title: The ForSpec Temporal Logic: A New Temporal Property-Specification Language

abstract: In this paper we describe the ForSpec Temporal Logic (FTL), the new temporal property-specification logic of ForSpec, Intel's new formal specification language. The key features of FTL are as follows: it is a linear temporal logic, based on Pnueli's LTL, it is based on a rich set of logical and arithmetical operations on bit vectors to describe state properties, it enables the user to define temporal connectives over time windows, it enables the user to define regular events, which are regular sequences of Boolean events, and then relate such events via special connectives, it enables the user to express properties about the past, and it includes constructs that enable the user to model multiple clock and reset signals, which is useful in the verification of hardware design.


title: Comparing Game User Research Methodologies for the Improvement of Level Design in a 2-D Platformer

abstract: In this paper we compare the effects of using three game user research methodologies to assist in shaping levels for a 2-D platformer game, and illustrate how the use of such methodologies can help level designers to make more informed decisions in an otherwise qualitative oriented design process. Game user interviews, game metrics and psychophysiology (biometrics) were combined in pairs to gauge usefulness in small-scale commercial game development scenarios such as the casual game industry. Based on the recommendations made by the methods, three sample levels of a Super Mario clone were improved and the opinions of a second sample of users indicated the success of these changes. We conclude that user interviews provide the clearest indications for improvement among the considered methodologies while metrics and biometrics add different types of information that cannot be obtained otherwise.


title: Protein acetylation in metabolism — metabolites and cofactors

abstract: Reversible acetylation was initially described as an epigenetic mechanism regulating DNA accessibility. Since then, this process has emerged as a controller of histone and nonhistone acetylation that integrates key physiological processes such as metabolism, circadian rhythm and cell cycle, along with gene regulation in various organisms. The widespread and reversible nature of acetylation also revitalized interest in the mechanisms that regulate lysine acetyltransferases (KATs) and deacetylases (KDACs) in health and disease. Changes in protein or histone acetylation are especially relevant for many common diseases including obesity, diabetes mellitus, neurodegenerative diseases and cancer, as well as for some rare diseases such as mitochondrial diseases and lipodystrophies. In this Review, we examine the role of reversible acetylation in metabolic control and how changes in levels of metabolites or cofactors, including nicotinamide adenine dinucleotide, nicotinamide, coenzyme A, acetyl coenzyme A, zinc and butyrate and/or β-hydroxybutyrate, directly alter KAT or KDAC activity to link energy status to adaptive cellular and organismal homeostasis.


title: A Survey on Data Cleaning Methods in Cyberspace

abstract: Cyberspace offers users and information communication systems the opportunity to interact with each other for business. Data, as the carrier of information, represents the processing content of different business work. In order to improve the quality of data, data cleaning plays an important role in various cyberspace scenarios, such as RFID and sensor, ETL process etc. This paper presents a survey of the art-of-the-state data cleaning methods in cyberspace. According to the characteristics of data cleaning, we extract the relevant key elements of cyberspace to classify the existing works. After elaborating and analyzing each category, we summarize the description and challenges of data cleaning core technologies, such as data quality rules, models and crowdsourcing. Furthermore, we give suggestions for future research on data cleaning in cyberspace from both technology and interactive perspective.


title: UIML: an appliance-independent XML user interface language

abstract: Abstract Today's Internet appliances feature user interface technologies almost unknown a few years ago: touch screens, styli, handwriting and voice recognition, speech synthesis, tiny screens, and more. This richness creates problems. First, different appliances use different languages: WML for cell phones; SpeechML, JSML, and VoxML for voice enabled devices such as phones; HTML and XUL for desktop computers, and so on. Thus, developers must maintain multiple source code families to deploy interfaces to one information system on multiple appliances. Second, user interfaces differ dramatically in complexity (e.g, PC versus cell phone interfaces). Thus, developers must also manage interface content. Third, developers risk writing appliance-specific interfaces for an appliance that might not be on the market tomorrow. A solution is to build interfaces with a single, universal language free of assumptions about appliances and interface technology. This paper introduces such a language, the User Interface Markup Language (UIML), an XML-compliant language. UIML insulates the interface designer from the peculiarities of different appliances through style sheets. A measure of the power of UIML is that it can replace hand-coding of Java AWT or Swing user interfaces.


title: Scientific document summarization via citation contextualization and scientific discourse

abstract: The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific summarization addresses this challenge by providing summaries of the important contributions of scientific papers. We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure. Citation texts often lack the evidence and context to support the content of the cited paper and are even sometimes inaccurate. We first address the problem of inaccuracy of the citation texts by finding the relevant context from the cited paper. We propose three approaches for contextualizing citations which are based on query reformulation, word embeddings, and supervised learning. We then train a model to identify the discourse facets for each citation. We finally propose a method for summarizing scientific papers by leveraging the faceted citations and their corresponding contexts. We evaluate our proposed method on two scientific summarization datasets in the biomedical and computational linguistics domains. Extensive evaluation results show that our methods can improve over the state of the art by large margins.


Web-Based Text Mining of Hotel Customer Comments Using SAS ® Text Miner and Megaputer Polyanalyst ®


title: The Protégé OWL plugin: An open development environment for semantic web applications

abstract: We introduce the OWL Plugin, a Semantic Web extension of the Protege ontology development platform. The OWL Plugin can be used to edit ontologies in the Web Ontology Language (OWL), to access description logic reasoners, and to acquire instances for semantic markup. In many of these features, the OWL Plugin has created and facilitated new practices for building Semantic Web contents, often driven by the needs of and feedback from our users. Furthermore, Protege's flexible open-source platform means that it is easy to integrate custom-tailored components to build real-world applications. This document describes the architecture of the OWL Plugin, walks through its most important features, and discusses some of our design decisions.


title: Understanding the roles of self-esteem, self-compassion, and fear of self-compassion in eating disorder pathology: An examination of female students and eating disorder patients

abstract: Abstract The present study examined the relative contributions of self-compassion, fear of self-compassion, and self-esteem in eating disorder pathology. One-hundred and fifty-five female undergraduate students and 97 females entering eating disorder treatment completed the Self-Compassion Scale, Fears of Compassion Scale, Rosenberg Self-Esteem Inventory, and Eating Disorder Examination Questionnaire. T- tests revealed that the patient group had lower mean self-compassion and higher mean fear of self-compassion than the student group. When controlling for self-esteem, high fear of self-compassion emerged as the strongest predictor of eating disorder pathology in the patient group, whereas low self-compassion was the strongest predictor in the student group. These preliminary results suggest that targeting fear of self-compassion may be important when intervening with individuals suffering from an eating disorder, whereas building self-compassion may be a valuable approach for eating disorder prevention.


title: Visual event-related potentials in progressive supranuclear palsy.

abstract: This work was supported by grants-in-aid from the Ministry of Education, Science and Culture and from the Ministry of Health and Welfare of Japan.


title: Joint effects of emotion and color on memory

abstract: Numerous studies have shown that memory is enhanced for emotionally negative and positive information relative to neutral information. We examined whether emotion-induced memory enhancement is influenced by low-level perceptual attributes such as color. Because in everyday life red is often used as a warning signal, whereas green signals security, we hypothesized that red might enhance memory for negative information and green memory for positive information. To capture the signaling function of colors, we measured memory for words standing out from the context by color, and manipulated the color and emotional significance of the outstanding words. Making words outstanding by color strongly enhanced memory, replicating the well-known von Restorff effect. Furthermore, memory for colored words was further increased by emotional significance, replicating the memory-enhancing effect of emotion. Most intriguingly, the effects of emotion on memory additionally depended on color type. Red strongly increased memory for negative words, whereas green strongly increased memory for positive words. These findings provide the first evidence that emotion-induced memory enhancement is influenced by color and demonstrate that different colors can have different functions in human memory.


