title: On the measurements of the dielectric constant and dissipation factor of various elastomers

abstract: This paper depicts an ongoing research on the study of the electrical properties variation of various elastomer materials with frequency and temperature variations. Based on the experimental results, an intelligent prediction method for the electrical properties is devised and tested. Results of this method are compared to traditional curve fitting techniques.


title: DroidNative: Automating and optimizing detection of Android native code malware variants

abstract: According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively (~99%) on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malware variants, thwarting detection by signature-based detectors. In addition, the plethora of more sophisticated detectors making use of static analysis techniques to detect such variants operate only at the bytecode level, meaning that malware embedded in native code goes undetected. A recent study shows that 86% of the most popular Android applications contain native code, making native code malware a plausible threat vector. This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations and provides automation. As far as we know, DroidNative is the first system that builds cross-platform (x86 and ARM) semantic-based signatures at the Android native code level, allowing the system to detect malware embedded in either bytecode or native code. When tested with a dataset of 5490 samples, DroidNative achieves a detection rate (DR) of 93.57% and a false positive rate of 2.7%. When tested with traditional malware variants, it achieves a DR of 99.48%, compared to the DRs of academic and commercial tools that range from 8.33% to 93.22%.


title: Show me the money!: an analysis of project updates during crowdfunding campaigns

abstract: Hundreds of thousands of crowdfunding campaigns have been launched, but more than half of them have failed. To better understand the factors affecting campaign outcomes, this paper targets the content and usage patterns of project updates -- communications intended to keep potential funders aware of a campaign's progress. We analyzed the content and usage patterns of a large corpus of project updates on Kickstarter, one of the largest crowdfunding platforms. Using semantic analysis techniques, we derived a taxonomy of the types of project updates created during campaigns, and found discrepancies between the design intent of a project update and the various uses in practice (e.g. social promotion). The analysis also showed that specific uses of updates had stronger associations with campaign success than the project's description. Design implications were formulated from the results to help designers better support various uses of updates in crowdfunding campaigns.


title: An advanced fingerprint matching using minutiae-based indirect local features

abstract: Biometric systems examine the uniqueness of an individual based on physical and behavioral characteristics. Among the known traits, fingerprint is the most significant biometric trait due to its ease of use and high accuracy. However, the efficiency of the fingerprint matching technique depends on the feature vector it uses. The ideal feature vector should be invariant to several common transformations, which usually a fingerprint capturing system is subjected to. Current work focuses to achieve such an invariance by extracting the features based on the spatial relationship among minutiae points. We propose a minutiae point based 4-dimensional local feature vector, which simultaneously satisfies six desirable feature vector properties. This feature vector definition helps us to deal with problem of missing and spurious minutiae and thus enables us to design a robust authentication system. We have substantiated the efficacy of the proposed approach with the help of a number of fingerprint instances available in FVC and NIST databases.


title: Deep Learning for Multi-label Classification

abstract: In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input space prior to (or during) training. This is an important task, since many multi-label methods typically create many different copies or views of the same input data as they transform it, and considerable memory can be saved by taking advantage of redundancy. In this paper, we show that a proper development of the feature space can make labels less interdependent and easier to model and predict at inference time. For this task we use a deep learning approach with restricted Boltzmann machines. We present a deep network that, in an empirical evaluation, outperforms a number of competitive methods from the literature


title: Authenticated Byzantine Fault Tolerance Without Public-Key Cryptography

abstract: We have developed a practical state-machine replication algorithm that tolerates Byzantine faults: it works correctly in asynchronous systems like the Internet and it incorporates several optimizations that improve the response time of previous algorithms by more than an order of magnitude. This paper describes the most important of these optimizations. It explains how to modify the base algorithm to eliminate the major performance bottleneck in previous systems — public-key cryptography. The optimization replaces public-key signatures by vectors of message authentication codes during normal operation, and it overcomes a fundamental limitation on the power of message authentication codes relative to digital signatures — the inability to prove that a message is authentic to a third party. As a result, authentication is more than two orders of magnitude faster while providing the same level of security.


title: The digital tendon locking mechanism of the avian foot (Aves)

abstract: Representatives of all avian orders were studied in order to establish that the tendon-locking mechanism (TLM), consisting of local specialization of the flexor tendons and the adjacent portion of the flexor tendon sheath, is by no means rare, but rather, constitutes the prevalent condition in a large majority of the avian species sampled. The areas of tubercles on the tendons and the adjacent sheath plications intermesh with one another thereby forming a true tendon-locking mechanism that maintains the distal and other interphalangeal joints of the digits in the flexed position. The TLM seems to function not only in perching, but in a wide variety of other activities of the avian foot including swimming, wading, prey-grasping, clinging, hanging, and tree climbing. The basic structural components of the mechanism are remarkably similar in the divergent avian groups adapted for these activities. Ultrastructural detail of the TLM was studied by means of scanning and transmission electron microscopy. Interdigital variation in distribution of the TLM in all of the digits of individuals were made as were comparisons of the interspecific distribution of the TLM. An analysis of the biomechanics involved in engaging the elements of the TLM and how they produce locking of the flexed joints of the digits includes a consideration of the roles of the podothecal pads, ungual flexor processes, and the elastic flexor and extensor ligaments of the toes. The components of the TLM are differentiated in early fetal development establishing that the TLM components are not acquired adventitiously in response to such factors as posthatching mechanical stresses.


title: Virtualization for Distributed Ledger Technology (vDLT)

abstract: Recently, with the tremendous development of crypto-currencies, distributed ledger technology (DLT) (e.g., blockchain) has attracted significant attention. The traditional Internet was originally design to handle the exchange of information. With DLT, we will have the Internet of value. Although, DLT has a great potential to create new foundations for our economic and social systems, the existing DLT has a number of drawbacks (e.g., scalability) that prevent it from being used as a generic platform for distributed ledger across the globe. In this paper, we present a novel virtualization approach to address the challenges in the existing DLT systems. Specifically, in the proposed virtualization for DLT (vDLT), the underlying resources (e.g., hardware, compute, storage, network, and so on) are abstracted. By providing a logical view of resources, vDLT can significantly improve the performance, facilitate system evolution, and simplify DLT management and configuration. Several use cases of vDLT are presented to illustrate the effectiveness of the proposed vDLT.


title: Clustering validity checking methods: part II

abstract: Clustering results validation is an important topic in the context of pattern recognition. We review approaches and systems in this context. In the first part of this paper we presented clustering validity checking approaches based on internal and external criteria. In the second, current part, we present a review of clustering validity approaches based on relative criteria. Also we discuss the results of an experimental study based on widely known validity indices. Finally the paper illustrates the issues that are under-addressed by the recent approaches and proposes the research directions in the field.


title: Random-data perturbation techniques and privacy-preserving data mining

abstract: Privacy is becoming an increasingly important issue in many data-mining applications. This has triggered the development of many privacy-preserving data-mining techniques. A large fraction of them use randomized data-distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. This paper questions the utility of the random-value distortion technique in privacy preservation. The paper first notes that random matrices have predictable structures in the spectral domain and then it develops a random matrix-based spectral-filtering technique to retrieve original data from the dataset distorted by adding random values. The proposed method works by comparing the spectrum generated from the observed data with that of random matrices. This paper presents the theoretical foundation and extensive experimental results to demonstrate that, in many cases, random-data distortion preserves very little data privacy. The analytical framework presented in this paper also points out several possible avenues for the development of new privacy-preserving data-mining techniques. Examples include algorithms that explicitly guard against privacy breaches through linear transformations, exploiting multiplicative and colored noise for preserving privacy in data mining applications.


