title: Treatment with the Self-Discovery Camp (SDiC) improves Internet gaming disorder

abstract: Abstract Introduction Internet gaming disorder (IGD) is a novel behavioral addiction that influences the physical, mental, and social aspects of health due to excessive Internet gaming. One type of intensive treatment for IGD is the therapeutic residential camp (TRC), which comprises many types of therapies, including psychotherapy, psychoeducational therapy, and cognitive behavioral therapy. The TRC was developed in South Korea and has been administered to many patients with IGD; however, its efficacy in other countries remains unknown. We investigated the efficacy of the Self-Discovery Camp (SDiC), a Japanese version of a TRC, and the correlations between individual characteristics and outcome measures. Methods We recruited 10 patients with IGD (all male, mean age = 16.2 years, diagnosed using the DSM-5) to spend 8 nights and 9 days at the SDiC. We measured gaming time as well as self-efficacy (using the Stages of Change Readiness and Treatment Eagerness Scale, a measure of therapeutic motivation and problem recognition). Results Total gaming time was significantly lower 3 months after the SDiC. Problem recognition and self-efficacy towards positive change also improved. Furthermore, there was a correlation between age of onset and problem recognition score. Conclusions Our results demonstrate the effectiveness of the SDiC for IGD, especially regarding gaming time and self-efficacy. Additionally, age of onset may be a useful predictor of IGD prognosis. Further studies with larger sample sizes and control groups, and that target long-term outcomes, are needed to extend our understanding of SDiC efficacy.


title: Using distance estimation and deep learning to simplify calibration in food calorie measurement

abstract: High calorie intake in the human body on the one hand, has proved harmful in numerous occasions leading to several diseases and on the other hand, a standard amount of calorie intake has been deemed essential by dietitians to maintain the right balance of calorie content in human body. As such, researchers have proposed a variety of automatic tools and systems to assist users measure their calorie in-take. In this paper, we consider the category of those tools that use image processing to recognize the food, and we propose a method for fully automatic and user-friendly calibration of the dimension of the food portion sizes, which is needed in order to measure food portion weight and its ensuing amount of calories. Experimental results show that our method, which uses deep learning, mobile cloud computing, distance estimation and size calibration inside a mobile device, leads to an accuracy improvement to 95 percent on average compared to previous work.


Shining the Floodlights on Mobile Web Tracking — A Privacy Survey


title: Detection of financial statement fraud and feature selection using data mining techniques

abstract: Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies.


The Acceptance Of Dual Processing Evaluation Theories in Accessing Online Information Credibility or


title: Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm

abstract: The Levenberg-Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss-Newton algorithms. The advantages and disadvantages of the different algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally the recursive Levenberg-Marquardt algorithm has better convergence properties than the other algorithms.


title: To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots

abstract: Teleoperated robots are playing an increasingly important role in military actions and medical services. In the future, remotely operated surgical robots will likely be used in more scenarios such as battlefields and emergency response. But rapidly growing applications of teleoperated surgery raise the question; what if the computer systems for these robots are attacked, taken over and even turned into weapons? Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II, an advanced teleoperated robotic surgery system. We identify a slew of possible cyber security threats, and experimentally evaluate their scopes and impacts. We demonstrate the ability to maliciously control a wide range of robots functions, and even to completely ignore or override command inputs from the surgeon. We further find that it is possible to abuse the robot's existing emergency stop (E-stop) mechanism to execute efficient (single packet) attacks. We then consider steps to mitigate these identified attacks, and experimentally evaluate the feasibility of applying the existing security solutions against these threats. The broader goal of our paper, however, is to raise awareness and increase understanding of these emerging threats. We anticipate that the majority of attacks against telerobotic surgery will also be relevant to other teleoperated robotic and co-robotic systems.


title: A model of authorization for next-generation database systems

abstract: The conventional models of authorization have been designed for database systems supporting the hierarchical, network, and relational models of data. However, these models are not adequate for next-generation database systems that support richer data models that include object-oriented concepts and semantic data modeling concepts. Rabitti, Woelk, and Kim [14] presented a preliminary model of authorization for use as the basis of an authorization mechanism in such database systems. In this paper we present a fuller model of authorization that fills a few major gaps that the conventional models of authorization cannot fill for next-generation database systems. We also further formalize the notion of implicit authorization and refine the application of the notion of implicit authorization to object-oriented and semantic modeling concepts. We also describe a user interface for using the model of authorization and consider key issues in implementing the authorization model.


title: A review on Fog Computing technology

abstract: Out of the many computing and software oriented models that are being adopted by Computer Networking, Fog Computing has captured quite a wide audience in Research and Industry. There is a lot of confusion on its precise definition, position, role and application. The Internet of Things (IOT), todays' digitized intelligent connectivity domain, demands real time response in many applications and services. This renders Fog Computing a suitable platform for achieving goals of autonomy and efficiency. This paper is a justification of the concepts, interest, approaches, and practices of Fog Computing. It describes the need for adopting this new model and investigate its prime features by elucidating the scenarios for implementing it, thereby outlining its significance in the IoT world.


title: Long short-term memory for speaker generalization in supervised speech separation.

abstract: Speech separation can be formulated as learning to estimate a time-frequency mask from acoustic features extracted from noisy speech. For supervised speech separation, generalization to unseen noises and unseen speakers is a critical issue. Although deep neural networks (DNNs) have been successful in noise-independent speech separation, DNNs are limited in modeling a large number of speakers. To improve speaker generalization, a separation model based on long short-term memory (LSTM) is proposed, which naturally accounts for temporal dynamics of speech. Systematic evaluation shows that the proposed model substantially outperforms a DNN-based model on unseen speakers and unseen noises in terms of objective speech intelligibility. Analyzing LSTM internal representations reveals that LSTM captures long-term speech contexts. It is also found that the LSTM model is more advantageous for low-latency speech separation and it, without future frames, performs better than the DNN model with future frames. The proposed model represents an effective approach for speaker- and noise-independent speech separation.


title: Continuous Word Representation using Neural Networks for Proper Name Retrieval from Diachronic Documents

abstract: Developing high-quality transcription systems for very large vocabulary corpora is a challenging task. Proper names are usually key to understanding the information contained in a document. One approach for increasing the vocabulary coverage of a speech transcription system is to automatically retrieve new proper names from contemporary diachronic text documents. In recent years, neural networks have been successfully applied to a variety of speech recognition tasks. In this paper, we investigate whether neural networks can enhance word representation in vector space for the vocabulary extension of a speech recognition system. This is achieved by using high-quality word vector representation of words from large amounts of unstructured text data proposed by Mikolov. This model allows to take into account lexical and semantic word relationships. Proposed methodology is evaluated in the context of broadcast news transcription. Obtained recall and ASR proper name error rate is compared to that obtained using cosine-based vector space methodology. Experimental results show a good ability of the proposed model to capture semantic and lexical information


title: Body Parts Detection for People Tracking Using Trees of Histogram of Oriented Gradient Descriptors

abstract: Vision algorithms face many challenging issues when itcomes to analyze human activities in video surveillance applications.For instance, occlusions makes the detectionand tracking of people a hard task to perform. Hence advancedand adapted solutions are required to analyze thecontent of video sequences. We here present a people detectionalgorithm based on a hierarchical tree of Histogramof Oriented Gradients referred to as HOG. The detectionis coupled with independently trained body part detectorsto enhance the detection performance and to reach state ofthe art performances. We adopt a person tracking schemewhich calculates HOG dissimilarities between detected personsthroughout a sequence. The algorithms are tested invideos with challenging situations such as occlusions. Falsealarms are further reduced by using 2D and 3D informationof moving objects segmented from a background referenceframe.


