title: Handwritten Urdu character recognition using one-dimensional BLSTM classifier

abstract: The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, that is why it nearly shares the similar challenges and complexities but with more intensity. We can categorize Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nasta’liq style, whereas Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nasta’liq handwritten dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. UNHD is publically available and can be download form https://sites.google.com/site/researchonurdulanguage1/databases. We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.


title: Human Agency in Social Cognitive Theory

abstract: The present article examines the nature and function of human agency within the conceptual model of triadic reciprocal causation. In analyzing the operation of human agency in this interactional causal structure, social cognitive theory accords a central role to cognitive, vicarious, self-reflective, and self-regulatory processes. The issues addressed concern the psychological mechanisms through which personal agency is exercised, the hierar- chical structure of self-regulatory systems, eschewal of the dichotomous construal of self as agent and self as object, and the properties of a nondualistic but nonreductional conception of human agency. The relation of agent cau- sality to the fundamental issues of freedom and deter- minism is also analyzed. The recent years have witnessed a resurgence of interest in the self-referent phenomena. One can point to several reasons why self processes have come to pervade many domains of psychology. Self-generated activities lie at the very heart of causal processes. They not only contribute to the meaning and valence of most external influences, but they also function as important proximal determi- nants of motivation and action. The capacity to exercise control over one's own thought processes, motivation, and action is a distinctively human characteristic. Because judgments and actions are partly self-determined, people can effect change in themselves and their situations through their own efforts. In this article, I will examine the mechanisms of human agency through which such changes are realized.


title: Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

abstract: Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.


title: Age estimation using a hierarchical classifier based on global and local facial features

abstract: The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed of an age group classification (e.g. the class of less than 20 years old, the class of 20-39 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted in the previous works. Consequently, we propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways, compared to the previous works. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Thirdly, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.


title: Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts

abstract: Today's biomedical research has become heavily dependent on access to the biological knowledge encoded in expert curated biological databases. As the volume of biological literature grows rapidly, it becomes increasingly difficult for biocurators to keep up with the literature because manual curation is an expensive and time-consuming endeavour. Past research has suggested that computer-assisted curation can improve efficiency, but few text-mining systems have been formally evaluated in this regard. Through participation in the interactive text-mining track of the BioCreative 2012 workshop, we developed PubTator, a PubMed-like system that assists with two specific human curation tasks: document triage and bioconcept annotation. On the basis of evaluation results from two external user groups, we find that the accuracy of PubTator-assisted curation is comparable with that of manual curation and that PubTator can significantly increase human curatorial speed. These encouraging findings warrant further investigation with a larger number of publications to be annotated. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/


title: HR Roles Effectiveness and HR Contributions Effectiveness: Comparing Evidence from HR and Line Managers

abstract: As the importance of human resource increases to the business, so does also the need to ensure its effective management. This study compared HR and line managers’ evaluation of the effectiveness of the HR department in terms of its roles and contributions to the firm using a multi-perspective view of effectiveness. Survey responses from 108 HR and 140 line managers from 108 large manufacturing and service firms in Malaysia showed significant differences in the ratings by HR and line managers. HR managers, quite unsurprisingly, rated higher both dimensions of the HR effectiveness as compared to line managers. For a comprehensive evaluation of HRM, the study reaffirms the need for a client-centered evaluation of effectiveness given that self-assessment is always exaggerated.


title: Learning Anonymized Representations with Adversarial Neural Networks

abstract: Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).


title: CiteSeer: an autonomous Web agent for automatic retrieval and identification of interesting publications

abstract: Research papers available on the World Wide Web (WWW or Web) areoften poorly organized, often exist in forms opaque to searchengines (e.g. Postscript), and increase in quantity daily.Significant amounts of time and effort are typically needed inorder to find interesting and relevant publications on the Web. Wehave developed a Web based information agent that assists the userin the process of performing a scientific literature search. Givena set of keywords, the agent uses Web search engines and heuristicsto locate and download papers. The papers are parsed in order toextract information features such as the abstract and individuallyidentified citations. The agents Web interface can be used to findrelevant papers in the database using keyword searches, or bynavigating the links between papers formed by the citations. Linksto both citing and cited publications can be followed. In additionto simple browsing and keyword searches, the agent can find paperswhich are similar to a given paper using word information and byanalyzing common citations made by the papers.


title: Radar remote monitoring of vital signs

abstract: Medical technology has improved remarkably over the past few generations, becoming more sophisticated and less invasive as the years progress. Now, with microwave Doppler radar phase modulation, noncontact respiration and heartbeat monitoring offers an attractive alternative to commonly prescribed chest-strap monitors. Doppler radar uses double-sideband transmission for vital sign detection from four sides of a human body. An unmodulated radio-frequency signal is transmitted toward the human body, where it is phase-modulated by the periodic physiological movement and reflected back to the receiver. The radar receiver captures the reflected signal and demodulates it to extract the vital sign signal components.


title: Alpha-structural recursion and induction

abstract: The nominal approach to abstract syntax deals with the issues of bound names and α-equivalence by considering constructions and properties that are invariant with respect to permuting names. The use of permutations gives rise to an attractively simple formalization of common, but often technically incorrect uses of structural recursion and induction for abstract syntax modulo α-equivalence. At the heart of this approach is the notion of finitely supported mathematical objects. This article explains the idea in as concrete a way as possible and gives a new derivation within higher-order classical logic of principles of α-structural recursion and induction for α-equivalence classes from the ordinary versions of these principles for abstract syntax trees.


title: What Future Quantitative Social Science Research Could Look Like: Confidence Intervals for Effect Sizes

abstract: An improved quantitative science would emphasize the use of confidence intervals (CIs), and especially CIs for effect sizes. This article reviews some definitions and issues related to developing these intervals. Confidence intervals for effect sizes are especially valuable because they facilitate meta-analytic thinking and the interpretation of intervals via comparison with the effect intervals from related prior studies. Several recommendations for the thoughtful use of such CIs are presented.


title: Image-guided ToF depth upsampling: a survey

abstract: Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies.


title: Barriers and benefits in the adoption of e‐government

abstract: This article examines the reasons individuals would choose electronic self‐service delivery methods over more traditional methods of service delivery for government services. The study investigated the factors related to decision making when people consider and evaluate the usage of an online e‐government delivery mechanism. The approach taken was based on a combination of attitudinal technology adoption models and the service quality concept, with data gathered via a questionnaire. Trust, financial security, information quality (all adoption barriers), time and money (both adoption benefits) were found to predict potential usage. That is willingness to use the online delivery option will be present if organisations can develop trust relationships with individuals, assure them that their financial details are secure, provide information that is relevant, accurate and up‐to‐date, and save individuals time and money. The results are significant to the public service manager who needs to consider both the barriers to adoption and the benefits of adoption if they are to develop plans to increase the take‐up of their electronic services.


title: Where is Education Heading and How About AI?

abstract: In this paper we present arguments supported by research examples for a fundamental shift of emphasis in education and its relation to technology, in particular AI- technology. No longer the ITS-paradigm dominates the field of AI and Education. New educational and pedagogic paradigms are being proposed and investigated, stressing the importance of learning how to learn instead of merely learning domain facts and rules of application. New uses of technology accompany this shift. We present trends and issues in this area exemplified by research projects and characterise three pedagogical scenarios in order to situate different modelling options for AI & Education.


title: New Comparative Study Between DES, 3DES and AES within Nine Factors

abstract: With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, also the internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information .Besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information. , which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question. Cryptography considers one of the techniques which used to protect the important information. In this paper a three algorithm of multimedia encryption schemes have been proposed in the literature and description. The New Comparative Study between DES, 3DES and AES within Nine Factors achieving an efficiency, flexibility and security, which is a challenge of researchers.