Evaluating Neural Machine Translation in English-Japanese Task


title: ADE: A Framework for Robust Complex Robotic Architectures

abstract: Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, fault-recovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system's utility.


title: Controller design and analysis for automation of Chemical Water Treatment system

abstract: The paper includes designing of automatic control of a chemical water treatment plant and its analysis in National Instruments-LabVIEW. The mathematical model used for controller design depicts all the aspects of mechanical-chemical chemical water treatment plant. Transfer function derived is reduced implementing Cohen-Coon algorithm. Closed loop process offers optimal efficiency by maintaining the concentration at set point by controlling the flow rate of water feed in purifier. The system implements PID coupled with dead time compensator for maintaining the concentration of harmful effluents under optimum level. Feed forward controller anticipates the temperature of the reactor by measuring the input concentration. Also, a cascade controller is implemented to control the reactor core temperature by controlling the flow rate of cooling water. Sensor setup and master control are also comprehended for complete automation of plant.


title: Comprehensive Use of Curvature for Robust and Accurate Online Surface Reconstruction

abstract: Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on. In our work, we focus on surface curvature as a feature that is detectable on range scans alone and hence does not depend on accurate multi-sensor alignment. In contrast to previous work that took curvature into consideration, however, we treat curvature as an independent quantity that we consistently incorporate into every stage of the real-time reconstruction pipeline, including densely curvature-weighted ICP, range image fusion, local surface reconstruction, and rendering. Using multiple benchmark sequences, and in direct comparison to other state-of-the-art online acquisition systems, we show that our approach significantly reduces drift, both when analyzing individual pipeline stages in isolation, as well as seen across the online reconstruction pipeline as a whole.


title: Surgical, Therapeutic, Nursing and Sex Robots in Machine and Information Ethics

abstract: Machine medical ethics is a novel field of research for ethicists, philosophers, artificial intelligence experts, information scientists, and medical specialists. I identify surgical, therapeutic, nursing and sex robots as the primary types of medical machines in this context. I raise general questions about machine ethics with a view to its development and application, specific questions about medical machine ethics (the term and concept which I prefer), and broad questions spanning multiple non-machine ethics, including information, technology, business and legal ethics, and interrelationships between these diverse ethics and machine medical ethics. Samples of each type of question are provided in my descriptions of surgical, therapeutic, nursing and sex robots. In particular, progress in information and technology ethics is needed in order to solve moral problems involving medical machines, and progress in machine ethics to prevent some of the problems.


title: Non-Intrusive Load Disaggregation Using Graph Signal Processing

abstract: With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individual appliances used. Leveraging piecewise smoothness of the power load signal, two GSP-based NILM approaches are proposed. The first approach, based on total graph variation minimization, searches for a smooth graph signal under known label constraints. The second approach uses the total graph variation minimizer as a starting point for further refinement via simulated annealing. The proposed GSP-based NILM approach aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Simulation results using two datasets of real house measurements demonstrate the competitive performance of the GSP-based approaches with respect to traditionally used hidden Markov model-based and decision tree-based approaches.


title: Cognitive Computing: Architecture, Technologies and Intelligent Applications

abstract: With the development of network-enabled sensors and artificial intelligence algorithms, various human-centered smart systems are proposed to provide services with higher quality, such as smart healthcare, affective interaction, and autonomous driving. Considering cognitive computing is an indispensable technology to develop these smart systems, this paper proposes human-centered computing assisted by cognitive computing and cloud computing. First, we provide a comprehensive investigation of cognitive computing, including its evolution from knowledge discovery, cognitive science, and big data. Then, the system architecture of cognitive computing is proposed, which consists of three critical technologies, i.e., networking (e.g., Internet of Things), analytics (e.g., reinforcement learning and deep learning), and cloud computing. Finally, it describes the representative applications of human-centered cognitive computing, including robot technology, emotional communication system, and medical cognitive system.


title: Effect of nutrition education intervention based on Pender's Health Promotion Model in improving the frequency and nutrient intake of breakfast consumption among female Iranian students

abstract: OBJECTIVE ::: To determine the effectiveness of nutrition education intervention based on Pender's Health Promotion Model in improving the frequency and nutrient intake of breakfast consumption among female Iranian students. ::: ::: ::: DESIGN ::: The quasi-experimental study based on Pender's Health Promotion Model was conducted during April-June 2011. Information (data) was collected by self-administered questionnaire. In addition, a 3 d breakfast record was analysed. P < 0·05 was considered significant. ::: ::: ::: SETTING ::: Two middle schools in average-income areas of Qom, Iran. ::: ::: ::: SUBJECTS ::: One hundred female middle-school students. ::: ::: ::: RESULTS ::: There was a significant reduction in immediate competing demands and preferences, perceived barriers and negative activity-related affect constructs in the experimental group after education compared with the control group. In addition, perceived benefit, perceived self-efficacy, positive activity-related affect, interpersonal influences, situational influences, commitment to a plan of action, frequency and intakes of macronutrients and most micronutrients of breakfast consumption were also significantly higher in the experimental group compared with the control group after the nutrition education intervention. ::: ::: ::: CONCLUSIONS ::: Constructs of Pender's Health Promotion Model provide a suitable source for designing strategies and content of a nutrition education intervention for improving the frequency and nutrient intake of breakfast consumption among female students.


title: Evidence Data Collection with ANDROSICS Tool for Android Forensics

abstract: With the rapidly growth of mobile technology, the lifestyle of people is becoming reliant on various mobile devices like smartphones, tablets and so on. By using these mobile devices, users can perform transactions such as purchasing goods, making bank transaction and paying for tickets and coupons. It may bring flexibility and convenience for communication, storing and sharing data, social media networks and entertainments. On the other side, criminals may commit the crimes by using Android devices. Therefore, the investigators need to prepare enough Android forensics tools for investigation and analysis. In this paper, we proposed the applicable tool (ANDROSICS) for Android forensics which emphasizes on evidence data collection process. This tool can support Live Forensics, Dead Forensics, Data Collection, Bypass Screen Lock, Imaging, Android Debug Bridge Utility, Virtual Keys and many forensic features.


title: Detection of distributed denial of service attacks using machine learning algorithms in software defined networks

abstract: Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of SDN can sometimes also be a major security threat. If the intruder succeeds in attacking the central controller, he would get access to the entire system. The controller is highly vulnerable to Distributed Denial of Service (DDoS) attacks which lead to exhaustion of the system resources which causes non-availability of the services given by the controller. It is critical to detect the attacks in the controller at earlier stage. Many algorithms and techniques have been discovered for this purpose. But less work has been done in the field of SDN networks. Using machine learning algorithms for classifying the connections into legitimate and illegitimate is one such solution. We use two machine learning algorithms namely, the Support Vector Machine (SVM) classifier and the Neural Network (NN) classifier to detect the suspicious and harmful connections.


title: Riderless bicycle with gyroscopic balancer controlled by FSMC and AFSMC

abstract: A Riderless bicycle has been developed with a gyroscopic balancer controller by a Fuzzy Sliding Mode Controller (FSMC) and an Adaptive Fuzzy Sliding Mode Controller (AFSMC). The FSMC controller was first implemented because it has better performance on controlling nonlinear systems than the one with PID control. The FSMC can also reduce the chattering phenomenon caused by SMC and the effect of linearizing a nonlinear system. Compared with other balancers, the gyroscopic balancer has a couple of advantages, such as faster system response, lower mass ratio of balancer to bicycle and relatively larger moment. To demonstrate the attributes stated above, we designed and conducted experiments, including the balancing of unmoving bicycle, unmoving bicycle with external impacts, as well as the bicycle moving forward and turning. The experimental results show that the bicycle can overcome jolts, uneven terrain and external disturbances. Furthermore, since the results of experiments are consistent with the ones of the simulation, it validates the derived bicycle dynamics model with the gyroscopic balancer and proves its robustness. However, the system's ability to resist continuous disturbance is not strong enough because of the limitation on the tilt angle of the gyroscopic balancer. Hence, we modified the control strategy by using AFSMC despite the fact that the FSMC performed better than PID control. From the simulations in Section IV, it shows that the AFSMC has better performance at resisting continuous disturbances than FSMC does. Furthermore, the abilities to balance the unmoving bicycle or moving bicycle are no less than FSMC. Thus, the AFSMC is employed to replace the FSMC. The designs of adaptive law and estimation law of AFSMC are based on the Lyapunov function to ensure the stability of the system. Experiments of the bicycle controlled by AFSMC are currently being conducted.