title: Distributed optimization over time-varying directed graphs

abstract: We consider distributed optimization by a collection of nodes, each having access to its own convex function, whose collective goal is to minimize the sum of the functions. The communications between nodes are described by a time-varying sequence of directed graphs, which is uniformly strongly connected. For such communications, assuming that every node knows its out-degree, we develop a broadcast-based algorithm, termed the subgradient-push, which steers every node to an optimal value under a standard assumption of subgradient boundedness. The subgradient-push requires no knowledge of either the number of agents or the graph sequence to implement. Our analysis shows that the subgradient-push algorithm converges at a rate of $O(\ln(t)/\sqrt{t})$, where the constant depends on the initial values at the nodes, the subgradient norms, and, more interestingly, on both the consensus speed and the imbalances of influence among the nodes.


title: Identifying critical issues in enterprise resource planning (ERP) implementation

abstract: Much has been written on implementation of enterprise resource planning (ERP) systems in organizations of various sizes. The literature is replete with many cases studies of both successful and unsuccessful ERP implementations. However, there have been very few empirical studies that attempt to delineate the critical issues that drive successful implementation of ERP systems. Although the failure rates of ERP implementations have been publicized widely, this has not distracted companies from investing large sums of money on ERP systems. This study reports the results of an empirical research on the critical issues affecting successful ERP implementation. Through the study, eight factors were identified that attempts to explain 86% of the variances that impact ERP implementation. There was a strong correlation between successfully implementing ERP and six out of the eight factors identified.


title: A method for locating multiple sources from a frame of a large-aperture microphone array data without tracking

abstract: In this paper we present a new method for locating multiple sound sources using only a local segment of data from a large-aperture microphone array. The result of this work may be used directly or as an open-loop input to a tracking algorithm. The proposed method employs the proven- robust steered response power using the phase transform as a functional, agglomerative clustering, and low-cost global optimization (stochastic region contraction). We test the algorithm with five simultaneous "talkers" under very difficult conditions in a real room but using electrical speakers instead of human talkers to have a controlled experiment.


title: Analyzing attributes of vessel populations

abstract: Almost all diseases affect blood vessel attributes (vessel number, radius, tortuosity, and branching pattern). Quantitative measurement of vessel attributes over relevant vessel populations could thus provide an important means of diagnosing and staging disease. Unfortunately, little is known about the statistical properties of vessel attributes. In particular, it is unclear whether vessel attributes fit a Gaussian distribution, how dependent these values are upon anatomical location, and how best to represent the attribute values of the multiple vessels comprising a population of interest in a single patient. The purpose of this report is to explore the distributions of several vessel attributes over vessel populations located in different parts of the head. In 13 healthy subjects, we extract vessels from MRA data, define vessel trees comprising the anterior cerebral, right and left middle cerebral, and posterior cerebral circulations, and, for each of these four populations, analyze the vessel number, average radius, branching frequency, and tortuosity. For the parameters analyzed, we conclude that statistical methods employing summary measures for each attribute within each region of interest for each patient are preferable to methods that deal with individual vessels, that the distributions of the summary measures are indeed Gaussian, and that attribute values may differ by anatomical location. These results should be useful in designing studies that compare patients with suspected disease to a database of healthy subjects and are relevant to groups interested in atlas formation and in the statistics of tubular objects.


title: Spatiotemporal frequency tuning of BOLD and gamma band MEG responses compared in primary visual cortex

abstract: In this study, the spatial and temporal frequency tuning characteristics of the MEG gamma (40–60 Hz) rhythm and the BOLD response in primary visual cortex were measured and compared. In an identical MEG/fMRI paradigm, 10 participants viewed reversing square wave gratings at 2 spatial frequencies [0.5 and 3 cycles per degree (cpd)] reversing at 5 temporal frequencies (0, 1 6, 10, 15 Hz). Three-dimensional images of MEG source power were generated with synthetic aperture magnetometry (SAM) and showed a high degree of spatial correspondence with BOLD responses in primary visual cortex with a mean spatial separation of 6.5 mm, but the two modalities showed different tuning characteristics. The gamma rhythm showed a clear increase in induced power for the high spatial frequency stimulus while BOLD showed no difference in activity for the two spatial frequencies used. Both imaging modalities showed a general increase of activity with temporal frequency, however, BOLD plateaued around 6–10 Hz while the MEG generally increased with a dip exhibited at 6 Hz. These results demonstrate that the two modalities may show activation in similar spatial locations but that the functional pattern of these activations may differ in a complex manner, suggesting that they may be tuned to different aspects of neuronal activity.


title: Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques

abstract: Cervical cancer is the fourth most common malignant disease in women’s worldwide. In most cases, cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Most medical data sets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used data set, SMOTE is used to solve this problem. The data set consists of 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.


title: Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

abstract: We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by the MCS switchings, we further introduce a switching cost factor in the proposed algorithm. The simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ~ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, is 30% higher than that of the upper confidence bandit (UCB) algorithm, and is 100% higher than that of the signal-to-noise ratio (SNR)-based algorithm. Meanwhile, the proposed algorithm with the switching cost factor can achieve a higher primary transmission rate than those of the benchmark algorithms without increasing system overheads.


Living the American Dream in Finland : The Social Mobility of Inventors


title: High Voltage High Speed Low Power Rail-to-Rail Source Driver for 8-bit Large TFT LCD Applications

abstract: A high-speed low-power source driver using a new rail-to-rail scheme is proposed, designed and evaluated for large TFT LCD applications. The proposed output driver is composed of a differential amplifier stage, second amplifier stage, driver selector, dual push-pull output driver stages for rail-to-rail output driving and feedback path selector. The output driver amplifier achieves a very large driving capability by eliminating the of the output switches used for polarity control. This increases the output driving speed while retaining low quiescent current consumption and can minimize the area of the output driving transistors and switches used for output polarity inversion. The fabricated 8-bit source driver implemented in 0.3 mum 18 V high voltage CMOS technology demonstrates that the driver exhibits maximum settling times of 8.1 mus and 8.3 ns for the rising and falling edges with dot inversion under a 10-kohm/300-pF panel load. The quiescent current consumptions for the PMOS input driver and NMOS input driver are 10 muA/channel on average. The values of the differential nonlinearity (DNL) and integral nonlinearity (INL) are less than +/-0.5 LSB.


title: Lipidomics for studying metabolism

abstract: Lipidomics uses the principles of analytical chemistry to reveal the total lipid content and composition in a cell. In this Review, Xianlin Han provides an overview of lipidomics as applied to the study of metabolic pathways and how this technique can help in the understanding of metabolic diseases.


title: EMG-based control of an exoskeleton robot for human forearm and wrist motion assist

abstract: We have been developing exoskeleton robots for assisting the motion of physically weak individuals such as elderly or slightly disabled in daily life. In this paper we propose an EMG-based control of a three degree of freedom (3-DOF) exoskeleton robot for the forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper describes the hardware design of the exoskeleton robot and the control method. The skin surface electromyographic (EMG) signals of muscles in forearm of the exoskeleton's user and the hand force/forearm torque are used as input information for the proposed controller. By applying the skin surface EMG signals as main input signals to the controller, automatic control of the robot can be realized without manipulating any other equipment. Fuzzy control method has been applied to realize the natural and flexible motion assist. An experiment has been performed to evaluate the proposed exoskeleton robot.


title: An approach for malware behavior identification and classification

abstract: Malware is one of the major security threats that can break computer operation. However, commercial anti-virus or anti-spyware that used signature-based matching to detects malware cannot solve that kind of threats. Nowadays malware writers try to avoid detection by using several techniques such as polymorphic, metamorphic and also hiding technique. In order to overcome that issue, we proposed a new framework for malware behavior identification and classification that apply dynamic approach. This framework consists of two major processes such as behavior identification and malware classification. These two major processes will integrate together as interrelated process in our proposed framework. Result from this study is a new framework that able to identify and classify malware based on it behaviors.


title: Simulating and analyse the power factor for Flyback Converters

abstract: This paper present a study about a power supply, study which can be applied to another supply circuits. This study can be useful also in the learning process. I start the study from modulation of mathematical equations for Flyback Converter. I demonstrate the difference between Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) true the Matlab graphics. I made analytic numerical calculations to determine the dependencies between power factor and voltage transfer ratio. I made also a Pspice simulation to demonstrate the input filter importance in diminution of harmonics from input current.


title: A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

abstract: This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.