title: Vehicular Traffic Monitoring Using Bluetooth Scanning Over a Wireless Sensor Network

abstract: The ubiquitous nature of Bluetooth equipped devices has made it opportunistic to scavenge information that can be repurposed for applications other than initially intended. One such opportunity is in vehicular traffic monitoring, whereby sampling of Bluetooth radios serve as proxies for vehicles and consequently for traffic density and flow. This paper discusses a complete data collection system developed at the University of Manitoba that utilizes a variety of wireless networking technologies and devices to collect inferred traffic data at an intersection along a major thoroughfare in an urban setting. Specifically, a wireless sensor network of slave probes was designed and implemented with the objective to collect Bluetooth device information for this purpose. To facilitate easy setup and a long battery life, a solar-powered probe design was investigated. Data from each slave probe is communicated to a master node through XBee communication, where it is stored on a secure digital (SD) memory card before being transmitted to a central server every five minutes over a global system for mobile communications (GSM) cellular network. The server parses the data received and stores it in a database. Consumer and corporate websites may then access this database to display archived data or current data in real-time to various users.


medixain : Robust Blockchain Optimization Enabling Individual Medical Wallet Architecture


title: Moving objects detection and tracking in infrared or thermal image

abstract: Moving objects detection and tracking in infrared images is an important goal in most of the practical applications of the thermo vision systems. For these thermo vision applications here is proposed to apply a cost function associated with the minimization of a global criterion for simultaneous estimation of the optical flow and detection of the moving objects in infrared images. The optical flow and moving objects detection and tracking in infrared images are modeled with an appropriate neural network. The thermo vision or infrared images, captured from thermo camera, are first partitioned in rectangular blocks. The blocks are described with a number of parameters placed in the corresponding feature vectors. It is proposed to apply as parameters of the blocks the following important in thermal images characteristics: the position, the gray level and the local motion information. It is chosen the classification of the feature vectors by considering the displaced frame difference, according to Bayesian theory of decision criterion and representing a metric in the parameter space.


title: The Generalized ICN for 25Gbps+ channel using NRZ, PAM-M or Duobinary coding scheme

abstract: The integrated crosstalk noise (ICN) has been proposed in the IEEE 802.3ba standard to measure the crosstalk in the high-speed channel and accepted as a replacement of the ICR (Insertion Crosstalk Ratio) for channel noise estimation. As the PHY options of NRZ, PAM-4 and/or Duobinary are actively explored for higher data rate, a generalized ICN model will be needed for characterizing the channel SI performance. This paper presents the analysis of the integrated crosstalk noise (ICN) model for 10 Gb/s NRZ coding based system and generalizes the ICN model for 25 Gb/s and beyond system adopting the NRZ, PAM-4 or Duobinary coding scheme.


title: Information Systems and Healthcare XVI: Physician Adoption of Electronic Medical Records: Applying the UTAUT Model in a Healthcare Context

abstract: This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to the phenomenon of physician adoption of electronic medical records (EMR) technology. UTAUT integrates eight theories of individual acceptance into one comprehensive model designed to assist in understanding what factors either enable or hinder technology adoption and use. As such, it provides a useful lens through which to view what is currently taking place in the healthcare industry regarding EMR adoption. This is mutually beneficial to both the healthcare and MIS communities, as UTAUT offers valuable practical insight to the healthcare industry in explaining why EMR technology has not been more widely adopted as well as what prescriptions may facilitate future adoption, while offering the MIS community the opportunity to strengthen existing theory through an illustration of its application.


title: Measuring pictorial balance perception at first glance using Japanese calligraphy

abstract: According to art theory, pictorial balance acts to unify picture elements into a cohesive composition. For asymmetrical compositions, balancing elements is thought to be similar to balancing mechanical weights in a framework of symmetry axes. Assessment of preference for balance (APB), based on the symmetry-axes framework suggested in Arnheim R, 1974 Art and Visual Perception: A Psychology of the Creative Eye (Berkeley, CA: University of California Press), successfully matched subject balance ratings of images of geometrical shapes over unlimited viewing time. We now examine pictorial balance perception of Japanese calligraphy during first fixation, isolated from later cognitive processes, comparing APB measures with results from balance-rating and comparison tasks. Results show high between-task correlation, but low correlation with APB. We repeated the rating task, expanding the image set to include five rotations of each image, comparing balance perception of artist and novice participant groups. Rotation has no effect on APB balance computation but dramatically affects balance rating, especially for art experts. We analyze the variety of rotation effects and suggest that, rather than depending on element size and position relative to symmetry axes, first fixation balance processing derives from global processes such as grouping of lines and shapes, object recognition, preference for horizontal and vertical elements, closure, and completion, enhanced by vertical symmetry.


title: Prevalence of dental caries among 12–14year old children in Qatar

abstract: Background ::: To ensure the oral health of a population, clinicians must deliver appropriate dental services, and local communities need to have access to dental care facilities. However, establishment of this infrastructure must be based on reliable information regarding disease prevalence and severity in the target population.


title: Emerging cognitive radio applications: A survey

abstract: Recent developments in spectrum policy and regulatory domains, notably the release of the National Broadband Plan, the publication of final rules for TV white spaces, and the ongoing proceeding for secondary use of the 2360-2400 MHz band for medical body area networks, will allow more flexible and efficient use of spectrum in the future. These important changes open up exciting opportunities for cognitive radio to enable and support a variety of emerging applications, ranging from smart grid, public safety and broadband cellular, to medical applications. This article presents a high-level view on how cognitive radio (primarily from a dynamic spectrum access perspective) would support such applications, the benefits that cognitive radio would bring, and also some challenges that are yet to be resolved. We also illustrate related standardization that uses cognitive radio technologies to support such emerging applications.


title: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

abstract: This article reports an empirical investigation of the accuracy of rules that classify examples on the basis of a single attribute. On most datasets studied, the best of these very simple rules is as accurate as the rules induced by the majority of machine learning systems. The article explores the implications of this finding for machine learning research and applications.


title: DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

abstract: In this work we use deep learning to establish dense correspondences between a 3D object model and an image "in the wild". We introduce "DenseReg", a fully-convolutional neural network (F-CNN) that densely regresses at every foreground pixel a pair of U-V template coordinates in a single feedforward pass. To train DenseReg we construct a supervision signal by combining 3D deformable model fitting and 2D landmark annotations. We define the regression task in terms of the intrinsic, U-V coordinates of a 3D deformable model that is brought into correspondence with image instances at training time. A host of other object-related tasks (e.g. part segmentation, landmark localization) are shown to be by-products of this task, and to largely improve thanks to its introduction. We obtain highly-accurate regression results by combining ideas from semantic segmentation with regression networks, yielding a 'quantized regression' architecture that first obtains a quantized estimate of position through classification, and refines it through regression of the residual. We show that such networks can boost the performance of existing state-of-the-art systems for pose estimation. Firstly, we show that our system can serve as an initialization for Statistical Deformable Models, as well as an element of cascaded architectures that jointly localize landmarks and estimate dense correspondences. We also show that the obtained dense correspondence can act as a source of 'privileged information' that complements and extends the pure landmark-level annotations, accelerating and improving the training of pose estimation networks. We report state-of-the-art performance on the challenging 300W benchmark for facial landmark localization and on the MPII and LSP datasets for human pose estimation.


title: Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints

abstract: We consider the use of energy harvesters, in place of conventional batteries with fixed energy storage, for point-to-point wireless communications. In addition to the challenge of transmitting in a channel with time selective fading, energy harvesters provide a perpetual but unreliable energy source. In this paper, we consider the problem of energy allocation over a finite horizon, taking into account channel conditions and energy sources that are time varying, so as to maximize the throughput. Two types of side information (SI) on the channel conditions and harvested energy are assumed to be available: causal SI (of the past and present slots) or full SI (of the past, present and future slots). We obtain structural results for the optimal energy allocation, via the use of dynamic programming and convex optimization techniques. In particular, if unlimited energy can be stored in the battery with harvested energy and the full SI is available, we prove the optimality of a water-filling energy allocation solution where the so-called water levels follow a staircase function.


