title: Robust Depth-based Person Re-identification

abstract: Person re-identification (re-id) aims to match people across non-overlapping camera views. So far the RGB-based appearance is widely used in most existing works. However, when people appeared in extreme illumination or changed clothes, the RGB appearance-based re-id methods tended to fail. To overcome this problem, we propose to exploit depth information to provide more invariant body shape and skeleton information regardless of illumination and color change. More specifically, we exploit depth voxel covariance descriptor and further propose a locally rotation invariant depth shape descriptor called Eigen-depth feature to describe pedestrian body shape. We prove that the distance between any two covariance matrices on the Riemannian manifold is equivalent to the Euclidean distance between the corresponding Eigen-depth features. Furthermore, we propose a kernelized implicit feature transfer scheme to estimate Eigen-depth feature implicitly from RGB image when depth information is not available. We find that combining the estimated depth features with RGB-based appearance features can sometimes help to better reduce visual ambiguities of appearance features caused by illumination and similar clothes. The effectiveness of our models was validated on publicly available depth pedestrian datasets as compared to related methods for re-id.


title: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

abstract: Automatic machine reading of texts in scenes is largely restricted by the poor character recognition accuracy. In this paper, we extend the Histogram of Oriented Gradient (HOG) and propose two new feature descriptors: Co-occurrence HOG (Co-HOG) and Convolutional Co-HOG (ConvCo-HOG) for accurate recognition of scene texts of different languages. Compared with HOG which counts orientation frequency of each single pixel, the Co-HOG encodes more spatial contextual information by capturing the co-occurrence of orientation pairs of neighboring pixels. Additionally, ConvCo-HOG exhaustively extracts Co-HOG features from every possible image patches within a character image for more spatial information. The two features have been evaluated extensively on five scene character datasets of three different languages including three sets in English, one set in Chinese and one set in Bengali. Experiments show that the proposed techniques provide superior scene character recognition accuracy and are capable of recognizing scene texts of different scripts and languages. HighlightsIntroduced powerful features Co-HOG and ConvCo-HOG for scene character recognition.Designed a new offset based strategy for dimension reduction of above features.Developed two new scene character datasets for Chinese and Bengali scripts.Extensive simulations on 5 datasets of 3 scripts show the efficiency of the approach.


Integrating Decision Tree and K-Means Clustering with Different Initial Centroid Selection Methods in the Diagnosis of Heart Disease Patients


Demography : Spatial Disparities and High Growth Rates


title: Skin Whitening Cosmetics: Feedback and Challenges in the Development of Natural Skin Lighteners

abstract: With the public’s growing interest in skin whitening, lightening ingredients only used under dermatological supervision until recently, are more and more frequently incorporated into cosmetic formulas. The active agents that lighten skin tone are either natural or synthetic substances, and may act at various levels of melanogenesis. They are used to treat various skin pigmentation disorders or simply to obtain a lighter skin tone as whiter skin may be synonymous of wealth, health, youth, and/or beauty in different cultures. However, recent studies demonstrated the adverse effects of some of these ingredients, leading to their interdiction or restricted use under the European Directive and several other international regulations. After an overview of skin whitening practices and the associated risks, this article provides insight into the mechanisms involved in melanin synthesis and the biological assays available to attest the lightening activity of individual ingredients. The legislation dealing with the use of skin lighteners is then discussed. As traditional depigmenting agents such as hydroquinone and corticosteroids are of safety concern, the potential of natural extracts has been investigated more and more; finally, a synthesis of three years of research in our laboratory for such plant extracts will be given.