title: Trajectory planning for stair climbing in the leg-wheel hybrid mobile robot quattroped

abstract: The algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot is reported. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. A brief study on stability of the robot is included. In addition, the first-step and last-step algorithm which deals with the transient between the flat ground and the stair is reported as well. Finally, simulation and experimental validation are performed to evaluate the performance of the algorithm.


title: Data-procedural languages for fpl-based machines

abstract: This paper introduces a new high level programming language for a novel class of computational devices namely data-procedural machines. These machines are by up to several orders of magnitude more efficient than the von Neumann paradigm of computers and are as flexible and as universal as computers. Their efficiency and flexibility is achieved by using field-programmable logic as the essential technology platform. The paper briefly summarizes and illustrates the essential new features of this language by means of two example programs.


title: Social Influence Based Clustering and Optimization over Heterogeneous Information Networks

abstract: Social influence analysis has shown great potential for strategic marketing decision. It is well known that people influence one another based on both their social connections and the social activities that they have engaged in the past. In this article, we develop an innovative and high-performance social influence based graph clustering framework with four unique features. First, we explicitly distinguish social connection based influence (self-influence) and social activity based influence (co-influence). We compute the self-influence similarity between two members based on their social connections within a single collaboration network, and compute the co-influence similarity by taking into account not only the set of activities that people participate but also the semantic association between these activities. Second, we define the concept of influence-based similarity by introducing a unified influence-based similarity matrix that employs an iterative weight update method to integrate self-influence and co-influence similarities. Third, we design a dynamic learning algorithm, called SI-Cluster, for social influence based graph clustering. It iteratively partitions a large social collaboration network into K clusters based on both the social network itself and the multiple associated activity information networks, each representing a category of activities that people have engaged. To make the SI-Cluster algorithm converge fast, we transform sophisticated nonlinear fractional programming problem with respect to multiple weights into a straightforward nonlinear parametric programming problem of single variable. Finally, we develop an optimization technique of diagonalizable-matrix approximation to speed up the computation of self-influence similarity and co-influence similarities. Our SI-Cluster-Opt significantly improves the efficiency of SI-Cluster on large graphs while maintaining high quality of clustering results. Extensive experimental evaluation on three real-world graphs shows that, compared to existing representative graph clustering algorithms, our SI-Cluster-Opt approach not only achieves a very good balance between self-influence and co-influence similarities but also scales extremely well for clustering large graphs in terms of time complexity while meeting the guarantee of high density, low entropy and low Davies--Bouldin Index.


Overcoming Ethnocentrism Through Developing Intercultural Communication Sensitivity and Multiculturalism


title: Design and analysis of a transformable spherical robot for multi-mode locomotion

abstract: In this manuscript, a novel transformable spherical robot for multi-mode locomotion is proposed. The robot has the spherical shell and an internal driving unit for spherical rolling, and also has a quadruped unit for walking. The robot can change the locomotion mode between spherical rolling mode and the quadruped walking mode to adapt to varied terrains. Moreover, the analysis and simulation of the robot moving across the obstacle shows that hybrid driving mode has better mobile performance than basic driving mode. The experiments that the prototype moves in the real environment show that the robot could adapt more types of terrains.


title: Emotion regulation choice: A conceptual framework and supporting evidence

abstract: Choice behavior is considered the fundamental means by which individuals exert control over their environments. One important choice domain that remains virtually unexplored is that of emotion regulation. This is surprising given that healthy adaptation requires flexibly choosing between regulation strategies in a manner that is responsive to differing situational demands. In the present article, we provide a broad conceptual framework that systematically evaluates the rules that govern the ways individuals choose between different emotion regulation strategies. This conceptual account is buttressed by empirical findings from 6 studies that show the effects of hypothesized emotional, cognitive, and motivational determinants of regulation choice (Studies 1–3) and illuminate the mechanisms that underlie choices between different emotion regulation strategies (Studies 4–6). Broad implications and future directions are discussed.


title: Residual Networks are Exponential Ensembles of Relatively Shallow Networks

abstract: In this work, we introduce a novel interpretation of residual networks showing they are exponential ensembles. This observation is supported by a large-scale lesion study that demonstrates they behave just like ensembles at test time. Subsequently, we perform an analysis showing these ensembles mostly consist of networks that are each relatively shallow. For example, contrary to our expectations, most of the gradient in a residual network with 110 layers comes from an ensemble of very short networks, i.e., only 10-34 layers deep. This suggests that in addition to describing neural networks in terms of width and depth, there is a third dimension: multiplicity, the size of the implicit ensemble. Ultimately, residual networks do not resolve the vanishing gradient problem by preserving gradient flow throughout the entire depth of the network - rather, they avoid the problem simply by ensembling many short networks together. This insight reveals that depth is still an open research question and invites the exploration of the related notion of multiplicity.


title: Anchor Loss in Hemispherical Shell Resonators

abstract: Micromachined hemispherical shell resonators (HSRs) can be used in high accuracy vibratory gyroscopes. These resonators need to have very low energy loss to achieve very high quality factor. Energy might be lost through the anchor, fluid-structure interaction, thermoelastic dissipation, phonon-phonon and phonon-electron interactions, and the resonator surface. This paper investigates energy loss through the anchor of HSRs using a numerical approach. To numerically determine wave radiation from the anchor to the infinite substrate, a perfectly matched layer is used around a finite substrate. Anchor loss investigations in HSRs are classified into four categories. First, the effects of shell properties-material, geometry, and imperfections-are investigated. Second, the relationships between anchor loss and properties of the stem, such as material, geometry, and stemshell misalignments, are studied. Third, the effects of substrate characteristics-substrate material, attachment material between the stem and substrate, and attachment configuration of the substrate and stem-are investigated. Finally, the effects of external motions, such as shock and rotation, are analyzed. It is found that anchor loss in HSRs strongly depends on the shell, stem, and substrate properties. This study also shows that any imperfection in the shell or any misalignment between the shell and stem increases anchor loss by orders of magnitude.


title: Design of a Low-Cost Underwater Acoustic Modem

abstract: There has been an increasing interest in creating short-range, low data rate, underwater wireless sensor networks for scientific marine exploration and monitoring. However, the lack of an inexpensive, underwater acoustic modem is preventing the proliferation of these sensor networks. Thus, we are building an underwater acoustic modem starting with the most critical component from a cost perspective-the transducer. The design substitutes a commercial transducer with a homemade transducer using cheap piezoceramic material and builds the rest of the modem's components around the properties of the transducer to extract as much performance as possible. This letter describes the high level design, and cost and power characteristics of each of the major modem components: the transducer, the analog transceiver, and the digital signal processor of our modem prototype.


title: Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar

abstract: This paper proposes a new approach to solve the problem of real-time vision-based hand gesture recognition with the combination of statistical and syntactic analyses. The fundamental idea is to divide the recognition problem into two levels according to the hierarchical property of hand gestures. The lower level of the approach implements the posture detection with a statistical method based on Haar-like features and the AdaBoost learning algorithm. With this method, a group of hand postures can be detected in real time with high recognition accuracy. The higher level of the approach implements the hand gesture recognition using the syntactic analysis based on a stochastic context-free grammar. The postures that are detected by the lower level are converted into a sequence of terminal strings according to the grammar. Based on the probability that is associated with each production rule, given an input string, the corresponding gesture can be identified by looking for the production rule that has the highest probability of generating the input string.


title: 1 Fast Linear Discriminant Analysis using QR Decomposition and Regularization

abstract: The work of the first ::: three authors was supported in part by the National Science Foundation grants ACI-0305543 and CCF-0621889. Any opinions, ::: findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect ::: the views of the National Science Foundation.