title: Reversible comparator design using quantum dot-cellular automata

abstract: ABSTRACTQuantum dot-cellular automata (QCA) emerge as a research area to design nanometre scale logic circuit. In digital logic design, a comparator is the fundamental building block that performs the comparison of two numbers. This paper deals with the design of reversible building block for 1-bit comparator and its implementation in QCA. An improved QCA layout of Feynman gate is also achieved. The QCA Feynman gate is denser and has low delay than the existing circuit. The proposed reversible comparator has quantum cost 9, whereas the QCA reversible comparator has the quantum cost 0.927. The quantum cost based comparison of the proposed QCA reversible comparator with conventional reversible comparator shows the cost effective circuit design in QCA. The simulation result matched the truth table of comparator which approves the functional capability of the proposed QCA layout of comparator. All the proposed layouts dissipate very low power.


title: Multi-Objective Search Results Clustering

abstract: Most web search results clustering (SRC) strategies have predominantly studied the definition of adapted representation spaces to the detriment of new clustering techniques to improve perfor-mance. In this paper, we define SRC as a multi-objective optimization (MOO) problem to take advantage of most recent works in clustering. In particular, we define two objective functions (compactness and separability), which are simultaneously optimized using a MOO-based simu-lated annealing technique called AMOSA. The proposed algorithm is able to automatically detect the number of clusters for any query and outperforms all state-of-the-art text-based solutions in terms of F β -measure and F b 3 -measure over two gold standard data sets.


title: Construction of a eukaryotic expression vector for pEGFP-FST and its biological activity in duck myoblasts

abstract: article i nfo Background: Follistatin (FST), a secreted glycoprotein, is intrinsically linked to muscle hypertrophy. To explore the function of duck FST in myoblast proliferation and differentiation, the pEGFP-FST eukaryotic expression vector was constructed and identified. The biological activities of this vector were analyzed by transfecting pEGFP-FST into cultured duck myoblasts using Lipofectamine™ 2000 and subsequently determining the mRNA expression profiles of FST and myostatin (MSTN). Results: The duck pEGFP-FST vector was successfully constructed and was confirmed to have high liposome-mediated transfection efficiency in duck myoblasts. Additionally, myoblasts transfected with pEGFP-FST had a higher biological activity. Significantly, the overexpression of FST in these cells significantly inhibited the mRNA expression of MSTN (a target gene that is negatively regulated by FST). Conclusions: The duck pEGFP-FST vector has been constructed successfully and exhibits biological activity by promoting myoblast proliferation and differentiation in vitro.


title: Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads

abstract: As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim.


title: Customer relationship management capabilities

abstract: Purpose – This study seeks to extend the resource‐based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities.Design/methodology/approach – Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling.Findings – A three‐factor (customer interaction management capability, customer relationship upgrading capability and customer win‐back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer‐centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on org...


title: A stopping criterion for active learning

abstract: Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.


title: Using Virtualization to Create and Deploy Computer Security Lab Exercises

abstract: Providing computer security laboratory exercises enables students to experience and understand the underlying concepts associated with computer security, but there are many impediments to the creation of realistic exercises of this type. Virtualization provides a mechanism for creating and deploying authentic computer security laboratory experiences for students while minimizing the associated configuration time and reducing the associated hardware requirements. This paper provides a justification for using virtualization to create and deploy computer security lab exercises by presenting and discussing examples of applied lab exercises that have been successfully used at two leading computer security programs. The application of virtualization mitigates many of the challenges encountered in using traditional computer laboratory environments for information assurance educational scenarios.


title: Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields

abstract: There is rich knowledge encoded in online web data. For example, punctuation and entity tags in Wikipedia data define some word boundaries in a sentence. In this paper we adopt partial-label learning with conditional random fields to make use of this valuable knowledge for semi-supervised Chinese word segmentation. The basic idea of partial-label learning is to optimize a cost function that marginalizes the probability mass in the constrained space that encodes this knowledge. By integrating some domain adaptation techniques, such as EasyAdapt, our result reaches an F-measure of 95.98% on the CTB-6 corpus, a significant improvement from both the supervised baseline and a previous proposed approach, namely constrained decode.


title: The Gold Standard in Corpus Annotation

abstract: Trustworthy corpora are necessary for training and meaningful evaluation of algorithms which use annotations. These standard collections are called Gold Standard Corpora (GSC). However the construction of GSC is a laborious and time-consuming process and size, quality and most of all availability of task-specific GSC directly influence the development of machine learning based natural language processing algorithms. This paper provides an introduction to gold standard corpus construction in the context of natural language processing and gives an overview of alternative approaches.


title: Seeing Behind the Camera: Identifying the Authorship of a Photograph

abstract: We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.


The Triangle of Life: Evolving Robots in Real-time and Real-space


title: A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments

abstract: This paper reviews the concepts of immersion and presence in virtual environments VEs. We propose that the degree of immersion can be objectively assessed as the characteristics of a technology, and has dimensions such as the extent to which a display system can deliver an inclusive, extensive, surrounding, and vivid illusion of virtual environment to a participant. Other dimensions of immersion are concerned with the extent of body matching, and the extent to which there is a self-contained plot in which the participant can act and in which there is an autonomous response. Presence is a state of consciousness that may be concomitant with immersion, and is related to a sense of being in a place. Presence governs aspects of autonomie responses and higher-level behaviors of a participant in a VE. The paper considers single and multiparticipant shared environments, and draws on the experience of ComputerSupported Cooperative Working CSCW research as a guide to understanding presence in shared environments. The paper finally outlines the aims of the FIVE Working Group, and the 1995 FIVE Conference in London, UK.


title: Sentence Similarity Learning by Lexical Decomposition and Composition

abstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.


title: Issues and Challenges in Anomaly Intrusion Detection for HTTP Web Services

abstract: In recent years, the development of Web-based applications has made possible novel online activities, such as banking and electronic shopping. This implies significant use of the Hypertext Transfer Protocol (HTTP) as the standard communication protocol enabler for Web services. Due to this role, HTTP has become an essential middle target of bound attacks for intruders. This paper is set to address various problems in anomaly-based intrusion detection for HTTP Web services. We seek to identify common essential methods and solutions, as well as the gaps, limitations and challenges in anomaly intrusion detection in terms of used experimental datasets, features and techniques.


title: High-performance polyline intersection based spatial join on GPU-accelerated clusters

abstract: The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.


title: An architecture-driven software mobility framework

abstract: Software architecture has been shown to provide an appropriate level of granularity for assessing a software system's quality attributes (e.g., performance and dependability). Similarly, previous research has adopted an architecture-centric approach to reasoning about and managing the run-time adaptation of software systems. For mobile and pervasive software systems, which are known to be innately dynamic and unpredictable, the ability to assess a system's quality attributes and manage its dynamic run-time behavior is especially important. In the past, researchers have argued that a software architecture-based approach can be instrumental in facilitating mobile computing. In this paper, we present an integrated architecture-driven framework for modeling, analysis, implementation, deployment, and run-time migration of software systems executing on distributed, mobile, heterogeneous computing platforms. In particular, we describe the framework's support for dealing with the challenges posed by both logical and physical mobility. We also provide an overview of our experience with applying the framework to a family of distributed mobile robotics systems. This experience has verified our envisioned benefits of the approach, and has helped us to identify several avenues of future work.