title: Low-Profile Circularly Polarized Cavity-Backed Antennas Using SIW Techniques

abstract: Two single fed low-profile cavity-backed slot antennas and an antenna array for circular polarization (CP) applications are introduced. By employing the substrate-integrated waveguide (SIW) and half-mode SIW (HMSIW) techniques in antenna designs, low-profile cavity-backed structures are realized using the low-cost standard printed circuit board process. First, a novel spoon-shaped slot antenna is proposed to realize CP radiation based on the SIW cavity. Then, to minimize the antenna size and achieve the impedance bandwidth enhancement, the HMSIW cavity is introduced to realize a cavity-backed semicircle slot antenna (SSA). The equivalence between the edge of the HMSIW cavity and the longitude slot curved on the surface of the SIW cavity is proposed to guide the design process of the SSA. In addition, two hybrid modes are used to improve the impedance bandwidth of the SSA. Furthermore, a sequential rotation antenna array is introduced to validate the feasibility of the SSA for antenna array application. Finally, the proposed antennas and the antenna array are designed and fabricated at 28 GHz. The measured and simulated results agree very well.


title: A Fair Three-Party Contract Singing Protocol Based on Blockchain

abstract: Contract signing allows two or more mutual distrust entities to sign a predefined digital contract in a fair and effective way. It is a significant cryptographic service in commercial environment, where the crucial property for contract signing protocols is fairness. The existing solutions involved a trusted third party (TTP) to solve the problem of fairness. However, the existence of TTP become a bottleneck, since it can be a single point of failure or suffer from external or internal attack. In this paper, we propose a fair three-party contract singing protocol based on the primitive of blockchain, which provides a novel solution to design a fair protocol without a TTP. Our proposed construction makes use of the verifiable encrypted signature and the blockchain to accomplish the fair exchange. As result, a dishonest party will be monetarily penalized as it aborts after receiving the current output. Moreover, the privacy of the contract content can be preserved on the public chain.


title: Screening for gestational diabetes mellitus: the Sri Lankan experience

abstract: Objective To evaluate tests used for screening and confirmation of gestational diabetes mellitus (GDM) in Sri Lanka. Methods Field based: Consecutive pregnant women in Homagama DDHS area (n = 853), were assessed for risk factors and subjected to random and postprandial urinary Benedict's and Dipstick® tests, fasting and 2 hour post 75 g glucose capillary blood glucose (FBG and 2 hBG) which were validated against 75 g oral glucose tolerance test (OGTT) performed at 24-28 weeks (WHO criteria). Hospital based: Retrospective analysis of consecutive high-risk women (n = 999) and prospective study of randomly selected GDM women (n = 66) to assess predictive value of the OGTT. Results Field based: Sensitivity and specificity respectively of random urine Benedict's, 10%, 99.2%; postprandial urine Benedict's, 52.2%, 94.5%; postprandial urine Dipstick®, 68.7%, 90%; capillary FBG threshold 4.1 mmol/l, 62.6%, 73%; capillary 2 hBG threshold 7.2 mmol/l, 98.5%, 95.2%; risk factors, 93.1%, 22.2%. Hospital based: OGTT-11.6% lag curves, 16.3% abnormal, FPG accuracy at 4.7 mmol/l; predictive value of 2hPG≥8.9 mmol/l for insulin treatment-sensitivity 97.2%, specificity 71.4%. Conclusions Current practice of random urine testing in community screening for gestational diabetes is unreliable, and glucose specific postprandial urine test improves sensitivity. FPG is unsuitable for screening, the 2 hour post 75 g blood glucose at a threshold of >7.2 mmol/l is sensitive and specific. In laboratory confirmation using 75 g OGTT the fasting plasma glucose has low predictive value, 2 hour test performed alone is liable to false positives and 2 hour glucose >8.9 mmol/l following a peak at 1 hour suggests the need for insulin treatment. Keywords: Commuity Screening; OGTT cut-off values doi: 10.4038/cmj.v51i2.1353 Ceylon Medical Journal Vol.51(2) 2006 53-58


title: Incremental Skip-gram Model with Negative Sampling

abstract: This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.


title: Optimized Cost per Click in Taobao Display Advertising

abstract: Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.