title: GSM based Automatic Irrigation Control System for Efficient Use of Resources and Crop Planning by Using an Android Mobile

abstract: The greenhouse based modern agriculture industries are the recent requirement in every part of agriculture in India. In this technology, the humidity and temperature of plants are precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place in large farmhouse, which makes very difficult to maintain the uniformity at all the places in the farmhouse manually. It is observed that for the first time an android phone-control the Irrigation system, which could give the facilities of maintaining uniform environmental conditions are proposed. The Android Software Development Kit provides the tools and Application Programmable Interface necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of human life serving multiple needs of humans. This application makes use of the GPRS (General Packet Radio Service) feature of mobile phone as a solution for irrigation control system. GSM (Global System for Mobile Communication) is used to inform the user about the exact field condition. The information is passed onto the user request in the form of SMS. Drip irrigation is artificial method of supplying water to the roots of the plant. It is also called micro irrigation. In past few years there is a rapid growth in this system. The user communicates with the centralized unit through SMS. The centralized unit communicates with the system through SMS which will be received by the GSM with the help of the SIM card. The GSM sends this data to ARM7which is also continuously receives the data from sensors in some form of codes. After processing, this data is displayed on the LCD. Thus in short whenever the system receives the activation command from the subscriber it checks all the field conditions and gives a detailed feedback to the user and waits for another activation command to start the motor. The motor is controlled by a simple manipulation in the internal structure of the starter. The starter coil is indirectly activated by means of a transistorized relay circuit. When the motor is started, a constant monitoring on soil moisture and water level is done & once the soil moisture is reached to sufficient level the motor is automatically turned off & a massage is send to subscriber that the motor is turned off. The water level indicator indicates three levels low, medium, high and also empty tank. Shen etc. al (2007) introduced a GSM-SMS remote measurement and control system for greenhouse based on PC-based database system connected with base station. Base station is developed by using a microcontroller, GSM module, sensors and actuators. In practical operation, the central station receives and sends messages through GSM module. Criterion value of parameters to be measured in every base station is set by central station, and then in base stations parameters including the air temperature, the air humidity. Indu etc. al (2013) mainly focuses on reviews in the field of remote monitoring and control, the technology used and their potential advantages. The paper proposes an innovative GSM/Bluetooth based remote controlled embedded system for irrigation. The system sets the irrigation time depending on the temperature and humidity reading from sensors and type of crop and can automatically irrigate the field when unattended. Information is exchanged between far end and designed system via SMS on GSM network. A Bluetooth module is also interfaced with the main microcontroller chip which eliminates the SMS charges when the user is within the limited range of few meters to the designated system. The system informs users about many conditions like status of electricity, dry running motor, increased temperature, water content in soil and smoke via SMS on GSM network or by Bluetooth. The GSM based irrigation system (Fig.1) may offer users the flexibility to regulate and control the operations of their irrigation systems with little intervention to reduce runoff from over watering for improvement in crop yield. This enables users to take advantage of the globally deployed GSM networks with its low SMS service cost to use mobile phones and simple SMS commands to manage their irrigation system. It will be possible for users to use SMS to monitor directly the conditions of their farmland, schedule the water


title: Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery

abstract: Increased demand for hybrid and electric vehicles has motivated research to improve onboard state of charge (SOC) and state of health estimation (SOH). In particular, batteries with composite electrodes have become popular for automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. SOH algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes, such as lithium consumption at the solid-electrolyte interface or active material dissolution. Furthermore, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells. In this paper, we first present a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3Co1/3O2 electrode battery that predicts the surface and bulk lithium concentration of each material in the composite electrode, as well as the current split between each material. The model is then used in dual-nonlinear observers to estimate the cell SOC and loss of cyclable lithium over time. Three different observer types are compared: 1) the extended Kalman filter; 2) fixed interval Kalman smoother; and 3) particle filter. Finally, an experimental aging campaign is used to compare the estimated capacities for five different cells with the measured capacities over time.


title: Classification of Three Types of Walking Activities Regarding Stairs Using Plantar Pressure Sensors

abstract: In this letter, we propose a novel method for classifying ambulatory activities using eight plantar pressure sensors within smart shoes. Using these sensors, pressure data of participants can be collected regarding level walking, stair descent, and stair ascent. Analyzing patterns of the ambulatory activities, we present new features with which to describe the ambulatory activities. After selecting critical features, a multi-class support vector machine algorithm is applied to classify these activities. Applying the proposed method to the experimental database, we obtain recognition rates up to 95.2% after six steps.


title: Making Sense of Entities and Quantities in Web Tables

abstract: HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad-hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re-use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables. This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base. To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption), monetary (e.g. revenue), temporal (e.g. date) and dimensionless (e.g. counts) measures. Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.


title: Reconstituting knowledge management

abstract: Purpose – The purpose of the paper is to improve traditional knowledge management models in light of complexity theory, emphasizing the importance of moving away from hierarchical relationships among data, information, knowledge, and wisdom.Design/methodology/approach – Traditional definitions and models are critically reviewed and their weaknesses highlighted. A transformational perspective of the traditional hierarchies is proposed to highlight the need to develop better perspectives. The paper demonstrates the holistic nature of data, information, knowledge, and wisdom, and how they are all based on an interpretation of existence.Findings – Existing models are logically extended, by adopting a complexity‐based perspective, to propose a new model – the E2E model – which highlights the non‐linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of m...


title: Spatial resilience: integrating landscape ecology, resilience, and sustainability

abstract: Landscape ecology has a high potential to contribute to sustainability in the interactions of people and nature. Landscape ecologists have already made considerable progress towards a more general understanding of the relevance of spatial variation for ecosystems. Incorporating the complexities of societies and economies into landscape ecology analyses will, however, require a broader framework for thinking about spatial elements of complexity. An exciting recent development is to explicitly try to integrate landscape ecology and ideas about resilience in social–ecological systems through the concept of spatial resilience. Spatial resilience focuses on the importance of location, connectivity, and context for resilience, based on the idea that spatial variation in patterns and processes at different scales both impacts and is impacted by local system resilience. I first introduce and define the concepts of resilience and spatial resilience and then discuss some of their potential contributions to the further interdisciplinary integration of landscape ecology, complexity theory, and sustainability science. Complexity theorists have argued that many complex phenomena, such as symmetry-breaking and selection, share common underlying mechanisms regardless of system type (physical, social, ecological, or economic). Similarities in the consequences of social exclusion and habitat fragmentation provide an informative example. There are many strong parallels between pattern–process interactions in social and ecological systems, respectively, and a number of general spatial principles and mechanisms are emerging that have relevance across many different kinds of system. Landscape ecologists, with their background in spatially explicit pattern–process analysis, are well placed to contribute to this emerging research agenda.


title: Negotiate, reciprocate, or cooperate? The impact of exchange modes on inter-employee knowledge sharing

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to investigate the impact of exchange modes – negotiated, reciprocal, generalized, and productive – on inter-employee knowledge sharing. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Based on the affect theory of social exchange, a theoretical model was developed and empirically tested using a survey of 691 employees from 15 North American credit unions. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The negotiated mode of knowledge exchange, i.e. when a knowledge contributor explicitly establishes reciprocation conditions with a recipient, develops negative knowledge sharing attitude. The reciprocal mode, i.e. when a knowledge donor assumes that a receiver will reciprocate, has no effect on knowledge sharing attitude. The generalized exchange form, i.e. when a knowledge contributor believes that other organizational members may reciprocate, is weakly related to knowledge sharing attitude. The productive exchange mode, i.e. when a knowledge provider assumes he or she is a responsible citizen within a cooperative enterprise, strongly facilitates the development of knowledge sharing attitude, which, in turn, leads to knowledge sharing intentions. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: To facilitate inter-employee knowledge sharing, managers should focus on the development of positive knowledge sharing culture when all employees believe they contribute to a common good instead of expecting reciprocal benefits. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This is one of the first studies to apply the affect theory of social exchange to study knowledge sharing.


title: Fast, Large-Scale String Match for a 10Gbps FPGA-Based Network Intrusion Detection System

abstract: Intrusion Detection Systems such as Snort scan incoming packets for evidence of security threats. The most computation-intensive part of these systems is a text search against hundreds of patterns, and must be performed at wire-speed. FPGAs are particularly well suited for this task and several such systems have been proposed. In this paper we expand on previous work, in order to achieve and exceed a processing bandwidth of 11Gbps. We employ a scalable, low-latency architecture, and use extensive fine-grain pipelining to tackle the fan-out, match, and encode bottlenecks and achieve operating frequencies in excess of 340MHz for fast Virtex devices. To increase throughput, we use multiple comparators and allow for parallel matching of multiple search strings. We evaluate the area and latency cost of our approach and find that the match cost per search pattern character is between 4 and 5 logic cells.