title: Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

abstract: For many forms of e-learning environments, the system's behavior can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take. Pedagogical strategies are policies to decide the next system action when there are multiple ones available. In this project we present a Reinforcement Learning (RL) approach for inducing effective pedagogical strategies and empirical evaluations of the induced strategies. This paper addresses the technical challenges in applying RL to Cordillera, a Natural Language Tutoring System teaching students introductory college physics. The algorithm chosen for this project is a model-based RL approach, Policy Iteration, and the training corpus for the RL approach is an exploratory corpus, which was collected by letting the system make random decisions when interacting with real students. Overall, our results show that by using a rather small training corpus, the RL-induced strategies indeed measurably improved the effectiveness of Cordillera in that the RL-induced policies improved students' learning gains significantly.


title: CQR codes: Colored quick-response codes

abstract: This paper proposes a new way to store/transmit information using a Colored QR Code structure. Instead of using only black and white modules, the proposed code is designed to employ 5 different RGB colors (red, green, blue, black and white), which enables twice as much storage capacity compared to traditional binary QR Codes. Reed-Solomon error-correcting code with a theoretical correction capability of 38.41% is also applied. In our experiments, each Colored QR Code in the test set is printed, scanned using a 3.2 megapixel digital camera and decoded. We show that the proposed scheme can consistently decode 1024 bits of information stored on a 1.3 cm × 1.3 cm printed area.


title: Wi-chase: A WiFi based human activity recognition system for sensorless environments

abstract: An extensive set of research efforts have explored Channel State Information for human activity detection. By extracting CSI from a sequence of packets, one can statistically analyze the temporal variations embedded therein and recognize corresponding human activities. In this paper, we present Wi-Chase, a sensorless system based on CSI from ubiquitous WiFi packets for human activity detection. Different from existing schemes utilizing only CSI of one or a small subset of subcarriers, Wi-Chase fully utilizes all available subcarriers of the WiFi signal and incorporates variations in both their phases and magnitudes. As each subcarrier carries integral information that will improve the recognition accuracy because of detailed correlated information content in different subcarriers, we can achieve much higher detection accuracy. To the best of our knowledge, this is the first system that gathers information from all the subcarriers to identify and classify multiple activities. Our experimental results show that Wi-Chase is robust and achieves an average classification accuracy greater than 97% for multiple communication links.


title: A wireless electrocardiogram detection for personal health monitoring

abstract: The current paper presents low-power analog integrated circuits (ICs) for wireless electrocardiogram (ECG) detection in personal health monitoring. Considering the power-efficient communication in the body sensor network (BSN), the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. The proposed system comprises the design and implementation with three subsystems, namely, (1) the ECG acquisition node, (2) the protocol for standard IEEE 802.15.4 ZigBee system, and (3) the radio frequency (RF) transmitter circuits. A preamplifier, a low-pass filter, and a successive-approximation analog-to-digital converter (SA-ADC) are integrated to detect an ECG signal. For high integration, the ZigBee protocol is adopted for wireless communication. To transmit the ECG signal through wireless communication, a quadrature voltage-controlled oscillator and a 2.4 GHz low-IF transmitter with a power amplifier and up-conversion mixer are also developed. In the receiver, a 2.4 GHz fully integrated CMOS radio-frequency front-end with a low-noise amplifier, and a quadrature mixer is proposed. The low-power wireless bio-signal acquisition SoC (WBSA-SoC) has been implemented in TSMC 0.18-μm standard CMOS process. The measurement results on the human body reveal that the ECG signals can be acquired effectively by the proposed SoC.


title: A study of staircase modulation technique for single phase matrix converter

abstract: This paper proposes the implementation of staircase modulation technique to a single phase matrix converter for reducing the harmonic distortions in the output. The matrix converter is used as cyclo-inverter as well cyclo-converter. Simulation results are shown for the converter using MATLAB/SIMULINK software. Various parameters including output frequency of the converter, carrier frequency of the converter, number of steps in staircase wave, modulation index, step size, length and breadth of each step etc., are varied and results of each case are obtained. The minimum total harmonic distortion (THD) obtained is 1.003% for cyclo-inverter and 1.209% for cyclo-converter, when only output frequency is varied keeping all other parameters constant at carrier frequency of 3 kHz. The output voltage obtained is more than 90% of the fundamental, which is on higher side.


title: Bitconeview: visualization of flows in the bitcoin transaction graph

abstract: Bitcoin is a digital currency whose transactions are stored into a public ledger, called blockchain, that can be viewed as a directed graph with more than 70 million nodes, where each node represents a transaction and each edge represents Bitcoins flowing from one transaction to another one. We describe a system for the visual analysis of how and when a flow of Bitcoins mixes with other flows in the transaction graph. Such a system relies on high-level metaphors for the representation of the graph and the size and characteristics of transactions, allowing for high level analysis of big portions of it.


title: Complex analysis of movement in evaluation of flat bench press performance.

abstract: The complex methodology of investigations was applied to study a movement structure on bench press. We have checked the usefulness of multimodular measuring system (SMART-E, BTS company, Italy) and a special device for tracking the position of barbell (pantograph). Software Smart Analyser was used to create a database allowing chosen parameters to be compared. The results from different measuring devices are very similar, therefore the replacement of many devices by one multimodular system is reasonable. In our study, the effect of increased barbell load on the values of muscles activity and bar kinematics during the flat bench press movement was clearly visible. The greater the weight of a barbell, the greater the myoactivity of shoulder muscles and vertical velocity of the bar. It was also confirmed the presence of the so-called sticking point (period) during the concentric phase of the bench press. In this study, the initial velocity of the barbell decreased (vmin) not only under submaximal and maximal loads (90 and 100% of the one repetition maximum; 1-RM), but also under slightly lighter weights (70 and 80% of 1-RM).


title: Big Data, the Internet of Things, and the Revised Knowledge Pyramid

abstract: The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. An earlier version of this paper presented a revised knowledge-KM pyramid that included processes such as filtering and sense making, reversed the pyramid by positing there was more knowledge than data, and showed knowledge management as an extraction of the pyramid. This paper expands the revised knowledge pyramid to include the Internet of Things and Big Data. The result is a revision of the data aspect of the knowledge pyramid. Previous thought was of data as reflections of reality as recorded by sensors. Big Data and the Internet of Things expand sensors and readings to create two layers of data. The top layer of data is the traditional transaction / operational data and the bottom layer of data is an expanded set of data reflecting massive data sets and sensors that are near mirrors of reality. The result is a knowledge pyramid that appears as an hourglass.


title: IT ] 4 N ov 2 01 6 1 Denoising-based Vector AMP

abstract: The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the vector AMP (VAMP) algorithm has been proposed to fix this problem. In this work, we show that the benefits of VAMP extend to D-VAMP.


title: A New Non-invasive Method for Measuring Blood Glucose Using Instantaneous Differential Near Infrared Spectrophotometry

abstract: We describe further development of a novel method for non-invasive measurement of blood glucose concentration (BGL), named Pulse Glucometry, based on differential near infrared spectrophotometry. Sequential temporal differences of infrared transmittance spectra from the radiation intensity (Ilambda) emerging from a fingertip containing an arterial pulse component (DeltaIlambda) are analysed. To perform the measurements we developed a new high-speed spectrophotometer, covering the wavelength range from 900 to 1700 nm, scanning at a maximum spectral rate of 1800 spectra/s, with a minimum exposure time of 20 mus. Spectra related only to the pulsatile blood component are derived, thus minimising influences of basal components such as resting blood volume, skin, muscle and bone. We have now improved the performance of the spectrophotometer and in the present paper we describe new in vivo measurements carried out in 23 healthy volunteers undergoing glucose tolerance tests. Blood samples were collected from the cephalic vein simultaneously with radiation intensity measurements in the fingertip every 10 min before and after oral administration of glucose solution for 120 min. BGL values were then predicted using a PLS calibration model and compared with blood values determined by colorimetric assay. The precision and accuracy of the non-invasive determinations are encouraging.