Phenological phases and phenological species groups of mesic beech forests and their suitability for climatological monitoring.


title: Inductive matrix completion for predicting gene–disease associations

abstract: MOTIVATION ::: Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. ::: ::: ::: RESULTS ::: Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature. ::: ::: ::: AVAILABILITY ::: Source code and datasets can be downloaded from http://bigdata.ices.utexas.edu/project/gene-disease.


title: Layer-Based Approach for Image Pair Fusion

abstract: Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.


title: The Publication Incidence of Replications and Critical Commentary in Economics

abstract: Replication is not common in the economics literature. No replications were found in a sampling of 1,698 papers from three major economics journals. Only 5.4 percent were replications with extensions, and they accounted for only four percent of journal space devoted to research reports. The replications with extensions generally produced results that conflicted with the original works. Of the 92 extensions published, 65.2 percent conflicted with the earlier results, and only 20 percent provided full confirmation.


title: A hybrid packet-circuit switched on-chip network based on SDM

abstract: In this paper, we propose a novel on-chip communication scheme by dividing the resources of a traditional packet-switched network-on-chip between a packet-switched and a circuit-switched sub-network. The former directs packets according to the traditional packet-switching mechanism, while the latter forwards packets over circuits which are directly established between two non-adjacent nodes by bypassing the intermediate routers. A packet may switch between the sub-networks several times to reach its destination. The circuits are set up using a low-latency and low-cost setup-network. The network resources are split between the two sub-networks using Spatial-Division Multiplexing (SDM). The work aims to improve the power and performance metrics of Network-on-Chip (NoC) architectures and benefits from the power and scalability advantage of packet-switched NoCs and superior communication performance of circuit-switching. The evaluation results show a significant reduction in power and latency over a traditional packet-switched NoC.


title: How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation

abstract: With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.


title: Evidence for a direct effect of temperature and thyroid hormone on myelinogenesis in vitro

abstract: The effect of thyroxine on myelinogenesis in vitro of cerebella obtained from newborn rats and mice was tested. Cerebella were explanted and maintained in tissue culture under standard conditions described by Bornstein and Murray (1958) and Bunge et al. (1962). Thyroxine was added to the culture medium in concentrations of 1.5–3 μg per milliliter of feed. Cultures were maintained at 36°C, 32°C, and 30°C, respectively. ::: ::: The most consistent effect observed was acceleration of myelinogenesis in the presence of thyroxine in cultures grown at optimum temperatures. In cultures maintained at reduced temperatures, the differentiation of cells into neurons and glial elements was delayed, but eventually took place. Myelin formation was, however, almost completely suppressed. The presence of thyroxine in cultures grown at low temperatures increased the incidence of myelinogenesis, but the amount formed was smaller than that obtained in control cultures grown at optimal temperature. Shifting cultures from low to optimum temperature resulted in quick recovery of myelinogenesis. Maintenance of myelin in fully differentiated cultures was severely inhibited in thyroxine-supplemented medium, that was not renewed several times during a 12-day period. ::: ::: These observations are interpreted as suggesting that one of the stimuli for myelinogenesis may be a metabolic signal, mediated probably via enzymes sensitive to thyroid hormone for their rate of synthesis.


title: Matlab to C compilation targeting Application Specific Instruction Set Processors

abstract: This paper discusses a MATLAB to C compiler exploiting custom instructions such as instructions for SIMD processing and instructions for complex arithmetic present in Application Specific Instruction Set Processors (ASIPs). The compiler generates ANSI C code in which the processor's special instructions are represented via specialized intrinsic functions. By doing this the generated code can be used as input to any C/C++ compiler. Thus the proposed compiler allows the description of the specialized instruction set of the target processor in a parameterized way allowing the support of any processor. The proposed compiler has been used for the generation of application code for an ASIP targeting DSP applications. The code generated by the proposed compiler achieves a speed up between 2x–30x on the targeted ASIP for six DSP benchmarks compared to the code generated by Mathworks MATLAB to C compiler. Thus the proposed compiler can be employed to reduce the development time/effort/cost and time to market by raising the abstraction of application design in an embedded systems / system-on-chip development context while still improving implementation efficiency.


title: Real Time Simple Face-Tracking Algorithm based on Minimum Facial Features

abstract: This paper presents an algorithm to detect the human face from a video file using the knowledge-based approach and track the motion of the individual by detecting face in each frame. The algorithm detect human face by the geometric correlations between location of face and hairs in each frame of a video file. Range of skin color are used to figure out possible face regions so as to initially localize the face, furthermore, the probable hair blocks in an image frame are determined by means of hair color spectrums. Combined skin and hair blocks decide candidate face areas in light of the geometric relation. The phase correlation motion estimation algorithm is used to analyzing successive frames in a video sequence to identify faces that are in motion and track the human faces from the video file.. The accuracy of single-face tracking is higher than 90% with the frame rate of 10fps. Several algorithms have already been proposed and developed for various applications and employed successfully. But, those algorithms are quite complicated and hard to meet the realtime requirements of specific frame-rate. Therefore, the proposed is able to be expectedly transplanted to an embedded system, like the developing pet robot so as to perform dynamic face detection and tracking. The algorithm can be used for surveillance. The algorithm can be used for developing secure PC camera and web camera. The algorithm is being used for providing laptop security. General Terms Image Processing, algorithms.


title: Gender Differences in Food Choice: Effects of Superior Temporal Sulcus Stimulation

abstract: The easy availability of food has caused a shift from eating for survival to hedonic eating. Women, compared to men, have shown to respond differently to food cues in the environment on a behavioral and a neural level, in particular to energy rich (compared to low energy) foods. It has been demonstrated that the right posterior superior temporal sulcus (STS) is the only region exhibiting greater activation for high versus low calorie food choices. In order to test for a possible causal role of STS in food choice, we applied high frequency transcranial random noise stimulation (tRNS) on STS assuming a different response pattern between males and females. Our participants (18 females, 17 males) performed a forced choice task between food pairs matched for individual liking but differed in calorie, during the left STS, right STS stimulation and sham condition. Male participants showed a general preference for low calorie foods compared to females. In addition, we observed in males, but not in females, an increase of high calorie food choice during right STS tRNS compared to sham condition and left STS tRNS. Finally, we found an increase of missed choices during right STS stimulation compared to sham condition and left STS stimulation. In conclusion, thanks to tRNS evidence, we both confirm the involvement and suggest a causal role of right posterior STS in feeding behavior. Moreover, we suggest that gender differences exist in STS mechanisms underlying food choice.