title: Customer Churn Prediction Based on SVM-RFE

abstract: As markets become increasingly saturated, churn prediction and management has become of great concern to many industries. A company wishing to retain its customers needs to be able to predict those who are likely to churn and will make those customers the focus of customer retention efforts. Today Customer data has properties of large samples, high dimensions and more noises. In response to the limitations of existing feature selection in churn-prediction, we introduce and experimentally evaluate Support vector machine-recursive feature elimination attribute selection algorithm. It can identify key attributes of customer churn, rule out the related and redundant attributes, and reduce the dimensions of data. It is more important that this algorithm is related with the followed classification learning algorithm, so it can be better integrated in churn prediction. The empirical evaluation results suggest that the proposed feature selection algorithm extracts less key attributes and exhibits better satisfactory predictive effectiveness than other three comparable attribute selection algorithms.


title: Sudden forest canopy collapse corresponding with extreme drought and heat in a mediterranean-type eucalypt forest in southwestern Australia

abstract: Drought and heat-induced forest dieback and mortality are emerging global concerns. Although Mediterranean-type forest (MTF) ecosystems are considered to be resilient to drought and other disturbances, we observed a sudden and unprecedented forest collapse in a MTF in Western Australia corresponding with record dry and heat conditions in 2010/2011. An aerial survey and subsequent field investigation were undertaken to examine: the incidence and severity of canopy dieback and stem mortality, associations between canopy health and stand-related factors as well as tree species response. Canopy mortality was found to be concentrated in distinct patches, representing 1.5 % of the aerial sample (1,350 ha). Within these patches, 74 % of all measured stems (>1 cm DBHOB) had dying or recently killed crowns, leading to 26 % stem mortality six months following the collapse. Patches of canopy collapse were more densely stocked with the dominant species, Eucalyptus marginata, and lacked the prominent midstorey species Banksia grandis, compared to the surrounding forest. A differential response to the disturbance was observed among co-occurring tree species, which suggests contrasting strategies for coping with extreme water stress. These results suggest that MTFs, once thought to be resilient to climate change, are susceptible to sudden and severe forest collapse when key thresholds have been reached.


title: Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining

abstract: This paper proposes a two-stage methodology for online identification of power system dynamic signature using phasor measurement unit (PMU) measurements and data mining. Only transient stability status is usually predicted in the literature to assist with corrective control, without the dynamic behavior of generators in the event of instability. This paper uses traditional binary classification to identify transient stability in the first stage, and then develops a novel methodology to predict the nature of unstable dynamic behavior in the second stage. The method firstly applies hierarchical clustering to define patterns of unstable dynamic behavior of generators, and then applies different multiclass classification techniques, including decision tree, ensemble decision tree and multiclass support vector machine to identify characterized unstable responses. The proposed methodology is demonstrated on a multi-area transmission test system. High prediction accuracy at both stages of identification is demonstrated.


title: An Unsupervised Approach of Paraphrase Discovery from Large Crime Corpus

abstract: Massive crime reports often comprises valuable structured information regarding crime pattern but manual processing of these massive dataset is quite strenuous and error-prone. These huge dataset can be best exploited by identifying relational clusters of named entities from the crime reports. But often the clusters contain phrases not defining the same relation as the relational characterisation of the whole cluster. Therefore, paraphrasing is performed to filter out those phrases not defining the same relation. Paraphrases are mostly the phrases that reflect the same context in different articulations. Discovering paraphrases from a large corpus is a demanding task for various applications of natural language processing and researchers have been working on it since long time. But none have taken an attempt to perform the paraphrasing task on crime data. In order to deal with the perplexity of the phrases, the present work proposes an unsupervised approach for recognising synonymous phrases or paraphrases from an untagged crime corpus. This work mainly emphasises on the sentences that comprises two entities and each entity pair from different domain is represented as shallow parse tree. The head word from each parsing tree depicts the actual meaning of the phrase and all the phrases with the same headword have been accumulated for each domain of entity pairs. However, many phrases exist that reflects the same meaning without sharing the same headword. So, the objective is to cluster these phrases defining the same meaning by using an agglomerative hierarchical clustering technique. The method presented in this work is an unsupervised approach and it does not need any kind of training samples to work with.


title: Stochastic Separation Theorems

abstract: Abstract The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples where the system works properly. We demonstrate that in (moderately) high dimension this separation could be achieved with probability close to one by linear discriminants. Based on fundamental properties of measure concentration, we show that for M a exp ( b n ) random M -element sets in R n are linearly separable with probability p , p > 1 − ϑ , where 1 > ϑ > 0 is a given small constant. Exact values of a , b > 0 depend on the probability distribution that determines how the random M -element sets are drawn, and on the constant ϑ . These stochastic separation theorems provide a new instrument for the development, analysis, and assessment of machine learning methods and algorithms in high dimension. Theoretical statements are illustrated with numerical examples.


A Matlab toolbox for musical feature extraction from audio, DAFx


title: A framework of extracting multi-scale features using multiple convolutional neural networks

abstract: Most works related to convolutional neural networks (CNN) use the traditional CNN framework which extracts features in only one scale. We propose multi-scale convolutional neural networks (MSCNN) which can not only extract multi-scale features but also solve the issues of the previous methods which use CNN to extract multi-scale features. With the assumption of label-inheritable (LI) property, we also propose a method to generate exponentially more training examples for MSCNN from the given training set. Our experimental results show that MSCNN outperforms both the state-of-the-art methods and the traditional CNN framework on artist, artistic style, and architectural style classification, supporting that MSCNN outperforms the traditional CNN framework on the tasks which at least partially satisfy LI property.


A novel method for Rapid 3 D fat and water decomposition using a GlObally Optimal multi-surface Estimation ( R-GOOSE )


Thomistic Common Nature and Platonic Idea


title: User Access Management Based on Network Pricing for Social Network Applications

abstract: Social applications play a very important role in people's lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network's income.


title: Millimeter-wave phased-array antennas

abstract: Millimeter-wave band is the general trend for new communication, tracking radar and automotive systems. The crowd of RF frequency bands, the compactness of higher frequency radiating systems and the commercial availability of MMW components drive even more communications, tracking radars and automotive systems toward millimeter-wave band. Moreover, the antenna technology evolves from mechanical to full electronic systems and then phased-arrays are more and more widely employed for tracking radars and automotive systems. According to the general cost reduction trend, this paper describes some configuration of MMW phased-array antenna and presents theoretical and experimental results of a first demonstrator of passive phased-array designed in the frame of a research program funded by the Italian Minister of University and Science Research.


title: The design of an intelligent soccer-playing robot

abstract: Purpose – The purpose of this paper is to design intelligent robots operating in such dynamic environments like the RoboCup Middle-Size League (MSL). In the RoboCup MSL, two teams of five autonomous robots play on an 18- × 12-m field. Equipped with sensors and on-board computers, each robot should be able to perceive the environment, make decision and control itself to play the soccer game autonomously. Design/methodology/approach – This paper presents the design of our soccer robots, participating in RoboCup MSL. The mechanical platform, electrical architecture and software framework are discussed separately. The mechanical platform is designed modularly, so easy maintainability is achieved; the electronic architecture is built on industrial standards using PC-based control technique, which results in high robustness and reliability during the intensive and fierce MSL games; the software is developed upon the open-source Robot Operating System (ROS); thus, the advantages of ROS such as modularity, portab...


title: A deep learning architecture for brain tumor segmentation in MRI images

abstract: With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and sizes. We propose a novel yet simple fully convolutional network (FCN) which results in competitive performance and faster runtime than state-of-the-art model. Using the database provided for the Brain Tumor Segmentation (BraTS) challenge by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, we are able to achieve dice scores of 0.83 in the whole tumor region, 0.75 in the core tumor region and 0.72 in the enhancing tumor region, while our method is about 18 times faster than the state-of-the-art.


title: An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

abstract: This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.