title: Pairwise Confusion for Fine-Grained Visual Classification

abstract: Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally introducing confusion in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. PC is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.


title: VoxCeleb2: Deep Speaker Recognition

abstract: The objective of this paper is speaker recognition under noisy and unconstrained conditions. ::: We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. ::: Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin.


title: Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering

abstract: We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability in complex activities. Hence, we develop novel intermediate representations. An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed. Our hierarchical model captures interactions among people by adopting a dynamic group-based interaction mechanism. Next, our appearance rendering network encodes the targets' appearances by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate videos that are superior to state-of-the-art methods, and can handle complex human activity scenarios in video forecasting.


title: Array Synthesis for Optimal Microwave Power Transmission in the Presence of Excitation Errors

abstract: Beam capture efficiency (BCE) is one key factor of the overall efficiency in a microwave power transmission (MPT) system. However, excitation errors of transmitting array are inevitable in practice, which always cause deviation of BCE from the designed one. In this paper, we propose a synthesis method of transmitting array for optimal MPT by using cooperatively coevolving differential evolution (CCDE) algorithm while considering excitation errors including amplitude errors and phase errors. Toward this purpose, a statistical analysis (SA) method is also presented to evaluate the achievable worst BCE in the presence of excitation errors. The tolerance of BCE against the random excitation errors is simulated by computer program, and the positions and nominal excitation amplitudes are both simultaneously optimized to improve the worst BCE. Simulated results show that the worst BCE is improved about 4% from 81% to 85% based on the SA-CCDE synthesis method for the excitation errors $(\sigma _{\delta }, \sigma _{\Phi }) =(0.1,10^{\circ})$ , where $\delta _{n}$ denotes the amplitude error in percent and $\Phi _{n}$ represents the phase error.


title: The integrated response of the human cerebro-cerebellar and limbic systems to acupuncture stimulation at ST 36 as evidenced by fMRI

abstract: Abstract Clinical and experimental data indicate that most acupuncture clinical results are mediated by the central nervous system, but the specific effects of acupuncture on the human brain remain unclear. Even less is known about its effects on the cerebellum. This fMRI study demonstrated that manual acupuncture at ST 36 (Stomach 36, Zusanli ), a main acupoint on the leg, modulated neural activity at multiple levels of the cerebro-cerebellar and limbic systems. The pattern of hemodynamic response depended on the psychophysical response to needle manipulation. Acupuncture stimulation typically elicited a composite of sensations termed deqi that is related to clinical efficacy according to traditional Chinese medicine. The limbic and paralimbic structures of cortical and subcortical regions in the telencephalon, diencephalon, brainstem and cerebellum demonstrated a concerted attenuation of signal intensity when the subjects experienced deqi . When deqi was mixed with sharp pain, the hemodynamic response was mixed, showing a predominance of signal increases instead. Tactile stimulation as control also elicited a predominance of signal increase in a subset of these regions. The study provides preliminary evidence for an integrated response of the human cerebro-cerebellar and limbic systems to acupuncture stimulation at ST 36 that correlates with the psychophysical response.


title: Topology of molecular interaction networks

abstract: Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.


title: Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models

abstract: FAPESP (processes 2012/24487-3 and 2012/04549-4) and Brazil-Europe Erasmus Mundus project (process BM13DM0002) partially funded this research


title: Quantifying and Visualizing Attribute Interactions

abstract: Interactions are patterns between several attributes in data that cannot be inferred from any subset of these attributes. While mutual information is a well-established approach to evaluating the interactions between two attributes, we surveyed its generalizations as to quantify interactions between several attributes. We have chosen McGill's interaction information, which has been independently rediscovered a number of times under various names in various disciplines, because of its many intuitively appealing properties. We apply interaction information to visually present the most important interactions of the data. Visualization of interactions has provided insight into the structure of data on a number of domains, identifying redundant attributes and opportunities for constructing new features, discovering unexpected regularities in data, and have helped during construction of predictive models; we illustrate the methods on numerous examples. A machine learning method that disregards interactions may get caught in two traps: myopia is caused by learning algorithms assuming independence in spite of interactions, whereas fragmentation arises from assuming an interaction in spite of independence.


title: A space-vector modulated sensorless direct-torque control for direct-drive PMSG wind turbines

abstract: This paper proposes a space-vector modulation (SVM)-based direct-torque control (DTC) scheme for a permanent magnet synchronous generator (PMSG) used in a variable-speed direct-drive wind power generation system. A quasi-sliding-mode observer (QSMO) that uses a finite sampling frequency is proposed to estimate the rotor position and stator flux linkage based on the current model of the PMSG. The optimal torque command is directly obtained and used for DTC by which the maximum power point tracking (MPPT) control of the wind turbine generator is achieved without the need of any wind speed or rotor position sensors. In addition to realizing the fixed switching frequency and low flux and torque ripples, the benefits of the proposed control also include the removal of a torque observer, which leads to reduced computational cost, improved dynamic response, and enhanced robustness of the whole system. Simulation studies on a 3-kW PMSG wind turbine demonstrate the effectiveness of the proposed SVM-DTC algorithm with 10 kHz controlling frequency.


title: Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read

abstract: Spin Transfer Torque Magnetoresistive RAM (STTMRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9∼13% and reduces total energy by 4∼8% over all existing read schemes including HCRR and LCLL.


Stewart Platform Robot for Interactive Tabletop Engagement


title: An analytic solution to discrete Bayesian reinforcement learning

abstract: Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for offline learning in simulated environments. We propose a new algorithm, called BEETLE, for effective online learning that is computationally efficient while minimizing the amount of exploration. We take a Bayesian model-based approach, framing RL as a partially observable Markov decision process. Our two main contributions are the analytical derivation that the optimal value function is the upper envelope of a set of multivariate polynomials, and an efficient point-based value iteration algorithm that exploits this simple parameterization.


title: An Architecture for a Generic Dialogue Shell

abstract: This paper describes our work on dialogue systems that can mimic human conversation, with the goal of providing intuitive access to a wide range of applications by expanding the user's options in the interaction. We concentrate on practical dialogue: dialogues in which the participants need to accomplish some objective or perform some task. Two hypotheses regarding practical dialogue motivate our research. First, that the conversational competence required for practical dialogues, while still complex, is significantly simpler to achieve than general human conversational competence. And second, that within the genre of practical dialogue, the bulk of the complexity in the language interpretation and dialogue management is independent of the task being performed. If these hypotheses are true, then it should be possible to build a generic dialogue shell for practical dialogue, by which we mean the full range of components required in a dialogue system, including speech recognition, language processing, dialogue management and response planning, built in such a way as to be readily adapted to new applications by specifying the domain and task models. This paper documents our progress and what we have learned so far based on building and adapting systems in a series of different problem solving domains.


