cell patch-clamp recording and two-photon calcium imaging techniques in acute slices. We found that astrocytes and the chondroitin sulfate proteoglycan NG2-expressing (i.e., NG2) cells did express functional α7 nAChRs. Although the amplitudes of the responses were small, they could be enhanced by the α7-selective positive allosteric modulator PNU-120596. Under these conditions, we found that in comparing the properties of these responses between astrocytes, NG2, and interneurons, there were differences in the kinetics and increases in intracellular calcium levels. This is the first demonstration of functional α7 nAChR-mediated current responses in astrocytes in acute hippocampal slices, data which may shed light on the role of α7 nAChRs in neuroprotection.


title: You've got Mail, and Here is What you Could do With It!: Analyzing and Predicting Actions on Email Messages

abstract: With email traffic increasing, leading Web mail services have started to offer features that assist users in reading and processing their inboxes. One approach is to identify "important" messages, while a complementary one is to bundle messages, especially machine-generated ones, in pre-defined categories. We rather propose here to go back to the task at hand and consider what actions the users might conduct on received messages. We thoroughly studied, in a privacy-preserving manner, the actions of a large number of users in Yahoo mail, and found out that the most frequent actions are typically read, reply, delete and a sub-type of delete, delete-without-read. We devised a learning framework for predicting these four actions, for users with various levels of activity per action. Our framework leverages both vertical learning for personalization and horizontal learning for regularization purposes. In order to verify the quality of our predictions, we conducted a large-scale experiment involving users who had previously agreed to participate in such research studies. Our results show that, for recall values of 90%, we can predict important actions such as read or reply at precision levels up to 40% for active users, which we consider pretty encouraging for an assistance task. For less active users, we show that our regularization achieves an increase in AUC of close to 50%. To the best of our knowledge, our work is the first to provide a unified framework of this scale for predicting multiple actions on Web email, which hopefully provides a new ground for inventing new user experiences to help users process their inboxes.


title: Image and tag retrieval by leveraging image-group links with multi-domain graph embedding

abstract: A large number of images are available on online photo-sharing services along with rich meta-data, including tags, groups, and locations, etc. For associating two domains of different modalities, e.g. images and tags, Canonical Correlation Analysis (CCA) and its extended methods are used widely. We employ a more flexible graph embedding method called Cross-Domain Matching Correlation Analysis (CDMCA), which can deal with many-to-many relationships between any number of domains, for associating images, tags, and groups. Experiments of Tag-to-Image and Image-to-Tag retrieval tasks are performed on Flickr images. Tags are represented by feature vectors based on semantic word embedding, which enables the searching of even unseen tags. Groups are collections of images, and they provide the information of image-group links. Our experiments show that image and tag retrieval tasks improve by utilizing the group information as the third domain.