Neural Variational Inference For Topic Models


title: Usability measurement in context

abstract: Abstract Different approaches to the measurement of usability are reviewed and related to definitions of usability in international standards. It is concluded that reliable measures of overall usability can only be obtained by assessing the effectiveness, efficiency and satisfaction with which representative users carry out representative tasks in representative environments.This requires a detailed understanding of the context of use of a product. The ESPRIT MUSiC project has developed tools which can be used to measure usability in the laboratory and the field. An overview is given of the methods and tools for measuring user performance, cognitive workload, and user perceived quality.


Data concerning the effect of plyometric training on jump performance in soccer players: A meta-analysis


Prenatal diagnosis of Pallister‐Killian syndrome in one twin


title: Consumption of a high-fat meal containing cheese compared with a vegan alternative lowers postprandial C-reactive protein in overweight and obese individuals with metabolic abnormalities: a randomised controlled cross-over study

abstract: Dietary recommendations suggest decreased consumption of SFA to minimise CVD risk; however, not all foods rich in SFA are equivalent. To evaluate the effects of SFA in a dairy food matrix, as Cheddar cheese, v. SFA from a vegan-alternative test meal on postprandial inflammatory markers, a randomised controlled cross-over trial was conducted in twenty overweight or obese adults with metabolic abnormalities. Individuals consumed two isoenergetic high-fat mixed meals separated by a 1- to 2-week washout period. Serum was collected at baseline, and at 1, 3 and 6 h postprandially and analysed for inflammatory markers (IL-6, IL-8, IL-10, IL-17, IL-18, TNFα, monocyte chemotactic protein-1 (MCP-1)), acute-phase proteins C-reactive protein (CRP) and serum amyloid-A (SAA), cellular adhesion molecules and blood lipids, glucose and insulin. Following both high-fat test meals, postprandial TAG concentrations rose steadily (P < 0·05) without a decrease by 6 h. The incremental AUC (iAUC) for CRP was significantly lower (P < 0·05) in response to the cheese compared with the vegan-alternative test meal. A treatment effect was not observed for any other inflammatory markers; however, for both test meals, multiple markers significantly changed from baseline over the 6 h postprandial period (IL-6, IL-8, IL-18, TNFα, MCP-1, SAA). Saturated fat in the form of a cheese matrix reduced the iAUC for CRP compared with a vegan-alternative test meal during the postprandial 6 h period. The study is registered at clinicaltrials.gov under NCT01803633.


title: The Internet of Things in Healthcare: Potential Applications and Challenges

abstract: Exciting new applications of Internet of Things (IoT) technology are arising, particularly in healthcare, where the leveraging effects can significantly improve patients' well-being while alleviating the problem of scarce resources. But the hype around these applications far outpaces the reality. Furthermore, there is a real risk that these leveraging technologies will disassociate caregivers from patients, potentially resulting in a loss of caring. In this article, the authors review some of the most promising applications for IoT in healthcare and the significant challenges ahead.


title: Delayed transient post-traumatic quadriplegia.

abstract: Transient neurological deficit following cervical trauma have been reported following sports injuries, and has been referred to as cervical cord neurapraxia. The so-called "whiplash injuries" following minor motor vehicle collisions usually do not produce any neurological deficit. Here we report the case of a whiplash type of injury presenting with a delayed onset neurological deficit, which was followed by rapid and complete recovery. The patient, an otherwise healthy 34-year-old male, attended the emergency department of Sultan Qaboos University Hospital following a rear-end motor vehicle collision. We present images showing degenerative disc disease causing spinal canal narrowing and mild cord compression in the patient, but no spinal instability. Differential diagnoses are also discussed.


title: Problem formulations and solvers in linear SVM: a review

abstract: Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Weston Watkins SVM and directed acyclic graph SVM (DAGSVM) etc. SVM with a linear Kernel is called linear SVM and one with a non-linear Kernel is called non-linear SVM. Linear SVM is an efficient technique for high dimensional data applications like document classification, word-sense disambiguation, drug design etc. because under such data applications, test accuracy of linear SVM is closer to non-linear SVM while its training is much faster than non-linear SVM. SVM is continuously evolving since its inception and researchers have proposed many problem formulations, solvers and strategies for solving SVM. Moreover, due to advancements in the technology, data has taken the form of ‘Big Data’ which have posed a challenge for Machine Learning to train a classifier on this large-scale data. In this paper, we have presented a review on evolution of linear support vector machine classification, its solvers, strategies to improve solvers, experimental results, current challenges and research directions.


title: Relax with CouchDB — Into the non-relational DBMS era of bioinformatics

abstract: With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services.


title: The effect of music background on the emotional appraisal of film sequences

abstract: In this study the effects of musical background on the emotional appraisal of ::: film sequences was investigated. Four pairs of polar emotions defined in ::: Plutchik’s model were used as basic emotional qualities: joy-sadness, ::: anticipation-surprise, fear-anger, and trust disgust. In the preliminary ::: study eight film sequences and eight music themes were selected as the best ::: representatives of all eight Plutchik’s emotions. In the main experiment the ::: participant judged the emotional qualities of film-music combinations on ::: eight seven-point scales. Half of the combinations were congruent (e.g. ::: joyful film - joyful music), and half were incongruent (e.g. joyful film - ::: sad music). Results have shown that visual information (film) had greater ::: effects on the emotion appraisal than auditory information (music). The ::: modulation effects of music background depend on emotional qualities. In some ::: incongruent combinations (joysadness) the modulations in the expected ::: directions were obtained (e.g. joyful music reduces the sadness of a sad ::: film), in some cases (anger-fear) no modulation effects were obtained, and in ::: some cases (trust-disgust, anticipation-surprise) the modulation effects were ::: in an unexpected direction (e.g. trustful music increased the appraisal of ::: disgust of a disgusting film). These results suggest that the appraisals of ::: conjoint effects of emotions depend on the medium (film masks the music) and ::: emotional quality (three types of modulation effects).


title: Understanding the sensory experiences of young people with autism spectrum disorder: A preliminary investigation

abstract: Background/aim: Unusual responses to sensations can impact upon the daily activities for individuals with autism spectrum disorder. Current understandings of these sensory experiences have been drawn from the proxy reports from parents/caregivers, standardised self-report questionnaires and autobiographical accounts. As sensory experiences are intensely personal, the first-hand accounts of people with autism spectrum disorder may have greater validity than caregiver reports, but these have never been systematically researched. This study explored the utility of using a semi-structured interview protocol augmented with visual cues to facilitate our understanding of the way people with autism spectrum disorder experience sensory input, and use coping strategies to manage sensory issues that interfere with participation. Method: A semi-structured interview augmented by visual cues was used to investigate the sensory experiences of three adolescent males with autism spectrum disorder. As is common in this population, two of the participants also had a diagnosis of Attention Deficit Hyperactivity Disorder and were taking stimulant medication. Results: Participants shared a preference for expected, predictable and controllable sensory input, whereas unexpected, unpredictable and uncontrollable sensations were perceived as unpleasant. A heightened awareness of and difficulty filtering extraneous sensory input, high levels of movement seeking and an over-focus on salient sensory input were also described. Strategies employed to manage sensory challenges included avoiding, increasing predictability and control and meta-cognitive adaptations. Conclusions: Further research involving a larger sample of participants is recommended to determine the utility of using a semi-structured interview protocol augmented with visual cues to understand the sensory experiences of individuals with high-functioning autism spectrum disorder.