title: Electrophysiological Studies of Face Perception in Humans

abstract: Event-related potentials (ERPs) associated with face perception were recorded with scalp electrodes from normal volunteers. Subjects performed a visual target detection task in which they mentally counted the number of occurrences of pictorial stimuli from a designated category such as butterflies. In separate experiments, target stimuli were embedded within a series of other stimuli including unfamiliar human faces and isolated face components, inverted faces, distorted faces, animal faces, and other nonface stimuli. Human faces evoked a negative potential at 172 msec (N170), which was absent from the ERPs elicited by other animate and inanimate nonface stimuli. N170 was largest over the posterior temporal scalp and was larger over the right than the left hemisphere. N170 was delayed when faces were presented upside-down, but its amplitude did not change. When presented in isolation, eyes elicited an N170 that was significantly larger than that elicited by whole faces, while noses and lips elicited small negative ERPs about 50 msec later than N170. Distorted human faces, in which the locations of inner face components were altered, elicited an N170 similar in amplitude to that elicited by normal faces. However, faces of animals, human hands, cars, and items of furniture did not evoke N170. N170 may reflect the operation of a neural mechanism tuned to detect (as opposed to identify) human faces, similar to the “structural encoder” suggested by Bruce and Young (1986). A similar function has been proposed for the face-selective N200 ERP recorded from the middle fusiform and posterior inferior temporal gyri using subdural electrodes in humans (Allison, McCarthy, Nobre, Puce, & Belger, 1994c). However, the differential sensitivity of N170 to eyes in isolation suggests that N170 may reflect the activation of an eye-sensitive region of cortex. The voltage distribution of N170 over the scalp is consistent with a neural generator located in the occipitotemporal sulcus lateral to the fusiform/inferior temporal region that generates N200.


title: A Method of Risk Assessment for Multi-Factor Authentication

abstract: User authentication refers to user identification based on something a user knows, something a user has, something a user is or something the user does; it can also take place based on a combination of two or more of such factors. With the increasingly diverse risks in online environments, user authentication methods are also becoming more diversified. This research analyzes user authentication methods being used in various online environments, such as web portals, electronic transactions, financial services and e-government, to identify the characteristics and issues of such authentication methods in order to present a user authentication level system model suitable for different online services. The results of our method are confirmed through a risk assessment and we verify its safety using the testing method presented in OWASP and NIST SP800-63.


title: Better knowledge with social media? Exploring the roles of social capital and organizational knowledge management

abstract: Purpose – The purpose of this paper is to explore social media’s impact on organizational knowledge quality through the theoretical lens of social capital and resource exchange.Design/methodology/approach – Theory-confirming, quantitative study using panel data collected through web-based survey.Findings – The results show that while social media affect structural capital and cognitive capital directly, it only affects relational capital indirectly through structural and cognitive capital. Moreover, overall social media and the enhanced social capital do help promote organizational efforts in knowledge management, which subsequently leads to higher level of organizational knowledge quality.Research limitations/implications – All survey respondents were from the U.S., which may limit the generalizability of the findings. The authors also call for more research in establishing the time sequence in the proposed causal relations and in the individual level mechanism through which social media promotes organizational knowledge quality.Practical implications – This study highlights both the potential and limitations of social media in promoting organizational knowledge management. Businesses must consciously manage the assimilation and use of social media to benefit from them.Originality/value – The authors position the study at the intersection of social media, social capital, and knowledge management and explicate how social media work through social capital and organizational knowledge management efforts to affect knowledge quality.


Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis


title: Wireless power transfer: A survey of EV battery charging technologies

abstract: In this paper, a comprehensive review of existing technological solutions for wireless power transfer used in electric vehicle battery chargers is given. The concept of each solution is thoroughly reviewed and the feasibility is evaluated considering the present limitations in power electronics technology, cost and consumer acceptance. In addition, the challenges and advantages of each technology are discussed. Finally a thorough comparison is made and a proposed mixed conductive/wireless charging system solution is suggested to solve the inherent existing problems.


title: In vivo homodimerisation of HTLV-1 Gag and MA gives clues to the retroviral capsid and TM envelope protein arrangement.

abstract: During retroviral particle formation, the capsid precursors (Gag) associate with the cell membrane via their matrix (MA) domain to form viral assembling particles. After budding, Gag and its proteolytically matured MA, form a shell in the released immature and mature particles, respectively. Although the arrangement of Gag domains in vitro and their radial organisation in retroviral particles have been extensively studied, little is known concerning Gag inter-subunit interactions in authentic retroviruses. We report that human T-cell leukemia virus type 1 Gag homodimerises in the cell via a disulphide bonding at cysteine 61 in the MA domain. Most Gags are homodimeric after budding and MAs are also dimeric in mature authentic virions. Molecular modelling of the MA domain indicates that non-covalent interactions at the MA dimer interface may also be important for Gag (and MA) dimerisation. In addition, all amino acids previously reported to be involved in MA-transmembrane (TM) interactions are located on the MA face opposite to the dimer interface. The model reveals that homodimerisation is compatible with a hexameric network of Gag and MA dimers that look like the hexameric networks observed for other retroviruses. These data, together with previous studies, lead us to propose a supra-molecular arrangement model in which the transmembrane glycoproteins of the virion envelope are anchored in a hexameric cage hole formed by the MA.


title: On the trajectory tracking control of industrial SCARA robot manipulators

abstract: In this paper, the authors discuss, from an experimental point of view, the use of different control strategies for the trajectory tracking control of an industrial selective compliance assembly robot arm robot, which is one of the most employed manipulators in industrial environments, especially for assembly tasks. Specifically, they consider decentralized controllers such as proportional-integral-derivative-based and sliding-mode ones and model-based controllers such as the classical computed-torque one and a neural-network-based controller. A simple procedure for the estimation of the dynamic model of the manipulator is given. Experimental results provide a detailed framework about the cost/benefit ratio regarding the use of the different controllers, showing that the performance obtained with decentralized controllers may suffice in a large number of industrial applications, but in order to achieve low tracking errors also for high-speed trajectories, it might be convenient to adopt a neural-network-based control scheme, whose implementation is not particularly demanding.


title: Parasitics assisted soft-switching and secondary-clamped current-fed bidirectional voltage doubler for fuel cell vehicles

abstract: A novel current-fed secondary-assisted clamped zero current commutated bidirectional full-bridge voltage doubler is proposed for fuel cell vehicles (FCVs). The proposed topology with novel modulation is suitable for interfacing energy storage with fuel cell or dc bus in FCVs. Voltage doubler on secondary side is selected to enhance the gain by 2x and reduce transformer size. Parasitics based zero voltage switching (ZVS), secondary based zero current switching (ZCS) of low voltage side devices and ZVS of secondary devices are achieved. Proposed secondary modulation technique naturally clamps the voltage across the primary side devices with zero current commutation eliminating the necessity for traditional active-clamp or passive snubbers. Switching losses are reduced significantly owing to soft-switching of all semiconductor devices. Synchronous rectification may be implemented to obtain high efficiency. Soft-switching and voltage-clamping are inherent and achieved at variation in load and source voltage. Steady state operation, analysis, design, simulation and experimental results are presented in this paper.


title: Tree-Structured Reinforcement Learning for Sequential Object Localization

abstract: Existing object proposal algorithms usually search for possible object regions over multiple locations and scales separately, which ignore the interdependency among different objects and deviate from the human perception procedure. To incorporate global interdependency between objects into object localization, we propose an effective Tree-structured Reinforcement Learning (Tree-RL) approach to sequentially search for objects by fully exploiting both the current observation and historical search paths. The Tree-RL approach learns multiple searching policies through maximizing the long-term reward that reflects localization accuracies over all the objects. Starting with taking the entire image as a proposal, the Tree-RL approach allows the agent to sequentially discover multiple objects via a tree-structured traversing scheme. Allowing multiple near-optimal policies, Tree-RL offers more diversity in search paths and is able to find multiple objects with a single feedforward pass. Therefore, Tree-RL can better cover different objects with various scales which is quite appealing in the context of object proposal. Experiments on PASCAL VOC 2007 and 2012 validate the effectiveness of the Tree-RL, which can achieve comparable recalls with current object proposal algorithms via much fewer candidate windows.