KnowYourNyms? A Game of Semantic Relationships


title: Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation

abstract: Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.


title: V1 activity is reduced during binocular rivalry

abstract: During binocular rivalry, one of two incompatible monocular stimuli is erased from perceptual awareness for seconds at a time. To examine whether this "rivalry suppression" occurs in V1, we measured functional magnetic resonance imaging activity during binocular rivalry and compared it with those in the two reference conditions: one representing complete suppression and the other representing no suppression. We found that the amplitude of V1 activity during rivalry fell midway between those in the two reference conditions; the amount of V1 activity associated with the nondominant pattern was reduced by 48% to 77% during rivalry. The same pattern of results was obtained with meaningful rival targets (i.e., a human face and a house). In this work, using a different experimental protocol, we confirmed the findings of earlier imaging studies that neuronal events associated with binocular rivalry occur as early as V1. Furthermore, our findings extend those earlier findings by demonstrating robust neural suppression during binocular rivalry regardless of the stimulus complexity of the rivaling targets.


title: Context-based Space Filling Curves

abstract: A context-based scanning technique for images is presented. An image is scanned along a context-based space filling curve that is computed so as to exploit inherent coherence in the image. The resulting one-dimensi onal representation of the image has improved autocorrelation compared with universal scans such as the PeanoHilbert space filling curve. An efficient algorithm for computing context-based space filling curves is presented. We also discuss the potential of improved autocorrelation of context-based space filling curves for image and video lossless compression.


title: Pharmaceutical development and optimization of azithromycin suppository for paediatric use

abstract: Pharmaceutical development and manufacturing process optimization work was undertaken in order to propose a potential paediatric rectal formulation of azithromycin as an alternative to existing oral or injectable formulations. The target product profile was to be easy-to-use, cheap and stable in tropical conditions, with bioavailability comparable to oral forms, rapidly achieving and maintaining bactericidal concentrations. PEG solid solution suppositories were characterized in vitro using visual, HPLC, DSC, FTIR and XRD analyses. In vitro drug release and in vivo bioavailability were assessed; a study in rabbits compared the bioavailability of the optimized solid solution suppository to rectal solution and intra-venous product (as reference) and to the previous, non-optimized formulation (suspended azithromycin suppository). The bioavailability of azithromycin administered as solid solution suppositories relative to intra-venous was 43%, which compared well to the target of 38% (oral product in humans). The results of 3-month preliminary stability and feasibility studies were consistent with industrial production scale-up. This product has potential both as a classical antibiotic and as a product for use in severely ill children in rural areas. Industrial partners for further development are being sought.


title: Design Principles for Industrie 4.0 Scenarios: A Literature Review

abstract: Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted definition of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a literature review, the paper provides a definition of Industrie 4.0 and identifies six design principles for its implementation: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in implementing appropriate scenarios.


title: Estimating human shape and pose from a single image

abstract: We describe a solution to the challenging problem of estimating human body shape from a single photograph or painting. Our approach computes shape and pose parameters of a 3D human body model directly from monocular image cues and advances the state of the art in several directions. First, given a user-supplied estimate of the subject's height and a few clicked points on the body we estimate an initial 3D articulated body pose and shape. Second, using this initial guess we generate a tri-map of regions inside, outside and on the boundary of the human, which is used to segment the image using graph cuts. Third, we learn a low-dimensional linear model of human shape in which variations due to height are concentrated along a single dimension, enabling height-constrained estimation of body shape. Fourth, we formulate the problem of parametric human shape from shading. We estimate the body pose, shape and reflectance as well as the scene lighting that produces a synthesized body that robustly matches the image evidence. Quantitative experiments demonstrate how smooth shading provides powerful constraints on human shape. We further demonstrate a novel application in which we extract 3D human models from archival photographs and paintings.


title: Amoeba: A Shape changing Storage System for Big Data

abstract: Data partitioning significantly improves the query performance in distributed database systems. A large number of techniques have been proposed to efficiently partition a dataset for a given query workload. However, many modern analytic applications involve ad-hoc or exploratory analysis where users do not have a representative query workload upfront. Furthermore, workloads change over time as businesses evolve or as analysts gain better understanding of their data. Static workload-based data partitioning techniques are therefore not suitable for such settings. ::: ::: In this paper, we describe the demonstration of Amoeba, a distributed storage system which uses adaptive multi-attribute data partitioning to efficiently support ad-hoc as well as recurring queries. Amoeba applies a robust partitioning algorithm such that ad-hoc queries on all attributes have similar performance gains. Thereafter, Amoeba adaptively repartitions the data based on the observed query sequence, i.e., the system improves over time. All along Amoeba offers both adaptivity (i.e., adjustments according to workload changes) as well as robustness (i.e., avoiding performance spikes due to workload changes). We propose to demonstrate Amoeba on scenarios from an internet-of-things startup that tracks user driving patterns. We invite the audience to interactively fire fast ad-hoc queries, observe multi-dimensional adaptivity, and play with a robust/reactive knob in Amoeba. The web front end displays the layout changes, runtime costs, and compares it to Spark with both default and workload-aware partitioning.


title: Lip augmentation.

abstract: Lip augmentation has become increasingly popular in recent years as a reflection of cultural trends emphasizing youth and beauty. Techniques to enhance the appearance of the lips have evolved with advances in biotechnology. An understanding of lip anatomy and aesthetics forms the basis for successful results. We outline the pertinent anatomy and aesthetics of the preoperative evaluation. A summary of various filler materials available is provided. Augmentation options include both injectable and open surgical techniques. The procedures and materials currently favored by the authors are described in greater detail.


Social relationship problems in children: an approach to intervention


title: An Intelligent Multi-Agent Recommender System for Human Capacity Building

abstract: This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together for recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.


title: Damped-SOGI-Based Control Algorithm for Solar PV Power Generating System

abstract: This paper deals with two stage solar PV power generating system with improved power quality in three-phase distribution system. This system not only feeds the power to the grid but it also provides the load compensation, power factor correction and harmonics elimination. For this, a double stage system is used where first stage is a DC-DC boost converter, which performs the MPPT (Maximum Power Point Tracking). For extracting maximum power from the PV string, an incremental conductance based MPPT algorithm is used. Moreover, in second stage a voltage source converter (VSC) is utilized. For control of VSC, a damped-SOGI (Second Order Generalized Integrator) algorithm is proposed. By using damped-SOGI based control algorithm, fundamental active and reactive power components of load currents are extracted for estimating the reference grid currents. After comparing these reference grid currents with sensed grid currents, these produce the switching pulses for the grid tied VSC. A prototype of the proposed system is developed in the laboratory. Test results are shown to validate the design and control algorithm under steady state and dynamic conditions at linear and nonlinear loads.