title: Dissociation in performance of children with ADHD and high-functioning autism on a task of sustained attention

abstract: Attention deficit hyperactivity disorder (ADHD) and autism are two neurodevelopmental disorders associated with prominent executive dysfunction, which may be underpinned by disruption within fronto-striatal and fronto-parietal circuits. We probed executive function in these disorders using a sustained attention task with a validated brain-behaviour basis. Twenty-three children with ADHD, 21 children with high-functioning autism (HFA) and 18 control children were tested on the Sustained Attention to Response Task (SART). In a fixed sequence version of the task, children were required to withhold their response to a predictably occurring no-go target (3) in a 1–9 digit sequence; in the random version the sequence was unpredictable. The ADHD group showed clear deficits in response inhibition and sustained attention, through higher errors of commission and omission on both SART versions. The HFA group showed no sustained attention deficits, through a normal number of omission errors on both SART versions. The HFA group showed dissociation in response inhibition performance, as indexed by commission errors. On the Fixed SART, a normal number of errors was made, however when the stimuli were randomised, the HFA group made as many commission errors as the ADHD group. Greater slow-frequency variability in response time and a slowing in mean response time by the ADHD group suggested impaired arousal processes. The ADHD group showed greater fast-frequency variability in response time, indicative of impaired top-down control, relative to the HFA and control groups. These data imply involvement of fronto-parietal attentional networks and sub-cortical arousal systems in the pathology of ADHD and prefrontal cortex dysfunction in children with HFA.


title: Induction Motor Rotor Bar Damage Evaluation With Magnetic Field Analysis

abstract: Detection of defects in induction machine rotor bars for unassembled motors is required to evaluate machines considered for repair as well as fulfilling incremental quality assurance checks in the manufacture of new machines. Detection of rotor bar defects prior to motor assembly are critical in increasing repair efficiency and assuring the quality of newly manufactured machines. Many methods of detecting rotor bar defects in unassembled motors lack the sensitivity to find both major and minor defects in both cast and fabricated rotors along with additional deficiencies in quantifiable test results and arc-flash safety hazards. A process of direct magnetic field analysis can examine measurements from induced currents in a rotor separated from its stator yielding a high-resolution fingerprint of a rotor's magnetic field. This process identifies both major and minor rotor bar defects in a repeatable and quantifiable manner appropriate for numerical evaluation without arc-flash safety hazards.


title: DC Arc Model Based on 3-D DC Arc Simulation

abstract: DC arc flash hazard assessment is a mounting concern with the recent development of large-scale photovoltaic arrays and dc buses. Currently, there are few methods available to model dc arcs and are largely based on historical arc tests, which may not be suitable for modern dc applications. In order to provide a suitable method to predict dc arc flash properties in power systems, new dc arc model development is necessary. Historically, model development was constrained by time consuming and limited laboratory arc flash tests. This paper proposes a new dc arc model based on 3-D dc arc simulation that is capable of providing comparable results to available lab testing.


title: A clickstream-based collaborative filtering personalization model: towards a better performance

abstract: In recent years, clickstream-based Web personalization models for collaborative filtering recommendation have received much attention mainly due to their scalability [10,16,19]. The common personalization models are the Markov model, (sequential) association rule, and clustering. These models have shown strengths and weaknesses in their performance: for instance, the Markov model has higher precision and lower recall than (sequential) association rule and clustering, and vice versa [22]. In order to address the trade-off relationship of precision and recall, some study has combined two or more different models [22] or applied multi-order models [24,27]. The performance increases by these models, however, are at best marginal and still there is room for improving the performance because of their first order (one model type) application in making recommendation. We propose a new hybrid model for improving the performance, especially recall. The proposed hybrid model applies four prediction models - the Markov model, sequential association rule, association rule, and a default model [1,17] - in tandem in their precision order. We evaluated our model with Web usage data, and the result is promising.


title: BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING

abstract: Image processing is an active research area in whic h medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor i s a serious life altering disease condition. Image segmentation plays a signi ficant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this paper we have prop osed segmentation of brain MRI image using K-means clustering algorithm followed by morphological filtering which avoids th e misclustered regions that can inevitably be forme d after segmentation of the brain MRI image for detection of tumor location.


title: High resolution automotive radar data clustering with novel cluster method

abstract: Clustering of measurement data is an important task in digital signal processing. Especially in the case of radar signal processing the need of clustering detection points becomes obvious when high-resolution radar sensor systems are used. Clustering is usually used as a preprocessing step for classification of the measured data. In this paper a new approach for automotive radar data clustering is presented. A shape finding technique from image signal processing, called border following, is used to perform this task. Some adjustments and modifications of the method are required to get it working with radar measurements. The adapted algorithm is proven in three different measurement spaces and rated for the best performance by focusing on clustering of cyclists. It is showed, that the technique produces clustered radar data appropriate to their physical appearance.


title: Blockchain platforms: A compendium

abstract: In recent years, cryptocurrencies gained popularity with Bitcoin. The main promising technology behind Bitcoin was ‘Blockchain’. Blockchain provided unique features like transactional privacy, system transparency, immutability of data, security with cryptography, etc. These features paved way for Blockchain in advancing many technologies like voting systems, IOT applications, supply chain management, banking, healthcare, insurance, etc. Blockchain development was boosted with the increasing demand of the technological update. Many blockchain platforms are available like hyperledger fabric, ethereum, corda, etc. We always end up with perplexity while choosing a platform for blockchain development. Through our survey, we provide a comparative analysis of all the hyperledger platforms, ethereum, corda to make a choice of the platform easily according to the requirement.


title: Heuristic algorithms for the three-dimensional bin packing problem

abstract: Abstract The Three-Dimensional Bin Packing Problem (3BP) consists of allocating, without overlapping, a given set of three-dimensional rectangular items to the minimum number of three-dimensional identical finite bins. The problem is NP-hard in the strong sense, and finds many industrial applications. We introduce a Tabu Search framework exploiting a new constructive heuristic for the evaluation of the neighborhood. Extensive computational results on standard benchmark instances show the effectiveness of the approach with respect to exact and heuristic algorithms from the literature.


title: 'Online recognition of Chinese characters: the state-of-the-art

abstract: Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.


title: Latent semantic analysis

abstract: To construct a tunnel in a formation (11, 15) with zones of high hydraulic pressure (15), a gallery (13) is driven from the stable formation (11) toward the problem formation zone (15). At least one lock zone (19) with pressure-tight gates (23, 23') is constructed under protection of the stable formation (11) at some distance from the problem formation zone (15). Behind this lock zone (19) the gallery (13) is now filled with water (25) and pressurized. Further driving into the problem formation zone (15) and therethrough is now performed under water (25) having a pressure corresponding to the hydraulic pressure of the problem formation zone (15). For this purpose the machines (47, 47', 73) are remote-controlled and are controlled and monitored from the atmospheric zone (17). Material transportation operations are managed substantially via the lock zone (19). The gallery (13) is stabilized against the static rock pressure by a first tunnel lining (71) and sealed against the hydrostatic pressure by a watertight internal ring installed inside the first tunnel lining.


title: Self-Attentive Residual Decoder for Neural Machine Translation.

abstract: Neural sequence-to-sequence networks with attention have achieved remarkable performance for machine translation. One of the reasons for their effectiveness is their ability to capture relevant source-side contextual information at each time-step prediction through an attention mechanism. However, the target-side context is solely based on the sequence model which, in practice, is prone to a recency bias and lacks the ability to capture effectively non-sequential dependencies among words. To address this limitation, we propose a target-side-attentive residual recurrent network for decoding, where attention over previous words contributes directly to the prediction of the next word. The residual learning facilitates the flow of information from the distant past and is able to emphasize any of the previously translated words, hence it gains access to a wider context. The proposed model outperforms a neural MT baseline as well as a memory and self-attention network on three language pairs. The analysis of the attention learned by the decoder confirms that it emphasizes a wider context, and that it captures syntactic-like structures.


title: Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

abstract: Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness external knowledge to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-and-graph based models; and discuss the implications of using external knowledge to solve the NLI problem. Our model achieves close to state-of-the-art performance for NLI on the SciTail science questions dataset.