title: Restriction in Vκ Gene Use and Antigen Selection in Anti-Myeloperoxidase Response in Mice

abstract: Anti-neutrophil cytoplasmic Abs, directed primarily toward myeloperoxidase (MPO) and proteinase 3, are detected in the majority of patients with distinct forms of small vessel vasculitides and pauci-immune necrotizing glomerulonephritis. However, the origin of these autoantibodies remains unknown. We studied the V region gene use in murine anti-MPO Abs derived from Spontaneous Crescentic Glomerulonephritis/Kinjoh mice. A total of 13 anti-MPO-producing hybridomas were generated from four unimmunized mice. Ten of the 13 hybridomas (corresponding to 3 of 4 clones) expressed Vκ1C but differed in their use of VH genes. The remaining three hybridomas expressed a Vκ5 gene. Anti-MPO hybridomas from individual mice were derived from single clones as deduced by sequence similarity and splice-site identity. We found a statistically significant bias of amino acid replacement mutations to the complementarity-determining regions (CDR) in the Vκ1C-expressing hybridomas. Intriguingly, all 10 Vκ1C hybridomas share a lysine to glutamate mutation in the CDR1. To determine the effects of somatic V gene mutations on binding to MPO, we generated an anti-MPO Ab with an unmutated Vκ1C L chain and compared its ability to bind MPO with its mutated counterpart. The mutated hybridoma-derived Ab has a 4.75-fold higher avidity for MPO than the unmutated Ab. These results suggest that: 1) the L chain plays a dominant role in determining Ab specificity to MPO, 2) the anti-MPO Ab response is oligoclonal, consistent with Ag selection, and 3) MPO is a driving Ag in the murine anti-MPO Ab response.


title: MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation

abstract: Interactive segmentation, in which a user provides a bounding box to an object of interest for image segmentation, has been applied to a variety of applications in image editing, crowdsourcing, computer vision, and medical imaging. The challenge of this semi-automatic image segmentation task lies in dealing with the uncertainty of the foreground object within a bounding box. Here, we formulate the interactive segmentation problem as a multiple instance learning (MIL) task by generating positive bags from pixels of sweeping lines within a bounding box. We name this approach MILCut. We provide a justification to our formulation and develop an algorithm with significant performance and efficiency gain over existing state-of-the-art systems. Extensive experiments demonstrate the evident advantage of our approach.


title: High resolution sparse voxel DAGs

abstract: We show that a binary voxel grid can be represented orders of magnitude more efficiently than using a sparse voxel octree (SVO) by generalising the tree to a directed acyclic graph (DAG). While the SVO allows for efficient encoding of empty regions of space, the DAG additionally allows for efficient encoding of identical regions of space, as nodes are allowed to share pointers to identical subtrees. We present an efficient bottom-up algorithm that reduces an SVO to a minimal DAG, which can be applied even in cases where the complete SVO would not fit in memory. In all tested scenes, even the highly irregular ones, the number of nodes is reduced by one to three orders of magnitude. While the DAG requires more pointers per node, the memory cost for these is quickly amortized and the memory consumption of the DAG is considerably smaller, even when compared to an ideal SVO without pointers. Meanwhile, our sparse voxel DAG requires no decompression and can be traversed very efficiently. We demonstrate this by ray tracing hard and soft shadows, ambient occlusion, and primary rays in extremely high resolution DAGs at speeds that are on par with, or even faster than, state-of-the-art voxel and triangle GPU ray tracing.


Hierarchical Multi-Label Classification Networks


Learning to Align across Languages : Toward Multilingual FrameNet


title: Effect of Working Memory Updating Training on Retrieving Symptoms of Children With Learning Disabilities

abstract: Working memory (WM) deficiency is a primary reason for the poor academic performance of children with learning disabilities (LDs). Studies have shown that the WM of typical children could be improved through training, and WM training contributes to improving their fluid intelligence and academic achievement. However, few studies have investigated WM training for children with LDs, and results have been inconsistent. The present study examined the long-term effects of WM updating training and whether it can mitigate LD symptoms. Fifty-four children with LDs were recruited and divided randomly into a training or control group. The training group underwent adaptive running WM training for 20 days. Before and after training, the 2 groups completed a 2-back task, a digit span task (forward and backward), Raven’s Standard Progressive Matrices test, and a scholastic attainment test (Chinese and math). The tests were repeated 6 months later. The results showed that, as compared with the controls, the training gro...


Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification


Unsupervised Learning of Multi-Level Descriptors for Person Re-Identification.


title: ON GRAPHS PRESERVING RECTILINEAR SHORTEST PATHS IN THE PRESENCE OF OBSTACLES

abstract: In physical VLSI design, network design (wiring) is the most time-consuming phase. For solving global wiring problems, we propose to first compute from the layout geometry a graph that preserves all shortest paths between pairs of relevant points, and then to operate on that graph for computing shortest paths, Steiner minimal tree approximations, or the like. For a set of points and a set of simple orthogonal polygons as obstacles in the plane, withn input points (polygon corner or other) altogether, we show how a shortest paths preserving graph of sizeO(n logn) can be computed in timeO(n logn) in the worst case, with spaceO(n). We illustrate the merits of this approach with a simple example: If the length of a longest edge in the graph is bounded by a polynomial inn, an assumption that is clearly fulfilled for graphs derived from VLSI layout geometries, then a shortest path can be computed in timeO(n logn log logn) in the worst case; this result improves on the known best one ofO(n(logn)3/2).


Handing DVFS to Hardware : Using Power Capping to Control Software Performance


title: Simple Additive Weighting approach to Personnel Selection problem

abstract: Selection of qualified personnel is a key success factor for an organization. The complexity and importance of the problem call for analytical methods rather than intuitive decisions. In literature, there are various methods regarding personnel selection. This paper considers a real application of personnel selection with using the opinion of expert by one of the decision making model, it is called SAW method. This paper has applied seven criteria that they are qualitative and positive for selecting the best one amongst five personnel and also ranking them. Finally the introduced method is used in a case study. Personnel selection directly and significantly affects the quality of employees, and hence, it has always been an important topic for organizations, including public agencies and private enterprises. Various approaches have been developed to help organizations make best personnel selection decisions to place the right people in the right jobs. Many studies have reported a positive association between various human resources practices and objective and perceptual measures of selecting human resources, some authors have expressed concern that results may be biased because of methodological problems. Traditional methods for selection of human resources are mostly based on statistical analyses of test scores that are treated as accurate reflections of reality. Modern approaches, however, recognize that selection is a complex process that involves a significant amount of vagueness and subjectivity (1). In general, personnel selection, depending on the firm"s specific targets, the availability of means and the individual preferences of the decision makers (DMs), is a highly complex problem. The multi criteria nature of the problem makes Multi- Criteria Decision Making (MCDM) methods and copes with this, given that they consider many criteria at the same time, with various weights and thresholds, having


title: SLING: A framework for frame semantic parsing

abstract: We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.


title: Postprocessing For Edge Enhancement In Low Bit Rate Coded Images

abstract: Low bit rate image coding results in artifacts at reconstruction whose characteristics depend on the form of the coding/decoding algorithm used. A very common, and visually objectionable effect of most coding methods is distortion of edges. Classical spatially-invariant filtering techniques are of little use in removing this signal-dependent reconstruction error. This paper presents an algorithm for locating and tracing perceptually important edges, and for enhancement through nonstationary filtering. Edge detection and enhancement are reduced to a sequence of simple one-dimensional processes. The algorithm yields encouraging experimental results when applied to images coded by the block list transform and vector quantization.© (1988) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network

abstract: We propose an efficient and accurate head orientation estimation algorithm using a monocular camera. Our approach is leveraged by deep neural network and we exploit the architecture in a data regression manner to learn the mapping function between visual appearance and three dimensional head orientation angles. Therefore, in contrast to classification based approaches, our system outputs continuous head orientation. The algorithm uses convolutional filters trained with a large number of augmented head appearances, thus it is user independent and covers large pose variations. Our key observation is that an input image having \(32 \times 32\) resolution is enough to achieve about 3 degrees of mean square error, which can be used for efficient head orientation applications. Therefore, our architecture takes only 1 ms on roughly localized head positions with the aid of GPU. We also propose particle filter based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.