Personalized Security Messaging: Nudges for Compliance with Browser Warnings


A calibration curve for immobilized dihydrofolate reductase activity assay


title: A 100 watts L-band power amplifier

abstract: In this paper, a 1100–1350 MHz power amplifier is introduced. For the realization of the RF power amplifier an LDMOS transistor is used. The amplifier delivers 100W power to a 50 ohm load with 50% efficiency. The transducer power gain of the amplifier is has 16dB at 100W output power. A mixed lumped element and transmission line matching circuit is used to implement matching network. The simulations are performed by the nonlinear model of the transistor, which is provided by the manufacturer. Finally the results of simulations are compared with the measurements.


title: Dynamic Humanoid Locomotion: A Scalable Formulation for HZD Gait Optimization

abstract: Hybrid zero dynamics (HZD) has emerged as a popular framework for dynamic walking but has significant implementation difficulties when applied to the high degrees of freedom humanoids. The primary impediment is the process of gait design—it is difficult for optimizers to converge on a viable set of virtual constraints defining a gait. This paper presents a methodology that allows for fast and reliable generation of dynamic robotic walking gaits through the HZD framework, even in the presence of underactuation. Specifically, we describe an optimization formulation that builds upon the novel combination of HZD and direct collocation methods. Furthermore, achieving a scalable implementation required developing a defect-variable substitution formulation to simplify expressions, which ultimately allows us to generate compact analytic Jacobians of the constraints. We experimentally validate our methodology on an underactuated humanoid, DURUS, a spring-legged machine designed to facilitate energy-economical walking. We show that the optimization approach, in concert with the HZD framework, yields dynamic and stable walking gaits in hardware with a total electrical cost of transport of 1.33.


title: Discovering Commonsense Entailment Rules Implicit in Sentences

abstract: Reasoning about ordinary human situations and activities requires the availability of diverse types of knowledge, including expectations about the probable results of actions and the lexical entailments for many predicates. We describe initial work to acquire such a collection of conditional (if--then) knowledge by exploiting presuppositional discourse patterns (such as ones involving 'but', 'yet', and 'hoping to') and abstracting the matched material into general rules.


title: Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies

abstract: Reinforcement learning is a promising new approach for automatically developing effective policies for real-time self-management. RL can achieve superior performance to traditional methods, while requiring less built-in domain knowledge. Several case studies from real and simulated systems management applications demonstrate RL's promises and challenges. These studies show that standard online RL can learn effective policies in feasible training times. Moreover, a Hybrid RL approach can profit from any knowledge contained in an existing policy by training on the policy's observable behavior, without needing to interface directly to such knowledge


title: Towards Competency Question-Driven Ontology Authoring

abstract: Ontology authoring is a non-trivial task for authors who are not proficient in logic. It is difficult to either specify the requirements for an ontology, or test their satisfaction. In this paper, we propose a novel approach to address this problem by leveraging the ideas of competency questions and test-before software development. We first analyse real-world competency questions collected from two different domains. Analysis shows that many of them can be categorised into patterns that differ along a set of features. Then we employ the linguistic notion of presupposition to describe the ontology requirements implied by competency questions, and show that these requirements can be tested automatically.


title: Non-native text analysis: A survey

abstract: Non-native speakers of English far outnumber native speakers; English is the main language of books, newspapers, airports, air-traffic control, international business, academic conferences, science, technology, diplomacy, sports, international competitions, pop music, and advertising (British Council 2014). Online education in the form of massive online open courses is also primarily in English—even teaching English. This creates enormous amounts of text written by non-native speakers, which in turn generates a need for grammar correction and analysis. Even aside from massive online open courses, the number of English learners in Asia alone is in the tens of millions. In this paper, we provide a survey of the two main areas of existing work on non-native text analysis, prefaced by an overview of common datasets used by researchers, comparing their attributes and potential uses. Then, an introduction to native language identification follows: determining the native language of an author based on text in the second language. This section is subdivided into various techniques and a shared task on this classification problem. Next, we discuss non-native grammatical error correction—finding and modifying text to fix errors or to make it sound more fluent. Again, we discuss different methods before investigating a relevant shared task. Lastly, we end with conclusions and potential future directions. While this survey primarily focuses on detecting and correcting non-native English text, many approaches are general and can be used across any language pairing.


title: Hybrid Fuzz Testing: Discovering Software Bugs via Fuzzing and Symbolic Execution

abstract: Random mutational fuzz testing (fuzzing) and symbolic executions are program testing techniques that have been gaining popularity in the security research community. Fuzzing finds bugs in a target program by natively executing it with random inputs while monitoring the execution for abnormal behaviors such as crashes. While fuzzing may have a reputation of being able to explore deep into a program’s state space efficiently, naive fuzzers usually have limited code coverage for typical programs since unconstrained random inputs are unlikely to drive the execution down many different paths. In contrast, symbolic execution tests a program by treating the program’s input as symbols and interpreting the program over such symbolic inputs. Although in theory symbolic execution is guaranteed to be effective in achieving code coverage if we explore all possible paths, this generally requires exponential resource and is thus not practical for many real-world programs. This thesis presents our attempt to attain the best of both worlds by combining fuzzing with symbolic execution in a novel manner. Our technique, called hybrid fuzzing, first uses symbolic execution to discover frontier nodes that represent unique paths in the program. After collecting as many frontier nodes as possible under a user-specifiable resource constraint, it transits to fuzz the program with preconditioned random inputs, which are provably random inputs that respect the path predicate leading to each frontier node. Our current implementation supports programs with linear path predicates and can automatically generate preconditioned random inputs from a polytope model of the input space extracted from binaries. These preconditioned random inputs can then be used with any fuzzer. Experiments show that our implementation is efficient in both time and space, and the inputs generated by it are able to gain extra breadth and depth over previous approaches.


title: A Clockwork RNN

abstract: Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when long-term memory is required. This paper introduces a simple, yet powerful modification to the simple RNN (SRN) architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate. Rather than making the standard RNN models more complex, CW-RNN reduces the number of SRN parameters, improves the performance significantly in the tasks tested, and speeds up the network evaluation. The network is demonstrated in preliminary experiments involving three tasks: audio signal generation, TIMIT spoken word classification, where it outperforms both SRN and LSTM networks, and online handwriting recognition, where it outperforms SRNs.


title: Germanium aluminum nitride thin films for piezo-MEMS devices

abstract: This paper reports on piezoelectric polarity inversion of AlN thin films by doping. The germanium doped aluminum nitride (GeAlN) thin films are grown by RF magnetron sputtering. Doping amount is varied from 0 to 23%. The maximum piezoelectric polarity inversion from Al polarity to N polarity is observed at 3% of Ge where the piezoelectric d 33 value changes from + 7.8 to −7.7 pC/N. The bulk acoustic wave (BAW) resonator with the stack of both N polarity and Al polarity layers is demonstrated. The B AW resonator is excited at 2nd overtone mode at 2.58 GHz. Finally, the mechanism of polarity inversion of GeAlN is discussed and first-principal calculations of GeAlN system are presented.


title: How much is my car worth? A methodology for predicting used cars prices using Random Forest

abstract: Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponentially increasing. Car sellers sometimes take advantage of this scenario by listing unrealistic prices owing to the demand. Therefore, arises a need for a model that can assign a price for a vehicle by evaluating its features taking the prices of other cars into consideration. In this paper, we use supervised learning method namely Random Forest to predict the prices of used cars. The model has been chosen after careful exploratory data analysis to determine the impact of each feature on price. A Random Forest with 500 Decision Trees were created to train the data. From experimental results, the training accuracy was found out to be 95.82%, and the testing accuracy was 83.63%. The the model can predict the price of cars accurately by choosing the most correlated features.


title: PSYCHOLOGICAL SCIENCE Research Article FITTING THE MIND TO THE WORLD: Face Adaptation and Attractiveness Aftereffects

abstract: Average faces are attractive, but what is average depends on experience. We examined the effect of brief exposure to consistent facial distortions on what looks normal (average) and what looks attractive. Adaptation to a consistent distortion shifted what looked most normal, and what looked most attractive, toward that distortion. These normality and attractiveness aftereffects occurred when the adapting and test faces differed in orientation by 90° (∓45° vs. −45°), suggesting adaptation of high-level neurons whose coding is not strictly retino-topic. Our results suggest that perceptual adaptation can rapidly recalibrate people's preferences to fit the faces they see. The results also suggest that average faces are attractive because of their central location in a distribution of faces (i.e., prototypicality), rather than because of any intrinsic appeal of particular physical characteristics. Recalibration of preferences may have important consequences, given the powerful effects of perceived attractivene...


title: DramaBank: Annotating Agency in Narrative Discourse

abstract: We describe the Story Intention Graph, a set of discourse relations designed to represent aspects of narrative. Compared to prior models, ours is a novel synthesis of the notions of goal, plan, intention, outcome, affect and time that is amenable to corpus annotation. We describe a collection project, DramaBank, which includes encodings of texts ranging from small fables to epic poetry and contemporary nonfiction.