Occupiers of Saratoga Avenue v City of Johannesurg Metropolitan Municipality and Another


title: Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

abstract: This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse - but still acceptable - performance when compared to the single language model, while benefiting from better generalization properties across languages.


title: Brook trout use of thermal refugia and foraging habitat influenced by brown trout

abstract: The distribution of native brook trout (Salvelinus fontinalis) in eastern North America is often limited by temperature and introduced brown trout (Salmo trutta), the relative importance of which is poorly understood but critical for conservation and restoration planning. We evaluated effects of brown trout on brook trout behavior and habitat use in experimental streams across increasing temperatures (14–23 °C) with simulated groundwater upwelling zones providing thermal refugia (6–9 °C below ambient temperatures). Allopatric and sympatric trout populations increased their use of upwelling zones as ambient temperatures increased, demonstrating the importance of groundwater as thermal refugia in warming streams. Allopatric brook trout showed greater movement rates and more even spatial distributions within streams than sympatric brook trout, suggesting interference competition by brown trout for access to forage habitats located outside thermal refugia. Our results indicate that removal of introduced brown...


title: Railway Fastener Inspection by Real-Time Machine Vision

abstract: In this paper, a real-time railway fastener detection system using a high-speed laser range finder camera is presented. First, an extensive analysis of various methods based on pixel-wise and histogram similarities are conducted on a specific railway route. Then, a fusing stage is introduced which combines least correlated approaches also considering the performance upgrade after fusing. Then, the resulting method is tested on a larger database collected from a different railway route. After observing repeated successes, the method is implemented on NI LabVIEW and run real-time with a high-speed 3-D camera placed under a railway carriage designed for railway quality inspection.


title: Intelligent Development Environment and Software Knowledge Graph

abstract: Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts — intelligent development environment (IntelliDE) and software knowledge graph — for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.


title: The Need for Effective Information Security Awareness

abstract: Security awareness is an often-overlooked factor in an information security program. While organizations expand their use of advanced security technology and continuously train their security professionals, very little is used to increase the security awareness among the normal users, making them the weakest link in any organization. As a result, today, organized cyber criminals are putting significant efforts to research and develop advanced hacking methods that can be used to steal money and information from the general public. Furthermore, the high internet penetration growth rate in the Middle East and the limited security awareness among users is making it an attractive target for cyber criminals. In this paper, we will show the need for security awareness programs in schools, universities, governments, and private organizations in the Middle East by presenting results of several security awareness studies conducted among students and professionals in UAE in 2010. This includes a comprehensive wireless security survey in which thousands of access points were detected in Dubai and Sharjah most of which are either unprotected or employ weak types of protection. Another study focuses on evaluating the chances of general users to fall victims to phishing attacks which can be used to steal bank and personal information. Furthermore, a study of the user’s awareness of privacy issues when using RFID technology is presented. Finally, we discuss several key factors that are necessary to develop a successful information security awareness program.


title: Addictive Use Due to Personality: Focused on Big Five Personality Traits and Game Addiction

abstract: Authors : Eui Jun Jeong, Hye Rim Lee, Ji Hye Yoo Abstract : Recent studies have verified the significant relationship of user personality with Internet use. However, in game studies, little research has emphasized on the effects of personality traits on game addiction. This study examined whether big five personality traits affect game addiction with control of psychological, social, and demographic factors. Specifically, using data from a survey of 789 game users in Korea, we conducted a regression analysis to see the associations of psychological (loneliness/depression), social (activities with family/friends), self-efficacy (game/general), gaming (daily gaming time/perception), demographic (age/gender), and personality traits (extraversion, neuroticism conscientiousness, agreeableness, & openness) with the degree of game addiction. Results showed that neuroticism increase game addiction with no effect of extraversion on the addiction. General self-efficacy negatively affected game addiction, whereas game self-efficacy increased the degree of game addiction. Loneliness enhanced game addiction while depression showed a negative effect on the addiction. Results and implications are discussed.


title: A Motion Recognition Method Using Foot Pressure Sensors

abstract: This paper proposes a method for recognizing postures and gestures using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles are the best for motion recognition. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for a user, the recognition accuracy was about 93.6% on average. Although individual differences were seen, the best combinations of areas for each subject were largely divided into two major patterns. When two points were chosen, combinations of the near thenar, which is located near the thumb ball, and near the heel or point of the outside of the middle of the foot were highly recognized. Of the best two points, one was commonly the near thenar for subjects. By taking three points of data and covering these two combinations, it will be possible to cope with individual differences. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.0% on average. On the basis of these results, two types of pressure sensing shoes were developed.


title: A Full Body Steerable Wind Display for a Locomotion Interface

abstract: This paper presents the Treadport Active Wind Tunnel (TPAWT)-a full-body immersive virtual environment for the Treadport locomotion interface designed for generating wind on a user from any frontal direction at speeds up to 20 kph. The goal is to simulate the experience of realistic wind while walking in an outdoor virtual environment. A recirculating-type wind tunnel was created around the pre-existing Treadport installation by adding a large fan, ducting, and enclosure walls. Two sheets of air in a non-intrusive design flow along the side screens of the back-projection CAVE-like visual display, where they impinge and mix at the front screen to redirect towards the user in a full-body cross-section. By varying the flow conditions of the air sheets, the direction and speed of wind at the user are controlled. Design challenges to fit the wind tunnel in the pre-existing facility, and to manage turbulence to achieve stable and steerable flow, were overcome. The controller performance for wind speed and direction is demonstrated experimentally.


IOS Press Augmented Reality Applied to Ultrasound-Guided Breast Cyst Aspiration


title: Organisation and delivery of liaison psychiatry services in general hospitals in England: results of a national survey

abstract: Objectives To describe the current provision of hospital-based liaison psychiatry services in England, and to determine different models of liaison service that are currently operating in England. Design Cross-sectional observational study comprising an electronic survey followed by targeted telephone interviews. Setting All 179 acute hospitals with an emergency department in England. Participants 168 hospitals that had a liaison psychiatry service completed an electronic survey. Telephone interviews were conducted for 57 hospitals that reported specialist liaison services additional to provision for acute care. Measures Data included the location, service structures and staffing, working practices, relations with other mental health service providers, policies such as response times and funding. Model 2-based clustering was used to characterise the services. Telephone interviews identified the range of additional liaison psychiatry services provided. Results Most hospitals (141, 79%) reported a 7-day service responding to acute referrals from the emergency department and wards. However, under half of hospitals had 24 hours access to the service (78, 44%). One-third of hospitals (57, 32%) provided non-acute liaison work including outpatient clinics and links to specialist hospital services. 156 hospitals (87%) had a multidisciplinary service including a psychiatrist and mental health nurses. We derived a four-cluster model of liaison psychiatry using variables resulting from the electronic survey; the salient features of clusters were staffing numbers, especially nursing; provision of rapid response 24 hours 7-day acute services; offering outpatient and other non-acute work, and containing age-specific teams for older adults. Conclusions This is the most comprehensive study to date of liaison psychiatry in England and demonstrates the wide availability of such services nationally. Although all services provide an acute assessment function, there is no uniformity about hours of coverage or expectation of response times. Most services were better characterised by the model we developed than by current classification systems for liaison psychiatry.


title: A Review of Image Processing and Deep Learning Based Methods for Automated Analysis of Digital Retinal Fundus Images

abstract: Retinal fundus imaging is a medical procedure used by medical professionals in the discovery and tracking of various retinal abnormalities. Sometimes the analysis of retinal fundus images can be slow and difficult when performed by medical staff, and in response to this many automated, image-processing based methods for the analysis of these images exist. In recent years, deep learning methods have become increasingly popular in machine learning applications, so it is no surprise that they are also being used in the image processing based analysis of retinal fundus images. In this paper we discuss recently proposed methods that use deep learning techniques in the image processing based analysis of digital retinal fundus images. Special attention is given to the analysis of retinal fundus image datasets and various techniques employed to the images from these datasets in order to make them suitable for deep learning based applications.