title: PinPoint: Localizing Interfering Radios Abstract

abstract: This paper presents PinPoint, a technique for localizing rogue interfering radios that adhere to standard protocols in the inhospitable ISM band without any cooperation from the interfering radio. PinPoint is designed to be incrementally deployed on top of existing 802.11 WLAN infrastructure, and used by network administrators to identify and troubleshoot sources of interference which may be disrupting the network. PinPoint's key contribution is a novel algorithm that accurately computes the line of sight angle of arrival (AoA) and cyclic signal strength indicator (CSSI) of the target interfering signal at all APs, even when the line of sight (LoS) component is buried by stronger multipath components, interference and noise. PinPoint leverages this algorithm to design an optimization technique, which can localize interfering radios and simultaneously identify the type of interference. Unlike several localization techniques which require extensive pre-deployment calibration (e.g. RF-Fingerprinting), PinPoint requires very little calibration by the network administrator, and uses a novel algorithm to self-initialize its bearings, even if the locations of some AP are initially unknown and are oriented randomly. We implement PinPoint on WARP software radios and deploy in an indoor testbed spanning an entire floor of our department. We compare PinPoint with the best known prior RSSI [8, 11] and MUSIC-AoA based approaches and show that PinPoint achieves a median localization error of 0.97 meters, which is around three times lower compared to the RSSI [8, 11] and MUSIC-AoA based approaches.


title: Analysis of electromagnetic stresses and structural integrity on the winding of a transformer under inrush currents conditions

abstract: The objective of this paper is to assist in the investigation of electromagnetic forces during faults inside the transformers as a result of transient states. The methodology is based on modeling a single-phase 50 MVA transformer subjected to inrush currents through finite element method (FEM). In this perspective, the conditions of inrush currents are used to estimate the magnetic field density dispersion in the transformer and to find the values of forces in axial and radial directions. These components are distributed along the energized windings for observing the loads in high-voltage windings. Thus, this article will present investigations of electromagnetic forces, structural deformation, stresses, and safety factor on the winding of transformers when subjected to inrush current.


title: A framework for automatic question generation from text using deep reinforcement learning

abstract: Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task, but they are not without shortcomings. Firstly, these models lack the ability to handle rare words and the word repetition problem. Moreover, all previous works optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation measure). In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, enhanced with the copy mechanism to handle the rare-words problem and the coverage mechanism to solve the word repetition problem. The evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward. Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L, {\em etc.}, suitable for sequence to sequence tasks such as QG. Our comprehensive evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation.


Pattern-revising Enhanced Simple Question Answering over Knowledge Bases


title: Looking Beyond the Visible Scene

abstract: A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.


title: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets

abstract: A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable to each input feature, l0-norm Sparse SVM (SSVM) is converted to a mixed integer programming (MIP) problem. Rather than directly solving this MIP, we propose an efficient cutting plane algorithm combining with multiple kernel learning to solve its convex relaxation. A global convergence proof for our method is also presented. Comprehensive experimental results on one synthetic and 10 real world datasets show that our proposed method can obtain better or competitive performance compared with existing SVM-based feature selection methods in term of sparsity and generalization performance. Moreover, our proposed method can effectively handle large-scale and extremely high dimensional problems.


title: Suspect tracking based on call logs analysis and visualization

abstract: In Thailand, investigator can track and find the suspects by using call logs from suspects' phone numbers and their contacts. In many cases, the suspects changed their phone numbers to avoid tracking. The problem is that the investigators have difficulty to track these suspects from their call logs. Our hypothesis is that each user has a unique calling behavior pattern. The calling pattern is importance for tracking suspect's telephone number. To compare the calling patterns, we consider common contact groups. Thus, the aim of this project is to develop a call logs tracking system which can predict a set of new possible suspect's phone numbers and present their contacts' connection with our network diagram visualization based on Graph database (Neo4j). This system will be very necessary for investigators because it can save investigators' time from analyzing excessive call logs data. The system can predict the possible suspect's phone numbers. Furthermore, our visualization can enhance human's sight ability to connect the relation among related phone numbers. Finally, the experimental results on real call logs demonstrate that our method can track telephone number approximately 69% of single possible suspect phone number's matching while 89% of multiple possible suspect phone numbers' matching.


title: Robust visual tracking via multi-task sparse learning

abstract: In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing l p, q mixed norms (p Є {2, ∞} and q = 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p = q = 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers.


title: The Effect of Gang-of-Four Design Patterns Usage on Design Quality Attributes

abstract: Context: In the plethora of studies, it has been empirically investigated that the incidence of design pattern instances can be considered as an indicator to elaborate the software design. The developers, who have more concern with design quality, are interested to know the effect of use intensity of design patterns on the system level design quality attributes. Goal: The objective of our study is to empirically investigate the effect of the frequent use of the Gang-of-Four (GoF) design patterns on the design quality attributes. Method: We perform a case study which includes three analyses in order to investigate, 1) the existence of a correlation between design pattern usage and design quality attributes, 2) the confounding effect of system size (number of classes) on the correlation, and 3) how the change in number of employed design pattern instances affects the design quality in the subsequent releases of a system. Results: The result of this study suggests that the reusability, flexibility and understandability have a significant relationship with the employed instances of Template, Adapter-Command, Singleton and State-Strategy design patterns, however, it is affected by the confounding effect of system size. Subsequently, in the subsequent releases of an open source project named velocity, we observed the use intensity of Singleton, Adapter-Command, and State-Strategy design patterns can improve the design quality in term of reusability and flexibility attributes.


title: Automatic feature selection for named entity recognition using genetic algorithm

abstract: This paper presents a feature selection approach for named entity recognition using genetic algorithm. Different aspects of genetic algorithm including computational time and criteria for evaluating an individual (i.e., size of the feature subset and the classifier's accuracy) are analyzed in order to optimize its learning process. Two machine learning algorithms, k-Nearest Neighbor and Conditional Random Fields, are used to calculate the accuracy of the named entity recognition system. To evaluate the effectiveness of our genetic algorithm, feature subsets returning by our proposed genetic algorithm are compared to feature subsets returning by a hill climbing algorithm and a backward one. Experimental results show that feature subsets obtained by our genetic algorithm is much smaller than the original feature set without losing of predictive accuracy. Furthermore, these feature subsets result in higher classifier's accuracies than that of the hill climbing algorithm and the backward one.


TREC 2018 News Track


title: A PLL control for self-tuning of parallel wireless power transfer receivers utilizing switch-mode gyrator emulated inductors

abstract: In multiple receivers wireless power transfer (WPT) systems, it is preferable to retune the resonant frequency of every receiver to the transmitter operating frequency in front of frequency mismatches. This paper discusses a proposal for electronic tuning for WPT receivers by means of a variable active switch-mode inductance. The proposed method benefits from the gyrator concept to emulate a variable inductance. Instead of the conventional approach of linear amplifier based implementation of a gyrator, a switch-mode gyrator circuit is exploited for more efficient operation. Additionally, a PLL-like control is presented to enable self-tuning for the receiver resonant tank. Furthermore, a design-space characterization for the system dynamic behavior has been discussed to show the control robustness and the instabilities (including slow-scale and fast-scale chaotic instabilities) it may undergo.