ENGAGING IMAGE CAPTIONING


title: Integrating driving behavior and traffic context through signal symbolization

abstract: This paper presents a novel method for integrating driving behavior and traffic context through signal symbolization in order to summarize driving semantics from sensor outputs. The method has been applied to risky lane change detection. Language models (nested Pitman-Yor language model) and speech recognition algorithms (hidden Markov Model) have been utilized for converting continuous sensor signals into a sequence of non-uniform segments (chunks). After symbolization, Latent Dirichlet Allocation (LDA) is used to integrate the symbolized driving behavior and the surrounding vehicle information for establishing the semantics of the driving scene. 988 lane changes of real-world highway driving are used for the evaluation. Risk level of each lane change rated by 10 subjects are used as ground truth. Best results have been obtained when driving behavior and surrounding vehicle information are integrated through co-occurrence chunking after independent symbolization of behavior and context signals.


Intrinsic Motivation in Museums : Why Does One Want to Learn ?


title: Parallel-Coupled Coplanar-Waveguide Bandpass Filter With Multiple Transmission Zeros

abstract: A compact parallel-coupled coplanar-waveguide bandpass filter with good selectivity and stopband response is proposed. By using the quarter-wavelength resonators in implementation, the filter size can be reduced and no spurious passband is observed at twice the center frequency (2f0). In addition, by suitably introducing the capacitive and inductive cross-coupling effects, four transmission zeros around the passband edges may be created for improving the filter selectivity. For design purpose, the equivalent-circuit models are also established. In this study, the compact fourth-order filter with multiple transmission zeros for improving the selectivity is implemented, and its stopband is extended up to 3.43f0 with a rejection better than -30dB


title: On Corrsin equation closure

abstract: Corrsin equation closure is done using the gradient hypothesis relating a two-point third-order correlation moment to a two-point second-order correlation function of a passive scalar field. A numerical locally isotropic turbulence model based on a closed system of Kolmogorov and Yaglom equations is constructed. A similarity solution of the Corrsin equation, which corresponds to infinitely high Reynolds and Peclet numbers, is constructed under assumption of constancy of Corrsin and Loitsiansky invariants. A numerical model of turbulence dynamics and temperature fluctuations behind a heated grid in a wind tunnel, which is based on Karman-Howarth and Corrsin closed equations, is developed.


title: Index selection for OLAP

abstract: On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional "data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked first, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource-space-their selection should be done together for the most efficient use of space. The authors give algorithms that automate the selection of summary tables and indexes. In particular, they present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and they show that an algorithm of moderate complexity can perform fairly close to the optimal.


title: Test and simulation of axial flux-motor characteristics for hybrid electric vehicles

abstract: High power density is the main advantage of the axial flux motors compared to the conventional radial flux motors. Therefore, they are suitable candidates for the power train of hybrid electric vehicles. In addition, operating speed range and efficiency of axial flux motors can be improved by changing the air gap of the machine. In this paper, for each operating point, the optimal air gap is estimated based on the measured efficiency at different air gaps. The motor model is developed using the estimated optimal air gap and efficiency. Vehicle performance with the axial flux motor is also simulated based on the developed motor/controller model. Furthermore, the actual motor performance in a typical vehicle environment is analyzed with a vehicle simulator by applying the concept of hardware in the loop without installing the motor in the vehicle. Actual motor performance is compared with the simulation results as well. Motor performance in different drive cycles is also tested. The test motor can be used for the drivetrain of a series hybrid electric vehicle.


Efficient Rendering of Anisotropic Surfaces Using Computer Graphics Hardware


title: Novel Coupling Matrix Synthesis for Single-Layer Substrate-Integrated Evanescent-Mode Cavity Tunable Bandstop Filter Design

abstract: A new technique for designing tunable bandstop filters is presented. A novel coupling matrix synthesis method for this type of bandstop filter is shown. Phase cancellation, through combining two bandpass filters, is used to derive the coupling matrix. Therefore, classical coupling mechanisms utilized to implement bandpass filters can be used to design and realize bandstop filters. Using this method, bandstop filters can be designed without source-to-load coupling. This eliminates the need for a second substrate when compared to previously reported bandstop filters implemented using substrate-integrated evanescent-mode cavity technology, though the method itself is quite general. Finally, examples of tunable bandstop filters in the range from 3.0 to 3.6 GHz are demonstrated to verify the proposed method.


title: Unsupervised document structure analysis of digital scientific articles

abstract: Text mining and information retrieval in large collections of scientific literature require automated processing systems that analyse the documents’ content. However, the layout of scientific articles is highly varying across publishers, and common digital document formats are optimised for presentation, but lack structural information. To overcome these challenges, we have developed a processing pipeline that analyses the structure a PDF document using a number of unsupervised machine learning techniques and heuristics. Apart from the meta-data extraction, which we reused from previous work, our system uses only information available from the current document and does not require any pre-trained model. First, contiguous text blocks are extracted from the raw character stream. Next, we determine geometrical relations between these blocks, which, together with geometrical and font information, are then used categorize the blocks into different classes. Based on this resulting logical structure we finally extract the body text and the table of contents of a scientific article. We separately evaluate the individual stages of our pipeline on a number of different datasets and compare it with other document structure analysis approaches. We show that it outperforms a state-of-the-art system in terms of the quality of the extracted body text and table of contents. Our unsupervised approach could provide a basis for advanced digital library scenarios that involve diverse and dynamic corpora.


title: Optimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data

abstract: Private query processing on encrypted databases allows users to obtain data from encrypted databases in such a way that the users’ sensitive data will be protected from exposure. Given an encrypted database, users typically submit queries similar to the following examples: 1) How many employees in an organization make over U.S. $100000? 2) What is the average age of factory workers suffering from leukemia? Answering the questions requires one to search and then compute over the relevant encrypted data sets in sequence. In this paper, we are interested in efficiently processing queries that require both operations to be performed on fully encrypted databases. One immediate solution is to use several special-purpose encryption schemes simultaneously; however, this approach is associated with a high computational cost for maintaining multiple encryption contexts. Another solution is to use a privacy homomorphic scheme. However, no secure solutions have been developed that satisfy the efficiency requirements. In this paper, we construct a unified framework to efficiently and privately process queries with search and compute operations. For this purpose, the first part of our work involves devising several underlying circuits as primitives for queries on encrypted data. Second, we apply two optimization techniques to improve the efficiency of these circuit primitives. One technique involves exploiting single-instruction-multiple-data (SIMD) techniques to accelerate the basic circuit operations. Unlike general SIMD approaches, our SIMD implementation can be applied even to a single basic operation. The other technique is to use a large integer ring (e.g., $\mathbb {Z}_{2^{t}}$ ) as a message space rather than a binary field. Even for an integer of $k$ bits with $k>t$ , addition can be performed using degree 1 circuits with lazy carry operations. Finally, we present various experiments performed by varying the considered parameters, such as the query type and the number of tuples.


title: Auto-Encoding Total Correlation Explanation

abstract: Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned. We propose an information-theoretic approach to characterizing disentanglement and dependence in representation learning using multivariate mutual information, also called total correlation. The principle of Total Cor-relation Ex-planation (CorEx) has motivated successful unsupervised learning applications across a variety of domains but under some restrictive assumptions. Here we relax those restrictions by introducing a flexible variational lower bound to CorEx. Surprisingly, we find this lower bound is equivalent to the one in variational autoencoders (VAE) under certain conditions. This information-theoretic view of VAE deepens our understanding of hierarchical VAE and motivates a new algorithm, AnchorVAE, that makes latent codes more interpretable through information maximization and enables generation of richer and more realistic samples.


title: Context dependent recurrent neural network language model

abstract: Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.