title: Spatial Databases: A Tour

abstract: 1. Introduction to Spatial Databases. 2. Spatial Concepts and Data Models. 3. Spatial Query Language. 4. Spatial Storage and Indexing. 5. Query Processing and Optimization. 6. Spatial Networks. 7. Introduction to Spatial Data Mining. 8. Trends in Spatial Databases.


title: Comprehensive Approaches to User Acceptance of Internet of Things in a Smart Home Environment

abstract: With rapid improvements in communication technologies and infrastructure, the Internet of Things (IoT) has become a promising sector within the global information and communication technology industry. Various fields are employing the concept of IoT for their traditional products and services for convenient use by consumers. In the construction industry, the majority of leading companies and organizations are using IoT technologies in a smart home environment. However, only few studies have focused on the user experience of IoT technologies in such an environment. Thus, the current study explores the key determinants of user acceptance of IoT technologies in a smart home environment, and investigates a research model integrated with five potential user factors and a technology acceptance model. The results of the collected data, which were investigated using a structural equation modeling method, show that three positive motivations, compatibility, connectedness, and control, and a negative hindrance, cost, are significant determinants of the technology acceptance behavior of users. The current study can serve as a foundation for future studies on improving IoT technologies in a smart home environment by considering the user experience.


title: Review of Modular Implementation in a University

abstract: While the pros and cons of modularisation of British degree programmes are often debated, there are few appraisals of large structures following implementation. This paper reports on a substantial review during 1994 of the effectiveness, judged against its aims and incidental effects, of the modular framework at one large and dispersed British university. This was undertaken by a small project team using a questionnaire survey and focus group discussions with staff and students, 18 months after the major implementation. ::: ::: ::: ::: The results confirm the practicality of the basic framework, but indicate that the stated purposes have been achieved to varying extents. For example, flexibility for students has been increased, although not yet in practice to the extent intended; new inter-disciplinary degrees have been catalysed. The problems mainly concern the cohesion of student experience and the supporting infrastructure: student records, management information, timetabling and room allocations, educational guidance, module registration. There have also been major problems with the load and timing of assessments. ::: ::: ::: ::: The Review culminated in proposals from the project team, accepted by the Academic Council, for fine-tuning rather than for radical change. Some needs for longer term consideration were also identified.


title: New miniaturized dual-band rat-race coupler with microwave C-sections

abstract: Based on microwave C-sections, rat-race coupler is designed to have a dual-band characteristic and a miniaturized area. The C-section together with two transmission line sections attached to both of its ends is synthesized to realize a phase change of 90° at the first frequency, and 270° at the second passband. The equivalence is established by the transmission line theory, and transcendental equations are derived to determine its structure parameters. Two circuits are realized in this presentation; one is designed at 2.45/5.2 GHz and the other at 2.45/5.8 GHz. The latter circuit occupies only 31% of the area of a conventional hybrid ring at the first band. It is believed that this circuit has the best size reduction for microstrip dual-band rat-race couplers in open literature. The measured results show good agreement with simulation responses.


title: Programming with Actors in Java 8

abstract: There exist numerous languages and frameworks that support an implementation of a variety of actor-based programming models in Java using concurrency utilities and threads. Java 8 is released with fundamental new features: lambda expressions and further dynamic invocation support. We show in this paper that such features in Java 8 allow for a high-level actor-based methodology for programming distributed systems which supports the programming to interfaces discipline. The embedding of our actor-based Java API is shallow in the sense that it abstracts from the actual thread-based deployment models. We further discuss different concurrent execution and thread-based deployment models and an extension of the API for its actual parallel and distributed implementation. We present briefly the results of a set of experiments which provide evidence of the potential impact of lambda expressions in Java 8 regarding the adoption of the actor concurrency model in large-scale distributed applications.


title: Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis

abstract: Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior.


title: Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement

abstract: Segmentation of cervical cells in microscopic images is an important task for computer-aided diagnosis of cervical cancer. However, their segmentation is challenging due to inhomogeneous cell cytoplasm and the overlap between the cells. In this paper, we propose an automatic segmentation method for multiple overlapping cervical cells in microscopic images using superpixel partitioning and cell-wise contour refinement. First, the cell masses are detected by superpixel generation and triangle thresholding. Then, nuclei of cells are extracted by local thresholding and outlier removal. Finally, cell cytoplasm is initially segmented by superpixel partitioning and refined by cell-wise contour refinement with graph cuts. In experiments, our method showed competitive performances in two public challenge data sets compared to the state-of-the-art methods.


title: Mining e-contract documents to classify clauses

abstract: E-contracts begin as legal documents and end up as processes that help organizations abide by legal rules while fulfilling contract terms. As contracts are complex, their deployment is predominantly established and fulfilled with significant human involvement. One of the key difficulties with any kind of contract processing is the legal ambiguity, which makes it difficult to address any violation of the contract terms. Thus, there is a need to track clauses for the contract activities under execution and violation of clauses. This necessitates deriving clause patterns from e-contract documents and map to their respective activities for further monitoring and fulfillment of e-contracts during their enactment. In this paper, we present a classification approach to extract clause patterns from e-contract documents. This is a challenging task as activities and clauses are mostly derived from both legal and business process driven contract knowledge.


title: Shallow and Deep Convolutional Networks for Saliency Prediction

abstract: The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.


title: On the Permanence of Stored Information in the

abstract: Many people believe that information that is stored in long-term memory is permanent, citing examples of "retrieval techniques" that are alleged to uncover previously forgotten information. Such tech- niques include hypnosis, psychoanalytic procedures, methods for eliciting spontaneous and other conscious recoveries, and—perhaps most important—the electri- cal stimulation of the brain reported by Wilder Pen- field and his associates. In this article we first evaluate • the evidence and conclude that, contrary to apparent popular belief, the evidence in no way confirms the view that all memories are permanent and thus poten- tially recoverable. We then describe some failures that resulted from attempts to elicit retrieval of pre- viously stored information and conjecture what cir- cumstances might cause information stored in memory to be irrevocably destroyed. Few would deny the existence of a phenomenon called "forgetting," which is evident in the com- mon observation that information becomes less available as the interval increases between the time of the information's initial acquisition and the time of its attempted retrieval. Despite the prevalence of the phenomenon, the factors that underlie forgetting have proved to be rather elusive, and the literature abounds with hypothesized mechanisms to account for the ob- served data. In this article we shall focus our attention on what is perhaps the fundamental issue concerning forgetting; Does forgetting consist of an actual loss of stored information, or does it result from a loss of access to information, which, once stored, remains forever? It should be noted at the outset that this ques- tion may be impossible to resolve in an absolute sense. Consider the following thought experiment. A person (call him Geoffrey) observes some event, say a traffic accident. During the period of ob- servation, a movie camera strapped to Geoffrey's head records the event as Geoffrey experiences it. Some time later, Geoffrey attempts to recall and


Automated Bitcoin Trading via Machine Learning Algorithms


Revealing Social Identity Phenomena in Videogames with Archetypal Analysis


title: A wideband substrate integrated waveguide slotted array antenna with multimode and multidirectional characteristics

abstract: This paper presents the design of a substrate integrated waveguide slotted array antenna (SIW-SAA) with wideband, multimode and multidirectional characteristics. The slotted array consists of three transverse slots in the top wall of SIW which are inclined at 45° with respect to a single longitudinal slot. The number of slots and the position of the slotted array determine the impedance bandwidth. It is found from the simulated and measured results that the impedance bandwidth of the antenna is 92% (10 GHz–27 GHz) for S 11 ≤ −10 dB. Multiple modes get excited in the SIW-SAA resulting in multidirectional characteristics and an equivalent circuit model is used to explain the physics of this phenomenon. In the X band, the lowest mode (TE 10 ) of SIW exists and the combination of slotted array and SIW is responsible for unidirectional radiation. Bidirectional and omnidirectional patterns are observed in the elevation plane for Ku and K bands, respectively due to higher order modes that are excited in the bottom wall of SIW. With these multidirectional characteristics, the proposed antenna is an attractive choice in airborne control systems, long distance communications and broadcasting.