title: Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction

abstract: Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words, either homogeneous or heterogeneous. Experimental results show the effectiveness of the proposed approach for both tasks. The corpus-based approach is validated to work almost as well as the knowledge-based approach for computing word semantics.


title: Effect of Aldosterone and Mineralocorticoid Receptor Blockade on Vascular Inflammation

abstract: Aldosterone, the final product of the renin-angiotensin-aldosterone system, is classically viewed as a regulator of renal sodium and potassium handling, blood volume, and blood pressure. Recent studies suggest that aldosterone can cause microvascular damage, vascular inflammation, oxidative stress and endothelial dysfunction. In animal models, aldosterone-mediated vascular injury in the brain, heart, and kidneys leads to stroke, myocardial injury, and proteinuria. These effects may be modified by dietary salt intake; aldosterone-mediated vascular damage is increased in susceptible animals fed a high-salt diet compared to a low-salt diet despite lower plasma aldosterone levels on the high-salt diet. In humans, there is a growing literature supporting the adverse effects of aldosterone in heart failure, hypertension, left ventricular hypertrophy, and renal disease. Aldosterone receptor antagonists are beneficial even in patients on angiotensin converting enzyme inhibitors and attenuate aldosterone-mediated vascular injury by mechanisms that appear to be independent of changes in systolic blood pressure. This review focuses on the adverse effects of aldosterone on the vascular system and describes our current understanding of the underlying mechanisms for this injury.


title: Learning to Segment via Cut-and-Paste

abstract: This paper presents a weakly-supervised approach to object instance segmentation. Starting with known or predicted object bounding boxes, we learn object masks by playing a game of cut-and-paste in an adversarial learning setup. A mask generator takes a detection box and Faster R-CNN features, and constructs a segmentation mask that is used to cut-and-paste the object into a new image location. The discriminator tries to distinguish between real objects, and those cut and pasted via the generator, giving a learning signal that leads to improved object masks. We verify our method experimentally using Cityscapes, COCO, and aerial image datasets, learning to segment objects without ever having seen a mask in training. Our method exceeds the performance of existing weakly supervised methods, without requiring hand-tuned segment proposals, and reaches \(90\%\) of supervised performance.


title: Poststroke Fatigue: A 2-Year Follow-Up Study of Stroke Patients in Sweden

abstract: Background and Purpose — Fatigue is common among stroke patients. This study determined the prevalence of fatigue among long-term survivors after stroke and what impact fatigue had on various aspects of daily life and on survival. Methods — This study was based on Riks-Stroke, a hospital-based national register for quality assessment of acute stroke events in Sweden. During the first 6 months of 1997, 8194 patients were registered in Riks-Stroke, and 5189 were still alive 2 years after the stroke. They were followed up by a mail questionnaire, to which 4023 (79%) responded. Patients who reported that they always felt depressed were excluded. Results — To the question, “Do you feel tired?” 366 (10.0%) of the patients answered that they always felt tired, and an additional 1070 (29.2%) were often tired. Patients who always felt tired were on average older than the rest of the study population (74.5 versus 71.5 years, P Conclusions — Fatigue is frequent and often severe, even late after stroke. It is associated with profound deterioration of several aspects of everyday life and with higher case fatality, but it usually receives little attention by healthcare professionals. Intervention studies are needed.


title: Comparing salient point detectors

abstract: The use of salient points in content-based retrieval allows an image index to represent local properties of the image. Classic corner detectors can also be used for this purpose but they have drawbacks when are applied to various natural images mainly because visual features do not need to be corners and corners may gather in small regions. In this paper, we present a salient point detector using wavelet transform and we compare it with two corner detectors using two criteria: repeatability rate and information content. We determine which detector gives the best results and show that it satisfies the criteria well.


Branch Prediction Strategies and Branch Target Buffer Design


title: High performance crawling system

abstract: In the present paper, we will describe the design and implementation of a real-time distributed system of Web crawling running on a cluster of machines. The system crawls several thousands of pages every second, includes a high-performance fault manager, is platform independent and is able to adapt transparently to a wide range of configurations without incurring additional hardware expenditure. We will then provide details of the system architecture and describe the technical choices for very high performance crawling. Finally, we will discuss the experimental results obtained, comparing them with other documented systems


title: Dual-band compact square waveguide corrugated polarizer

abstract: A compact polarizer based on a corrugated waveguide of square cross-section including the input and output transitions is analysed by means of an accurate and efficient fullwave software based on mode matching, and a dual-frequency band polarizer of high electrical performance in the Ku band has been "fully" designed. The good agreement between the numerical and experimental results confirms both the design process and the proposed polarizer which can easily include different cross-sections of the attached orthomode transducer (OXIT) and horn devices, thus resulting in a very compact antenna.


title: Low-Dimensional Embeddings of Logic

abstract: Many machine reading approaches, from shallow information extraction to deep semantic parsing, map natural language to symbolic representations of meaning. Representations such as first-order logic capture the richness of natural language and support complex reasoning, but often fail in practice due to their reliance on logical background knowledge and the difficulty of scaling up inference. In contrast, low-dimensional embeddings (i.e. distributional representations) are efficient and enable generalization, but it is unclear how reasoning with embeddings could support the full power of symbolic representations such as first-order logic. In this proof-ofconcept paper we address this by learning embeddings that simulate the behavior of first-order logic.


title: Automatic detection of acute myeloid leukemia from microscopic blood smear image

abstract: Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.


title: Novel blood pressure estimation method using single photoplethysmography feature

abstract: Continuous blood pressure (BP) monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease. However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time (PTT), the simultaneous record of photoplethysmography (PPG) signal and electrocardiography (ECG) are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices. In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error −0.91 ± 3.84 mmHg for SBP estimation and −0.36 ± 3.36 mmHg for DBP estimation respectively. This approach performed better than the traditional PTT based BP estimation, which mean error for SBP estimation was −0.31 ± 4.78 mmHg, and for DBP estimation was −0.18 ± 4.32 mmHg. Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices.


title: Do extracurricular activities protect against early school dropout

abstract: This study examined the relation between involvement in school-based extracurricular activities and early school dropout. Longitudinal assessments were completed for 392 adolescents (206 girls, 186 boys) who were initially interviewed during 7th grade and followed up annually to 12th grade. A person-oriented cluster analysis based on Interpersonal Competence Scale ratings from teachers in middle schools (i.e., 7th-8th grades) identified configurations of boys and giris who differed in social-academic competence. Early school dropout was defined as failure to complete the 11th grade. Findings indicate that the school dropout rate among at-risk students was markedly lower for students who had earlier participated in extracurricul ar activities compared with those who did not participate (p < .001). However, extracurricular involvement was only modestly related to early school dropout among students who had been judged to be competent or highly competent during middle school. North American public secondary schools are unique in terms of the number and range of pursuits that they support in the classroom and beyond. In addition to offering a broad academic curriculum, middle schools and high schools encourage students to participate in various extracurricular activities; these include organized sports, special-interest academic pursuits, vocational clubs, supervised student government, newspapers, yearbooks, and various other activities. Extracurricular activities differ from standard courses in school because they are optional, ungraded, and are usually conducted outside the school day in school facilities. Although "extra" to the curriculum, many activities are closely linked to academic achievement and performance (e.g., math ciub, French club, national honor society). It seems reasonable to expect that participation in these clubs and organizations should enhance and extend classroom instruction. Other activities—such as football and other interscholastic athletics— have little obvious connection to academic achievement. It has been proposed that participation in the latter nonacademic extracurricular activities may be beneficial nonetheless. Participation could, for example, raise an individual's status within the school, extend her or his social affiliations in the school community (e.g., Csikszentmihalyi, Rathunde, & Whalen, 1993; Eder, 1985; Eder & Parker, 1987; Kinney, 1993), or enable both to occur.