title: Factors influencing Internet shopping value and customer repurchase intention

abstract: This research empirically examines the effect of various Internet shopping site qualities on the utilitarian and hedonic values of Internet shopping. The influence of the perceived level of Internet shopping value on customer satisfaction and repurchase intention is also investigated. We perform structural equation analysis with a sample of 293 observations consisting of two different income groups (workforce and student). Our results show that while system and service qualities are critical factors affecting utilitarian shopping value, information and service qualities are the factors most closely associated with hedonic shopping value. These findings suggest that service quality plays a significant role in increasing both utilitarian and hedonic shopping values. Our results also show that the impact of quality factors on Internet shopping values and subsequent repurchase intention differs across the two income groups.


title: Towards Adaptive Social Behavior Generation for Assistive Robots Using Reinforcement Learning

abstract: In this paper we explore whether a social robot can learn, in and from a task-oriented interaction with a human user, how to employ different social behaviors to achieve interactional goals under specific situational circumstances. We present a multimodal behavior generation architecture that maps high-level interactional functions and behaviors onto low-level behaviors executable by a robot. While high-level behaviors are selected based on the state of the user as well as the interaction, reinforcement learning is used within each behavior to optimize its local mapping onto lower-level behaviors. The approach is implemented and applied in a scenario in which a social robot (Furhat) assists a human player in solving a Memory game by guiding the attention of the user to target objects. Results of an evaluation study demonstrate that participants are able to solve the Memory faster with the adaptive, assistive robot.


title: Blind forensics of contrast enhancement in digital images

abstract: Digital images have seen increased use in applications where their authenticity is of prime importance. This proves to be problematic due to the widespread availability of digital image editing software. As a result, there is a need for the development of reliable techniques for verifying an image's authenticity. In this paper, a blind forensic algorithm is proposed for detecting the use of global contrast enhancement operations to modify digital images. Furthermore, a separate algorithm is proposed to identify the use of histogram equalization, a commonly implemented contrast enhancement operation. Both algorithms perform detection by seeking out unique artifacts introduced into an image's histogram as a result of the particular operation examined. Additionally, results are presented showing the effectiveness of both proposed algorithms.


Pro- and Anti-oxidant Properties of Redox-Active Catechol-Chitosan Films


title: Current Harmonics Cancellation in Three-Phase Four-Wire Systems by Using a Four-Branch Star Filtering Topology

abstract: This paper presents a new solution for filtering current harmonics in three-phase four-wire networks. The original four-branch star (FBS) filter topology presented in this paper is characterized by a particular layout of single-phase inductances and capacitors, without using any transformer or special electromagnetic device. Via this layout, a power filter, with two different and simultaneous resonance frequencies and sequences, is achieved-one frequency for positive-/negative-sequence and another one for zero-sequence components. This filter topology can work either as a passive filter, when only passive components are employed, or as a hybrid filter, when its behavior is improved by integrating a power converter into the filter structure. The paper analyzes the proposed topology, and derives fundamental concepts about the control of the resulting hybrid power filter. From this analysis, a specific implementation of a three-phase four-wire hybrid power filter is presented as an illustrative application of the filtering topology. An extensive evaluation using simulation and experimental results from a DSP-based laboratory prototype is conducted in order to verify and validate the good performance achieved by the proposed FBS passive/hybrid power filter.


Managers and leaders: are they different?


title: The Spanish adaptation of ANEW (affective norms for English words).

abstract: This article presents the Spanish adaptation of the Affective Norms for English Words (ANEW; Bradley & Lang, 1999). The norms are based on 720 participants’ assessments of the translation into Spanish of the 1,034 words included in the ANEW. The evaluations were done in the dimensions of valence, arousal and dominance using the Self-Assessment Manikin (SAM). Apart from these dimensions, five objective (number of letters, number of syllables, grammatical class, frequency and number of orthographic neighbors) and three subjective (familiarity, concreteness and imageability) psycholinguistic indexes are included. The Spanish adaptation of ANEW can be downloaded at www.psychonomic.org.


APPLICATION OF EXPERT SYSTEM AND MACHINE LEARNING APPROACH TO INTELLIGENT MAN-MACHINE INTERFACE


title: A Bayesian sampling approach to in-door localization of wireless devices using received signal strength indication

abstract: This paper describes a probabilistic approach to global localization within an in-door environment with minimum infrastructure requirements. Global localization is a flavor of localization in which the device is unaware of its initial position and has to determine the same from scratch. Localization is performed based on the received signal strength indication (RSSI) as the only sensor reading, which is provided by most off-the-shelf wireless network interface cards. Location and orientation estimates are computed using Bayesian filtering on a sample set derived using Monte-Carlo sampling. Research leading to the proposed method is outlined along with results and conclusions from simulations and real life experiments.


Influence of lane discipline on Delay at Rail-Road Crossing


Towards a new approximate matching function for digital forensics investigations


title: Actuator Control for the NASA-JSC Valkyrie Humanoid Robot: A Decoupled Dynamics Approach for Torque Control of Series Elastic Robots

abstract: This paper discusses the actuator-level control of Valkyrie, a new humanoid robot designed by NASA's Johnson Space Center in collaboration with several external partners. Several topics pertaining to Valkyrie's series elastic actuators are presented including control architecture, controller design, and implementation in hardware. A decentralized approach is taken in controlling Valkyrie's many series elastic degrees of freedom. By conceptually decoupling actuator dynamics from robot limb dynamics, the problem of controlling a highly complex system is simplified and the controller development process is streamlined compared to other approaches. This hierarchical control abstraction is realized by leveraging disturbance observers in the robot's joint-level torque controllers. A novel analysis technique is applied to understand the ability of a disturbance observer to attenuate the effects of unmodeled dynamics. The performance of this control approach is demonstrated in two ways. First, torque tracking performance of a single Valkyrie actuator is characterized in terms of controllable torque resolution, tracking error, bandwidth, and power consumption. Second, tests are performed on Valkyrie's arm, a serial chain of actuators, to demonstrate the robot's ability to accurately track torques with the presented decentralized control approach.


title: Confirmatory Factor Analyses of the Stanford-Binet Intelligence Scale, Fourth Edition

abstract: This study presents results of confirmatory factor analyses of the recently published Fourth Edition of the Stanford-Binet Intelligence Scale. Four possible models of the scale's factor structure were tested with confirmatory factor analysis via LISREL at five age levels and for the standardiza tion sample as a whole. Results of these analyses show that although the four-factor model presented by the scale's authors fits the general standardiza tion data fairly well, it did not fit similarly well with data from several age groups. Overall, however, models based on the authors' theoretical structure were usually significantly better fitting to empirical data than were models with fewer fac tors. Results are discussed in terms of their im plications for interpretation and use of the Stanford-Binet, Fourth Edition with young children.


title: AutoRec: A Recommender System Based on Social Media Stream

abstract: In this study, we employed Collaborative Filtering and Sentiment Analysis. This is to automatically produce a recommendation to consumers without requiring them to input ratings and feedback's. The researcher will be using Social media stream as test-bed which was currently being utilized by researchers around the globe to generate information which are useful in different fields. TwitterAPI will be employed to gather real time tweets to be stored in a database for analysis. Framework will be introduced to automatically produce a real time recommendation, which aims to test the effectiveness of the framework proposed by virtue of using DeLone and McClean IS Success Model. As a result the prototype datasets crawled from real world social media stream demonstrates that the proposed method depicts to be constructive. This study can contribute to the growing research projects on recommender system, data mining and to other relevant techniques.


title: Trust-Related Arguments in Internet Stores: A Framework for Evaluation

abstract: This paper discusses the trust related issues and arguments (evidence) Internet stores need to provide in order to increase consumer trust. Based on a model of trust from academic literature, in addition to a model of the customer service life cycle, the paper develops a framework that identifies key trust-related issues and organizes them into four categories: personal information, product quality and price, customer service, and store presence. It is further validated by comparing the issues it raises to issues identified in a review of academic studies, and to issues of concern identified in two consumer surveys. The framework is also applied to ten well-known web sites to demonstrate its applicability. The proposed framework will benefit both practitioners and researchers by identifying important issues regarding trust, which need to be accounted for in Internet stores. For practitioners, it provides a guide to the issues Internet stores need to address in their use of arguments. For researchers, it can be used as a foundation for future empirical studies investigating the effects of trust -related arguments on consumers ’ trust in Internet stores.