title: Recent progress in semantic image segmentation

abstract: Semantic image segmentation, which becomes one of the key applications in image processing and computer vision domain, has been used in multiple domains such as medical area and intelligent transportation. Lots of benchmark datasets are released for researchers to verify their algorithms. Semantic segmentation has been studied for many years. Since the emergence of Deep Neural Network (DNN), segmentation has made a tremendous progress. In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method. Firstly, we briefly summarize the traditional method as well as datasets released for segmentation, then we comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, up-sample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, Multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods. Finally, a conclusion in this area is drawn.


title: Operator training simulators in virtual reality environment for process operators: a review

abstract: Given the factors such as safety, profitability, and environmental concerns at stake, operator training is an everlasting and vital process in the process industry. An inevitable need for skilled operators in the chemical industry leads to search for novel and effective training methodologies. Consequently, dynamic simulation techniques have been considered as a tool to educate and train inexperienced personnel as expected by the industry. Traditional training methodologies are hardly sufficient to instruct the operators for seldom-occurring perilous situations. Conventional operator training simulators (OTS) are generally effective, but they lack to give operators the actual feel of the scenarios. Training effectiveness can be enhanced by providing operators with a sense of realism. Therefore, integration of OTS with virtual reality (VR-OTS) certainly comes out to be an alternative. VR-OTS can replicate emergency conditions, accidents, and investigate safety protocols. In this work, we discuss the need for virtual reality (VR) in OTS, merits of VR-OTS, and the role of training assessment methods. Contributions of OTS Authors’ in process industry from year 2000 to mid-2017 are reviewed and discussed extensively. The review shows that VR-OTS provides tangible benefits over its conventional counterparts in terms of improved safety of plant, increased productivity, and environmental protection. Finally, this paper outlines future scopes that the current researcher may consider to focus for the increased and improved VR-OTS usage.


title: Valuing Water Rights in Douglas County, Oregon, Using the Hedonic Price Method

abstract: : This paper uses the hedonic price method to estimate the value of an acre-foot of irrigation water in Douglas County, Oregon. The analysis uses detailed information from 113 arms-length transactions of farmland for 2000 and 2001. The estimated willingness-to-accept of $261 to sell an acre-foot of irrigation water is consistent with other studies and recent transactions in the study area. Estimates for the value of leasing water are provided using a range of discount rates and leasing periods.


title: Dual-Band/Tri-Band/Broadband CPW-Fed Stepped-Impedance Slot Dipole Antennas

abstract: In this study, two multi-band antennas and one broadband antenna using slot stepped-impedance resonators (SIRs) and coplanar waveguide (CPW) feeding have been investigated. All of the proposed antennas are fabricated on 1-mm FR4 substrates with a relative dielectric constant of 4.4 and a loss tangent of 0.02. In comparison to the conventional uniform-impedance resonator (UIR), the SIR shows a better control over spurious response. In this way, a dual-band antenna (antenna A) and a tri-band antenna (antenna B) could be designed at desired bands. Antenna A is implemented with center frequencies (f0, f√) = (1.5 GHz, 3.5 GHz), and Antenna B is realized with center frequencies (f0, fS1, fS2) = (1.5 GHz, 3.5 GHz, 5.5 GHz), where f0 is the fundamental frequency. The broadband antenna (antenna C) is designed by allocating the first three resonant frequencies close enough to achieve a 98.7% fractional bandwidth with a return loss of greater than 6 dB from 1.5 GHz to 4.4 GHz. Furthermore, to tackle the null radiation problem at the broadside direction of antennas A and B, parasitic slots are added near the main radiating resonators so as to achieve nearly omni-directional radiation patterns at the high-order mode frequencies.


title: Analysis of Probability as an Aid in the Clinical Diagnosis of Coronary-Artery Disease

abstract: The diagnosis of coronary-artery disease has become increasingly complex. Many different results, obtained from tests with substantial imperfections, must be integrated into a diagnostic conclusion about the probability of disease in a given patient. To approach this problem in a practical manner, we reviewed the literature to estimate the pretest likelihood of disease (defined by age, sex and symptoms) and the sensitivity and specificity of four diagnostic tests: stress electrocardiography, cardiokymography, thallium scintigraphy and cardiac fluoroscopy. With this information, test results can be analyzed by use of Bayes' theorem of conditional probability. This approach has several advantages. It pools the diagnostic experience of many physicians ans integrates fundamental pretest clinical descriptors with many varying test results to summarize reproducibly and meaningfully the probability of angiographic coronary-artery disease. This approach also aids, but does not replace, the physician's judgment and may assit in decisions on cost effectiveness of tests.


title: DSCC2008-2193 ARTICULATED WHEELED ROBOTS: EXPLOITING RECONFIGURABILITY AND REDUNDANCY

abstract: Articulated Wheeled Robotic (AWR) locomotion systems consist of chassis connected to a set of wheels through articulated linkages. Such articulated “leg-wheel systems” facilitate reconfigurability that has significant applications in many arenas, but also engender constraints that make the design, analysis and control difficult. We will study this class of systems in the context of design, analysis and control of a novel planar reconfigurable omnidirectional wheeled mobile platform. We first extend a twist based modeling approach to this class of AWRs. Our systematic symbolic implementation allows for rapid formulation of kinematic models for the general class of AWR. Two kinematic control schemes are developed which coordinate the motion of the articulated legs and wheels and resolve redundancy. Simulation results are presented to validate the control algorithm that can move the robot from one configuration to another while following a reference path. The development of two generations of prototypes is also presented briefly.


title: A longitudinal analysis of sex differences in math and spatial skills in primary school age children B

abstract: We report on a longitudinal study designed to assess possible sex differences in math achievement, math ability, and math-related tasks during the primary school age years. Participants included over 200 children from one public school district. Annual assessments included measures of math ability, math calculation achievement scores, rapid naming and decoding tasks, visual perception tests, visual motor tasks, and reading skills. During select years of the study we also administered tests of counting and math facts skills. We examined whether girls or boys were overrepresented among the bottom or top performers on any of these tasks, relative to their peers, and whether growth rates or predictors of math-related skills differed for boys and girls. Our findings support the notion that sex differences in math are minimal or nonexistent on standardized psychometric tests routinely given in assessments of primary school age children. There was no persistent finding suggesting a male or female advantage in math performance overall, during any single year of the study, or in any one area of math or spatial skills. Growth rates for all skills, and early correlates of later math performance, were comparable for boys and girls. The findings fail to support either persistent or emerging sex differences on non-specialized math ability measures during the primary school age years.