DOMAIN DECOMPOSITION AND NON-UNIFORM POLAR / SPHERICAL GRID INTERPOLATION APPROACH TO FAST FIELD EVALUATION


title: CFA-aware features for steganalysis of color images

abstract: Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.


title: Anomaly intrusion detection using one class SVM

abstract: Kernel methods are widely used in statistical learning for many fields, such as protein classification and image processing. We recently extend kernel methods to intrusion detection domain by introducing a new family of kernels suitable for intrusion detection. These kernels, combined with an unsupervised learning method - one-class support vector machine, are used for anomaly detection. Our experiments show that the new anomaly detection methods are able to achieve better accuracy rates than the conventional anomaly detectors.


title: Evolutionary Channels for the Formation of Double Neutron Stars

abstract: We analyze binary population models of double-neutron stars and compare results to the accurately measured orbital periods and eccentricities of the eight known such systems in our Galaxy. In contrast to past similar studies, we especially focus on the dominant evolutionary channels (we identify three); for the first time, we use a detailed understanding of the evolutionary history of three double neutron stars as actual constraints on the population models. We find that the evolutionary constraints derived from the double pulsar are particularly tight, and less than half of the examined models survive the full set of constraints. The top-likelihood surviving models yield constraints on the key binary evolution parameters, but most interestingly reveal (1) the need for electron-capture supernovae from relatively low-mass degenerate, progenitor cores, and (2) the most likely evolutionary paths for the rest of the known double neutron stars. In particular, we find that J1913+16 likely went through a phase of Case BB mass transfer, and J1906+0746 and J1756–2251 are consistent with having been formed in electron-capture supernovae.


title: Multi-perspective Anomaly Detection in Business Process Execution Events

abstract: Ensuring anomaly-free process model executions is crucial in order to prevent fraud and security breaches. Existing anomaly detection approaches focus on the control flow, point anomalies, and struggle with false positives in the case of unexpected events. By contrast, this paper proposes an anomaly detection approach that incorporates perspectives that go beyond the control flow, such as, time and resources (i.e., to detect contextual anomalies). In addition, it is capable of dealing with unexpected process model execution events: not every unexpected event is immediately detected as anomalous, but based on a certain likelihood of occurrence, hence reducing the number of false positives. Finally, multiple events are analyzed in a combined manner in order to detect collective anomalies. The performance and applicability of the overall approach are evaluated by means of a prototypical implementation along and based on real life process execution logs from multiple domains.


Functional Roles of Glu-269 and Glu-325 within the Lactose Permease of Escherichia coZi *


title: Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

abstract: Advancements in medical science and technology, medicine and public health coupled with increased consciousness about nutrition and environmental and personal hygiene have paved the way for the dramatic increase in life expectancy globally in the past several decades. However, increased life expectancy has given rise to an increasing aging population, thus jeopardizing the socio-economic structure of many countries in terms of costs associated with elderly healthcare and wellbeing. In order to cope with the growing need for elderly healthcare services, it is essential to develop affordable, unobtrusive and easy-to-use healthcare solutions. Smart homes, which incorporate environmental and wearable medical sensors, actuators, and modern communication and information technologies, can enable continuous and remote monitoring of elderly health and wellbeing at a low cost. Smart homes may allow the elderly to stay in their comfortable home environments instead of expensive and limited healthcare facilities. Healthcare personnel can also keep track of the overall health condition of the elderly in real-time and provide feedback and support from distant facilities. In this paper, we have presented a comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies.


title: Implicit Negotiation Beliefs and Performance: Experimental and Longitudinal Evidence

abstract: The authors argue that implicit negotiation beliefs, which speak to the expected malleability of negotiating ability, affect performance in dyadic negotiations. They expected negotiators who believe negotiating attributes are malleable (incremental theorists) to outperform negotiators who believe negotiating attributes are fixed (entity theorists). In Study 1, they gathered evidence of convergent and discriminant validity for the implicit negotiation belief construct. In Study 2, they examined the impact of implicit beliefs on the achievement goals that negotiators pursue. In Study 3, they explored the causal role of implicit beliefs on negotiation performance by manipulating negotiators’ implicit beliefs within dyads. They also identified perceived ability as a moderator of the link between implicit negotiation beliefs and performance. In Study 4, they measured negotiators’ beliefs in a classroom setting and examined how these beliefs affected negotiation performance and overall performance in the course 15 weeks later. Across all performance measures, incremental theorists outperformed entity theorists. Consistent with the authors’ hypotheses, incremental theorists captured more of the bargaining surplus and were more integrative than their entity theorist counterparts, suggesting implicit theories are important determinants of how negotiators perform. Implications and future directions are discussed.


title: Automatic noise recognition based on neural network using LPC and MFCC feature parameters

abstract: This paper studies the automatic noise recognition problem based on RBF and MLP neural networks classifiers using linear predictive and Mel-frequency cepstral coefficients (LPC and MFCC). We first briefly review the architecture of each network as automatic noise recognition (ANR) approach, then, compare them to each other and investigate factors and criteria that influence final recognition performance. The proposed networks are evaluated on 15 stationary and non-stationary types of noises with frame length of 20 ms in term of correct classification rate. The results demonstrate that the MLP network using LPCs is a precise ANR with accuracy rate of 99.9%, while the RBF network with MFCCs coefficients goes afterward with 99.0% of accuracy.


title: A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

abstract: The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one-to-one” e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new “cold start” item problems.


title: Video games and prosocial behavior: A study of the effects of non-violent, violent and ultra-violent gameplay

abstract: Recent research on violent video game effects on prosocial behavior is mixed.Current experiment aimed to address methodology concerns of past research.In the present experiment, violent games have no bearing on prosociality compared to non-violent games.Increasing violence to ultraviolent levels has no effect on prosociality. Experimental evidence has pointed toward a negative effect of violent video games on social behavior. Given that the availability and presence of video games is pervasive, negative effects from playing them have potentially large implications for public policy. It is, therefore, important that violent video game effects are thoroughly and experimentally explored, with the current experiment focusing on prosocial behavior. 120 undergraduate volunteers (Mage=19.01, 87.5% male) played an ultra-violent, violent, or non-violent video game and were then assessed on two distinct measures of prosocial behavior: how much they donated to a charity and how difficult they set a task for an ostensible participant. It was hypothesized that participants playing the ultra-violent games would show the least prosocial behavior and those playing the non-violent game would show the most. These hypotheses were not supported, with participants responding in similar ways, regardless of the type of game played. While null effects are difficult to interpret, samples of this nature (undergraduate volunteers, high male skew) may be problematic, and participants were possibly sensitive to the hypothesis at some level, this experiment adds to the growing body of evidence suggesting that violent video game effects are less clear than initially thought.


title: Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing

abstract: Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption and improve resource utilization in data centers. Since VM consolidation problem is strictly NP-hard, many heuristic algorithms have been proposed to tackle the problem. However, most of the existing works deal only with minimizing the number of hosts based on their current resource utilization and these works do not explore the future resource requirements. Therefore, unnecessary VM migrations are generated and the rate of Service Level Agreement (SLA) violations are increased in data centers. To address this problem, our VM consolidation method which is formulated as a bin-packing problem considers both the current and future utilization of resources. The future utilization of resources is accurately predicted using a k-nearest neighbor regression based model. In this paper, we investigate the effectiveness of VM and host resource utilization predictions in the VM consolidation task using real workload traces. The experimental results show that our approach provides substantial improvement over other heuristic algorithms in reducing energy consumption, number of VM migrations and number of SLA violations.