title: Equalization and clock recovery for a 2.5-10Gb/s 2-PAM/4-PAM backplane transceiver cell

abstract: A backplane transceiver uses a folded 5-tap TX equalizer and 5-tap RX equalizer to counteract losses and reflections. A flexible 2-PAM/4-PAM CDR uses select transitions for receive clock recovery. BER better than 10/sup -15/ at 10 Gb/s and power <60 mW/Gb are measured values for a 16" backplane with two high speed connectors.


title: How feasible is the use of magnetic field alone for indoor positioning?

abstract: The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.


title: Delta DLP 3D printing with large size

abstract: We present a delta DLP 3D printer with large size in this paper. Compared with traditional DLP 3D printers that use a low-cost off-the-shelf consumer projector and a single vertical carriage, the platform of our delta DLP 3D printer can also move horizontally in the plane. We show that this structure allows the printer to have a larger printing area than the projection area of a projector. Our system can print 3D models much larger than traditional DLP 3D printers. The major challenge to realize delta 3D printing with large size comes from how to partition an arbitrary planar polygonal shape (possibly with holes or multiple disjoint polygons) into a minimum number of rectangles with fixed size, which is NP-hard. We propose a simple yet efficient approximation algorithm to solve this problem. The time complexity of our algorithm is O(n3 log n), where n is the number of edges in the polygonal shape. A physical prototype system is built and several large 3D models with complex geometric structures have been printed as examples to demonstrate the effectiveness of our approach.


title: Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image

abstract: Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.


title: Extracellular Vesicles as Carriers of Non-coding RNAs in Liver Diseases

abstract: Extracellular vesicles (EVs) are small membranous vesicles secreted from normal, diseased, and transformed cells in vitro and in vivo. EVs have been found to play a critical role in cell-to-cell communication by transferring non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long ncRNAs (lncRNAs) and so on. Emerging evidence shows that transferring biological information through EVs to neighboring cells in intercellular communication not only keep physiological functions, but also participate in the pathogenesis of liver diseases. Liver diseases often promote release of EVs and/or in different cargo sorting into these EVs. Either of these modifications can promote disease pathogenesis. Given this fact, EV-associated ncRNAs, such as miR-192, miR-122 and lncRNA-ROR and so on, can serve as new diagnostic biomarkers and new therapeutic targets for liver disease, because altered EV-associated ncRNAs may reflect the underlying liver disease condition. In this review, we focus on understanding the emerging role of EV-associated ncRNAs in viral hepatitis, liver fibrosis, alcoholic hepatitis (AH), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC) and discuss their utility in biomarker discovery and therapeutics. A better understanding of this multifaceted pattern of communication between different type cells in liver may contribute to developing novel approaches for personalized diagnostics and therapeutics.


title: ViewBox: integrating local file systems with cloud storage services

abstract: Cloud-based file synchronization services have become enormously popular in recent years, both for their ability to synchronize files across multiple clients and for the automatic cloud backups they provide. However, despite the excellent reliability that the cloud back-end provides, the loose coupling of these services and the local file system makes synchronized data more vulnerable than users might believe. Local corruption may be propagated to the cloud, polluting all copies on other devices, and a crash or untimely shutdown may lead to inconsistency between a local file and its cloud copy. Even without these failures, these services cannot provide causal consistency. ::: ::: To address these problems, we present ViewBox, an integrated synchronization service and local file system that provides freedom from data corruption and inconsistency. ViewBox detects these problems using ext4-cksum, a modified version of ext4, and recovers from them using a user-level daemon, cloud helper, to fetch correct data from the cloud. To provide a stable basis for recovery, ViewBox employs the view manager on top of ext4-cksum. The view manager creates and exposes views, consistent in-memory snapshots of the file system, which the synchronization client then uploads. Our experiments show that ViewBox detects and recovers from both corruption and inconsistency, while incurring minimal overhead.


title: Recent Progress on C-RAN Centralization and Cloudification

abstract: This paper presents the latest progress on cloud RAN (C-RAN) in the areas of centralization and virtualization. A C-RAN system centralizes the baseband processing resources into a pool and virtualizes soft base-band units on demand. The major challenges for C-RAN including front-haul and virtualization are analyzed with potential solutions proposed. Extensive field trials verify the viability of various front-haul solutions, including common public radio interface compression, single fiber bidirection and wavelength-division multiplexing. In addition, C-RANs facilitation of coordinated multipoint (CoMP) implementation is demonstrated with 50%-100% uplink CoMP gain observed in field trials. Finally, a test bed is established based on general purpose platform with assisted accelerators. It is demonstrated that this test bed can support multi-RAT, i.e., Time-Division Duplexing Long Term Evolution, Frequency-Division Duplexing Long Term Evolution, and Global System for Mobile Communications efficiently and presents similar performance to traditional systems.


title: Low-cost scalable UHF RFID reader antenna with no surface dead zones

abstract: Ultra High Frequency (UHF) Radio Frequency Identification (RFID) is an automatic identification technology which has already explored different ventures to replace barcode systems. Near-field (NF) identification is preferred in inventory containing liquids and metals, as well as point-of-sale applications where the challenge is to have an antenna that can scale to large sizes and has no blind spots on its surface. This paper presents a scalable NF antenna design that utilizes non-conventional but commercially available materials that makes the antenna inexpensive. The antenna architecture comprises of a rectangular spiral microstrip line with intentional impedance mismatches and a finite ground plane made from conductive tapes and sheets sandwiched by a non-conventional substrate material. Traditional patch and loop NF antennas suffer from size limitation, surface dead spots and have far-field (FF) component that introduces stray tag reads. The proposed antenna design has no surface blind spots, easily scalable and has potential for building an inexpensive tracking shelve with confined read zones that can detect both NF and FF tags. The light weight, chemical resistant, non-toxic, and self-extinguishing properties of the antenna can significantly expand the range of applications and deployment environments of UHF RFID.


title: Efficient tensor completion: Low-rank tensor train

abstract: This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global information of tensors thanks to its construction by a well-balanced matricization scheme. Two algorithms are proposed to solve the corresponding tensor completion problem. The first one called simple low-rank tensor completion via tensor train (SiLRTC-TT) is intimately related to minimizing the TT nuclear norm. The second one is based on a multilinear matrix factorization model to approximate the TT rank of the tensor and called tensor completion by parallel matrix factorization via tensor train (TMac-TT). These algorithms are applied to complete both synthetic and real world data tensors. Simulation results of synthetic data show that the proposed algorithms are efficient in estimating missing entries for tensors with either low Tucker rank or TT rank while Tucker-based algorithms are only comparable in the case of low Tucker rank tensors. When applied to recover color images represented by ninth-order tensors augmented from third-order ones, the proposed algorithms outperforms the Tucker-based algorithms.


title: Cost-Efficient Data Redundancy in the Cloud

abstract: Nowadays, the usage of cloud storages to store data is a popular alternative to traditional local storage systems. However, besides the benefits such services can offer, there are also some downsides like vendor lock-in or unavailability. Furthermore, the large number of available providers and their different pricing models can turn the search for the best fitting provider into a tedious and cumbersome task. Furthermore, the optimal selection of a provider may change over time.In this paper, we formalize a system model that uses several cloud storages to offer a redundant storage for data. The according optimization problem considers historic data access patterns and predefined Quality of Service requirements for the selection of the best-fitting storages. Through extensive evaluations we show the benefits of our work and compare the novel approach against a baseline which follows a state-of-the-art approach.