title: The virtual playground: an educational virtual reality environment for evaluating interactivity and conceptual learning

abstract: The research presented in this paper aims at investigating user interaction in immersive virtual learning environments, focusing on the role and the effect of interactivity on conceptual learning. The goal has been to examine if the learning of young users improves through interacting in (i.e. exploring, reacting to, and acting upon) an immersive virtual environment (VE) compared to non-interactive or non-immersive environments. Empirical work was carried out with more than 55 primary school students between the ages of 8 and 12, in different between-group experiments: an exploratory study, a pilot study, and a large-scale experiment. The latter was conducted in a virtual environment designed to simulate a playground. In this “Virtual Playground,” each participant was asked to complete a set of tasks designed to address arithmetical “fractions” problems. Three different conditions, two experimental virtual reality (VR) conditions and a non-VR condition, that varied the levels of activity and interactivity, were designed to evaluate how children accomplish the various tasks. Pre-tests, post-tests, interviews, video, audio, and log files were collected for each participant, and analysed both quantitatively and qualitatively. This paper presents a selection of case studies extracted from the qualitative analysis, which illustrate the variety of approaches taken by children in the VEs in response to visual cues and system feedback. Results suggest that the fully interactive VE aided children in problem solving but did not provide a strong evidence of conceptual change as expected; rather, it was the passive VR environment, where activity was guided by a virtual robot, that seemed to support student reflection and recall, leading to indications of conceptual change.


title: Altered Corticostriatal Functional Connectivity in Obsessive-compulsive Disorder

abstract: Context Neurobiological models of obsessive-compulsive disorder (OCD) emphasize disturbances in the function and connectivity of brain corticostriatal networks, or “loops.” Although neuroimaging studies of patients have supported this network model of OCD, very few have applied measurements that are sensitive to brain connectivity features. Objective Using resting-state functional magnetic resonance imaging, we tested the hypothesis that OCD is associated with disturbances in the functional connectivity of primarily ventral corticostriatal regions, measured from coherent spontaneous fluctuations in the blood oxygenation level–dependent (BOLD) signal. Design Case-control cross-sectional study. Setting Hospital referral OCD unit and magnetic resonance imaging facility. Participants A total of 21 patients with OCD (10 men, 11 women) and 21 healthy control subjects matched for age, sex, and estimated intelligence. Main Outcome Measures Voxelwise statistical parametric maps testing the strength of functional connectivity of 4 striatal seed regions of interest (dorsal caudate nucleus, ventral caudate/nucleus accumbens, dorsal putamen, and ventral putamen) with remaining brain areas. Results For both groups, there was a clear distinction in the pattern of cortical connectivity of dorsal and ventral striatal regions, consistent with the notion of segregated motor, associative, and limbic corticostriatal networks. Between groups, patients with OCD had significantly increased functional connectivity along a ventral corticostriatal axis, implicating the orbitofrontal cortex and surrounding areas. The specific strength of connectivity between the ventral caudate/nucleus accumbens and the anterior orbitofrontal cortex predicted patients' overall symptom severity ( r 2 = 0.57; P Conclusions This study directly supports the hypothesis that OCD is associated with functional alterations of brain corticostriatal networks. Specifically, our findings emphasize abnormal and heightened functional connectivity of ventrolimbic corticostriatal regions in patients with OCD.


title: Investigating the new 64b/66b encoding scheme's power spectral density

abstract: With the emergence of new high-speed data standards, such as the IEEE 802.3ba, the clock rate on individual channels can go up to 10 Gbps creating the need for highly efficient encoding schemes. One such scheme is the 64b/66b. An undesired effect of using encoding schemes is the spreading of signal power to higher harmonics, thereby requiring greater bandwidth to transmit the signal. A clear understanding of the exact effect of the encoding scheme on the PSD of the transmitted data will help in better understanding the bandwidth requirements associated with these encoding schemes. This will prevent over budgeting of the channel requirements and allow for more exact specifications to be developed. This paper analyzes the change in the power spectral density of random data when encoded using 64b/66b scheme. A comparison to similar effects caused by using 8b/10b is presented for reference. The results show consistent variations in PSD which allow us to characterize the encoding scheme.


title: Beyond process monitoring : a proof-of-concept of event-driven business activity management

abstract: Purpose – The purpose of this paper is to show how to employ complex event processing (CEP) for the observation and management of business processes. It proposes a conceptual architecture of BPM event producer, processor, and consumer and describes technical implications for the application with standard software in a perfect order scenario.Design/methodology/approach – The authors discuss business process analytics as the technological background. The capabilities of CEP in a BPM context are outlined an architecture design is proposed. A sophisticated proof‐of‐concept demonstrates its applicability.Findings – The results overcome the separation and data latency issues of process controlling, monitoring, and simulation. Distinct analyses of past, present, and future blur into a holistic real‐time approach. The authors highlight the necessity for configurable event producer in BPM engines, process event support in CEP engines, a common process event format, connectors to visualizers, notifiers and return c...


title: Color texture moments for content-based image retrieval

abstract: We adopt a local Fourier transform as a texture representation scheme and derive eight characteristic maps for describing different aspects of cooccurrence relations of image pixels in each channel of the (SVcosH, SVsinH, V) color space. Then we calculate the first and second moments of these maps as a representation of the natural color image pixel distribution, resulting in a 48-dimensional feature vector. The novel low-level feature is named color texture moments (CTM), which can also be regarded as a certain extension to color moments in eight aspects through eight orthogonal templates. Experiments show that this new feature can achieve good retrieval performance for CBIR.


title: Neuro-Based Artificial Intelligence Model for Loan Decisions

abstract: Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an ena bling tool for evaluating credit applications to su pport loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpropagation learning algorithm was used to buil d up the proposed model. Results: Different representative cases of loan applications were cons idered based on the guidelines of different banks i n Jordan, to validate the neural network model. Conclusion: The results indicated that artificial neural networks are a successful technology that can be us ed in loan application evaluation in the Jordanian commercial banks.


title: Infectious complications in the renal transplant recipient.

abstract: The aim of this study was to estimate the incidence of infectious diseases in a group of patients who underwent kidney transplantation from January 1, 2004 to September 30, 2004, including 121 operations, with 119 from cadaveric and 2 from living donors. The protocol sought herpes viruses (CMV, VZV, and EBV), hepatitis viruses, human immunodeficiency virus, T. gondii, M. tubercolosis, and T. pallidum. Therapy for CMV was used both as prophylaxis in immunoglobulin (Ig)G-negative recipients from IgG-positive donors and preemptive therapy, that is, before the appearance of clinical symptoms, but after viremia reached borderline levels. For VZV infections, the treatment started after the appearance of papulo-vesicular cutaneous eruptions and antibody positivity. The treatment for pneumonia consisted of empirical therapy after radiography; for pyelonephritis, antibiotic therapy was based on the results of kidney echography, blood culture, and urine culture. Infectious complications appeared in 25 patients (20.7%), 3 of the which were polymicrobic: 12 CMV infections, 9 VZV infections, 3 pneumoniae, 4 pyelonephritis, and 1 salmonellosis. The most frequent infection was CMV, which occurred in the first 3 months after transplantation in 9 of 12 cases. This study showed that a knowledge of infection prevalence can help the physician to establish a more specific, efficacious antimicrobial therapy, despite the laboratory response not being available in a short time.


title: Performance improvement of Support Vector Machine (SVM) With information gain on categorization of Indonesian news documents

abstract: More news articles which are unstoppable increasing, causing problems with grouping news according to appropriate kind of label. Therefore it is necessary to deal with the problem of grouping news by it's category like business news, political news, and sports news. The categorization of news document belong to text classification domain, a Machine Learning topic as an approach that addressed this problem. Various algorithms have been used in previous studies such as Bayesian techniques, k-Nearest Neighborhood, Neural Networks, and Support Vector Machine (SVM). This study provides an understanding of the SVM method for news categorization on Indonesian news dataset that contain several types of news category. Problems in text classification is the number of features that affecting classification performance with SVM. Use of Information Gain as feature selection improve accuracy than without any feature selection. Our model give satisfying result with 98,057 % accuracy of Indonesia news classification. Improvement 2,9 points from 95,11% by SVM technique without feature selection.