title: PIBAS FedSPARQL: a web-based platform for integration and exploration of bioinformatics datasets

abstract: There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semantic technologies offer the ability to create links and mappings across datasets and manage them as a single, linked network so that searching can be carried out across datasets, independently of the source. We have developed an application called PIBAS FedSPARQL that uses semantic technologies to allow researchers to carry out such searching across a vast array of data sources. PIBAS FedSPARQL is a web-based query builder and result set visualizer of bioinformatics data. As an advanced feature, our system can detect similar data items identified by different Uniform Resource Identifiers (URIs), using a text-mining algorithm based on the processing of named entities to be used in Vector Space Model and Cosine Similarity Measures. According to our knowledge, PIBAS FedSPARQL was unique among the systems that we found in that it allows detecting of similar data items. As a query builder, our system allows researchers to intuitively construct and run Federated SPARQL queries across multiple data sources, including global initiatives, such as Bio2RDF, Chem2Bio2RDF, EMBL-EBI, and one local initiative called CPCTAS, as well as additional user-specified data source. From the input topic, subtopic, template and keyword, a corresponding initial Federated SPARQL query is created and executed. Based on the data obtained, end users have the ability to choose the most appropriate data sources in their area of interest and exploit their Resource Description Framework (RDF) structure, which allows users to select certain properties of data to enhance query results. The developed system is flexible and allows intuitive creation and execution of queries for an extensive range of bioinformatics topics. Also, the novel “similar data items detection” algorithm can be particularly useful for suggesting new data sources and cost optimization for new experiments. PIBAS FedSPARQL can be expanded with new topics, subtopics and templates on demand, rendering information retrieval more robust.


title: RelNN: A Deep Neural Model for Relational Learning

abstract: Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.


title: Pricing Tasks in Online Labor Markets

abstract: In this paper we present a mechanism for determining near-optimal prices for tasks in online labor markets, often used for crowdsourcing. In particular, the mechanisms are designed to handle the intricacies of markets like Mechanical Turk where workers arrive online and requesters have budget constraints. The mechanism is incentive compatible, budget feasible, and has competitive ratio performance and also performs well in practice. To demonstrate the mechanism's practical effectiveness we conducted experiments on the Mechanical Turk platform.


title: Dimension Reduction With Extreme Learning Machine

abstract: Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.


title: Community engagements with living sensing systems

abstract: We present the design and deployment of a bio-electronic sensing system. This system visualizes bacterial activity inside Winogradsky columns, which incubate soil samples to culture the naturally occurring microorganisms as they process metals and nutrients in the soil. Our month-long deployments with two urban communities offer insights into individual and collective appropriations of living sensing systems. These findings reveal future design trajectories that build on emergent themes in HumanComputer Interaction (HCI), including: new perspectives on materiality, which arise from integrating organic materials with the digital; a reframing of time, as systems shift from providing instant feedback to supporting prolonged engagement; and an emphasis on collective modes of participation beyond individual behavior change.


title: Semantic video annotation for accessible resources in flipped classrooms.

abstract: In this paper we present a project for applying a framework for accessibility to educational activities in flipped classrooms by exploiting semantic video annotation. The framework exploits semantic and adaptive technologies and is aimed to support accessibility to physical real-world things.


Building More Explainable Artificial Intelligence With Argumentation.


title: Stock Sequence Pattern Mining Method Based on SWI-GSP Algorithm

abstract: In the mining of stock data association, investors are more concerned with such association rules as X (t1)→Y (t2), That is, the X shares rise on t1, and the Y shares rise at a certain probability on day t2. As such association rules can't be directly used GSP algorithm, for this reason, the GSP algorithm is depth analyzed and improved, and the SWI-GSP (sliding window interval) algorithm is proposed. In the SWI-GSP algorithm, such association rule: X (t1)→Y (t2) (N= t2-t1) is implemented by adding the time interval parameter N (N= 0,1,2,3··· ···). The sliding time window W is designed, and the time interval N is counted in W, and the sliding window W moves along the time axis of the stock trading to realize the correlation counting in a transaction. Through the Chinese A-share data for the sample experiment, experiments show that, the SWI-GSP algorithm is better mining sequential pattern with time interval than GSP.


title: Development of 77 GHz Radar Lens Antennas for Automotive Applications Based on Given Requirements

abstract: Lens antennas are well suited for automotive radar sensors. The development of these kinds of antennas in the field of automotive radar applications is presented. Based on the given requirements for the associated radar senior the development of the antenna is shown. Therefore a fast and accurate antenna simulation tool based on geometric optics (GO) and a field of view simulator has been developed. The simulation chain from simulating the antenna elements to the radar field of view is presented.


title: Identification and real time control of an inverted pendulum using PI-PD controller

abstract: Inverted pendulum is one of the fundamental benchmarks in application areas of control engineering and robotics. It is a good example of single input multi output (SIMO) systems. In this paper, identification of a cart inverted pendulum system is carried out and real time control of the system is accomplished by using the PI-PD controller. PI-PD controller is a good approach to control integrating processes or unstable processes like inverted pendulum. In the study, PI-PD controllers are used to control both angular position of the pendulum and the cart position. Real time implementation results are given to illustrate the use of the PI-PD controller. Results are compared with PID controller and it is demonstrated that the PI-PD controller is much superior to a PID controller.


title: A critical assessment of connectivity measures for EEG data: a simulation study.

abstract: Abstract Information flow between brain areas is difficult to estimate from EEG measurements due to the presence of noise as well as due to volume conduction. We here test the ability of popular measures of effective connectivity to detect an underlying neuronal interaction from simulated EEG data, as well as the ability of commonly used inverse source reconstruction techniques to improve the connectivity estimation. We find that volume conduction severely limits the neurophysiological interpretability of sensor-space connectivity analyses. Moreover, it may generally lead to conflicting results depending on the connectivity measure and statistical testing approach used. In particular, we note that the application of Granger-causal (GC) measures combined with standard significance testing leads to the detection of spurious connectivity regardless of whether the analysis is performed on sensor-space data or on sources estimated using three different established inverse methods. This empirical result follows from the definition of GC. The phase-slope index (PSI) does not suffer from this theoretical limitation and therefore performs well on our simulated data. We develop a theoretical framework to characterize artifacts of volume conduction, which may still be present even in reconstructed source time series as zero-lag correlations, and to distinguish their time-delayed brain interaction. Based on this theory we derive a procedure which suppresses the influence of volume conduction, but preserves effects related to time-lagged brain interaction in connectivity estimates. This is achieved by using time-reversed data as surrogates for statistical testing. We demonstrate that this robustification makes Granger-causal connectivity measures applicable to EEG data, achieving similar results as PSI. Integrating the insights of our study, we provide a guidance for measuring brain interaction from EEG data. Software for generating benchmark data is made available.


title: The Obsessive-Compulsive Inventory: Development and validation of a short version.

abstract: Abstract This article reports on the development of a revised version of the Obsessive-Compulsive Inventory (OCI; E. B. Foa, M. J. Kozak, P. Salkovskis, M. E. Coles, & N. Amir, 1998), a psychometrically sound, theoretically driven, self-report measure. The revised OCI (OCI-R) improves on the parent version in 3 ways: It eliminates the redundant frequency scale, simplifies the scoring of the subscales, and reduces overlap across subscales. The reliability and validity of the OCI-R were examined in 215 patients with obsessive-compulsive disorder (OCD), 243 patients with other anxiety disorders, and 677 nonanxious individuals. The OCI-R, which contains 18 items and 6 subscales, has retained excellent psychometric properties. The OCI-R and its subscales differentiated well between individuals with and without OCD. Receiver operating characteristic (ROC) analyses demonstrated the usefulness of the OCI-R as a diagnostic tool for screening patients with OCD, utilizing empirically derived cutscores.


title: Interventions Shown to Aid Executive Function Development in Children 4 to 12 Years Old

abstract: To be successful takes creativity, flexibility, self-control, and discipline. Central to all those are ‘executive functions,’ including mentally playing with ideas, giving a considered rather than an impulsive response, and staying focused. Diverse activities have been shown to improve children’s executive functions – computerized training, non-computerized games, aerobics, martial arts, yoga, mindfulness, and school curricula. Central to all these is repeated practice and constantly challenging executive functions. Children with worse executive functions initially, benefit most; thus early executive-function training may avert widening achievement gaps later. To improve executive functions, focusing narrowly on them may not be as effective as also addressing emotional and social development (as do curricula that improve executive functions) and physical development (shown by positive effects of aerobics, martial arts, and yoga).