title: Online Handwritten Cursive Word Recognition Using Segmentation-Free MRF in Combination with P2DBMN-MQDF

abstract: This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature point coordinates as unary features and the differences in coordinates between the neighboring feature points as binary features. Each character is modeled as a MRF and word MRFs are constructed by concatenating character MRFs according to a trie lexicon of words during recognition. Our method expands the search space using a character-synchronous beam search strategy to search the segmentation and recognition paths. This method restricts the search paths from the trie lexicon of words and preceding paths, as well as the lengths of feature points during path search. We also combine it with a P2DBMN-MQDF recognizer that is widely used for Chinese and Japanese character recognition.


title: A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines

abstract: Road safety is one of the most vital and crucial part of every countries daily economic growth. It gives so much impact in public health specially in the Philippines. Securing its safety will be a big help to a particular country's economic growth. The main objective of this paper is to analyze road accident's data to discover hidden pattern that can be used as precautionary measure to at least lessen the accident that occur yearly in Los Banos, Laguna, Philippines. Predicting algorithms such as Decision Tree, Naive Bayes and Rule induction were used to identify factors affecting accident in Los Banos, Laguna. Using these three classifier the following are the results obtained by the researchers; for Decision Tree 92.84% accuracy occurred with 0.797 kappa while in Naive Bayes 91.50% accuracy was generated with 0.741 kappa and 92.50% accuracy for Rule Induction and 0.783 kappa was produced. The researchers discovered that the place where an accident happened don't have significant correlation on the fatality of the victim. On the other hand, the researchers also found that the time and day play a vital role on the fatality or severity of victims of road accident particularly car collision.


title: A rough set approach to feature selection based on ant colony optimization

abstract: Rough set theory is one of the effective methods to feature selection, which can preserve the meaning of the features. The essence of rough set approach to feature selection is to find a subset of the original features. Since finding a minimal subset of the features is a NP-hard problem, it is necessary to investigate effective and efficient heuristic algorithms. Ant colony optimization (ACO) has been successfully applied to many difficult combinatorial problems like quadratic assignment, traveling salesman, scheduling, etc. It is particularly attractive for feature selection since there is no heuristic information that can guide search to the optimal minimal subset every time. However, ants can discover the best feature combinations as they traverse the graph. In this paper, we propose a new rough set approach to feature selection based on ACO, which adopts mutual information based feature significance as heuristic information. A novel feature selection algorithm is also given. Jensen and Shen proposed a ACO-based feature selection approach which starts from a random feature. Our approach starts from the feature core, which changes the complete graph to a smaller one. To verify the efficiency of our algorithm, experiments are carried out on some standard UCI datasets. The results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features.


title: Decentralized User-Centric Access Control using PubSub over Blockchain

abstract: We present a mechanism that puts users in the center of control and empowers them to dictate the access to their collections of data. Revisiting the fundamental mechanisms in security for providing protection, our solution uses capabilities, access lists, and access rights following well-understood formal notions for reasoning about access. This contribution presents a practical, correct, auditable, transparent, distributed, and decentralized mechanism that is well-matched to the current emerging environments including Internet of Things, smart city, precision medicine, and autonomous cars. It is based on well-tested principles and practices used in a distributed authorization, cryptocurrencies, and scalable computing.


title: Deep Learning for the Web

abstract: Deep learning is a machine learning technology that automatically extracts higher-level representations from raw data by stacking multiple layers of neuron-like units. The stacking allows for extracting representations of increasingly-complex features without time-consuming, offline feature engineering. Recent success of deep learning has shown that it outperforms state-of-the-art systems in image processing, voice recognition, web search, recommendation systems, etc [1]. A lot of industrial-scale big data processing systems including IBM Watson's Jeopardy Contest 2011, Google Now, Facebook's face recognition system, and the voice recognition systems by Google and Microsoft use deep learning [2][3][6]. Deep learning has a huge potential to improve the intelligence of the web and the web service systems by efficiently and effectively mining big data on the Web[4][5]. This tutorial provides the basics of deep learning as well as its key applications. We give the motivation and underlying ideas of deep learning and describe the architectures and learning algorithms for various deep learning models. We also cover applications of deep learning for image and video processing, natural language and text data analysis, social data analytics, and wearable IoT sensor data with an emphasis in the domain of Web systems. We will deliver the key insight and understanding of these techniques, using graphical illustrations and examples that could be important in analyzing a large amount of Web data. The tutorial is prepared to attract general audience at the WWW Conference, who are interested in machine learning and big data analysis for Web data. The tutorial consists of five parts. The first part presents the basics of neural networks, and their structures. Then we explain the training algorithm via backpropagation, which is a common method of training artificial neural networks including deep neural networks. We will emphasize how each of these concepts can be used in various Web data analysis. In the second part of the tutorial, we describe the learning algorithms for deep neural networks and related ideas, such as contrastive divergence, wake-sleep algorithms, and Monte Carlo simulation. We then describe various kinds of deep architectures, including stacked autoencoders, deep belief networks [7], convolutional neural networks [8], and deep hypernetworks [9]. In the third part, we present more details of the recursive neural networks, which can learn structured tree outputs as well as vector representations for phrases and sentences. We first show how training the recursive neural network can be achieved by a modified version of the back-propagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Then we will present its applications to sentence analysis including POS tagging, and sentiment analysis. The fourth part discusses the neural networks used to generate word embeddings, such as Word2Vec [10], DSSM for deep semantic similarity [11], and object detection in images [12], such as GoogLeNet, and AlexNet. We will explain in detail the applications of these deep learning techniques in the analysis of various social network data. By this point, the audience should have a clear understanding of how to build a deep learning system for word, sentence and document level tasks. The fifth part of the tutorial will cover other application examples of deep learning. These include object segmentation and action recognition from videos [9], web data analytics, and wearable/IoT sensor data modeling for smart services.


title: Latent Space Model for Road Networks to Predict Time-Varying Traffic

abstract: Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamism associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges holistically. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learns the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the superiority of our framework for real-time traffic prediction on large road networks over competitors as well as baseline graph-based LSM's.


title: Design of a linear haptic display based on approximate straight line mechanisms

abstract: In this paper, we study a class of one degree-of-freedom mechanisms in order to design linear haptic interfaces. They allow to perform straight line motions with only revolute joints, thus limiting the friction that characterizes linear bearings. We particularly describe the characteristics of these systems and their good properties to design haptic displays: parallel architecture, very good linearity, good use of the actuator torque. The Hoeken's mechanism which has the best characteristics to build a direct drive general purpose haptic display is selected. We present the fabricated prototype and its evaluation in terms of bandwidth, Coulomb friction and apparent mass.


title: Citations and Readership are Poor Indicators of Research Excellence: Introducing TrueImpactDataset, a New Dataset for Validating Research Evaluation Metrics

abstract: In this paper we show that citation counts and Mendeley readership are poor indicators of research excellence. Our experimental design builds on the assumption that a good evaluation metric should be able to distinguish publications that have changed a research field from those that have not. The experiment has been conducted on a new dataset for bibliometric research which we call TrueImpactDataset. TrueImpactDataset is a collection of research publications of two types -- research papers which are considered seminal work in their area and papers which provide a survey (a literature review) of a research area. The dataset also contains related metadata, which include DOIs, titles, authors and abstracts. We describe how the dataset was built and provide overview statistics of the dataset. We propose to use the dataset for validating research evaluation metrics. By using this data, we show that widely used research metrics only poorly distinguish excellent research.