title: SPARCNet: A Hardware Accelerator for Efficient Deployment of Sparse Convolutional Networks

abstract: Deep neural networks have been shown to outperform prior state-of-the-art solutions that often relied heavily on hand-engineered feature extraction techniques coupled with simple classification algorithms. In particular, deep convolutional neural networks have been shown to dominate on several popular public benchmarks such as the ImageNet database. Unfortunately, the benefits of deep networks have yet to be fully exploited in embedded, resource-bound settings that have strict power and area budgets. Graphical processing unit (GPU) have been shown to improve throughput and energy-efficiency over central processing unit (CPU) due to their highly parallel architecture yet still impose a significant power burden. In a similar fashion, field programmable gate array (FPGA) can be used to improve performance while further allowing more fine-grained control over implementation to improve efficiency. In order to reduce power and area while still achieving required throughput, classification-efficient network architectures are required in addition to optimal deployment on efficient hardware. In this work, we target both of these enterprises. For the first objective, we analyze simple, biologically inspired reduction strategies that are applied both before and after training. The central theme of the techniques is the introduction of sparsification to help dissolve away the dense connectivity that is often found at different levels in convolutional neural networks. The sparsification techniques include feature compression partition, structured filter pruning, and dynamic feature pruning. Additionally, we explore filter factorization and filter quantization approximation techniques to further reduce the complexity of convolutional layers. In the second contribution, we propose SPARCNet, a hardware accelerator for efficient deployment of SPARse Convolutional NETworks. The accelerator looks to enable deploying networks in such resource-bound settings by both exploiting efficient forms of parallelism inherent in convolutional layers and by exploiting the sparsification and approximation techniques proposed. To demonstrate both contributions, modern deep convolutional network architectures containing millions of parameters are explored within the context of the computer vision dataset CIFAR. Utilizing the reduction techniques, we demonstrate the ability to reduce computation and memory by 60% and 93% with less than 0.03% impact on accuracy when compared to the best baseline network with 93.47% accuracy. The SPARCNet accelerator with different numbers of processing engines is implemented on a low-power Artix-7 FPGA platform. Additionally, the same networks are optimally implemented on a number of embedded commercial-off-the-shelf platforms including NVIDIAs CPU+GPU SoCs TK1 and TX1 and Intel Edison. Compared to NVIDIAs TK1 and TX1, the FPGA-based accelerator obtains 11.8 × and 7.5 × improvement in energy efficiency while maintaining a classification throughput of 72 images/s. When further compared to a number of recent FPGA-based accelerators, SPARCNet is able to achieve up to 15 × improvement in energy efficiency while consuming less than 2W of total board power at 100MHz. In addition to improving efficiency, the accelerator has built-in support for sparsification techniques and ability to perform in-place rectified linear unit (ReLU) activation function, max-pooling, and batch normalization.


title: Graphene for Reconfigurable Terahertz Optoelectronics

abstract: In this paper, we examine graphene as a material for reconfigurable terahertz (THz) optoelectronics. The ability of electrically tuning its optical properties in a wide range of THz frequencies, together with its 2-D nature and facile integration, leads to unique opportunities for inventing novel THz devices as well as for extending the performance of existing THz technologies. We first review progress in graphene THz active optoelectronic components to date, including large-area graphene, plasmonic, and metamaterial-based devices. Advanced designs and associated challenges are then discussed.


Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison


title: Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model

abstract: With the growing reliance on computerized systems and increasing rapidity of the introduction of new technologies, user acceptance of technology continues to be an important issue. Drawing upon recent findings in information systems, human computer interaction, and social psychology, the present research extends the technology acceptance model by incorporating the motivation variables of self-efficacy, enjoyment, and learning goal orientation in order to predict the use of Web-based information systems. One hundred nine subjects participated in the study, which was conducted in a field setting with the Blackboard system, a Web-based class management system. A survey was administered after a 2-week trial period and the actual use of the system was recorded by the Blackboard system over 8 weeks. The results largely support the proposed model, highlighting the important roles of self-efficacy, enjoyment, and learning goal orientation in determining the actual use of the system. Practical implications of the results are provided.


SSVEP-based BCI using Riemannian geometry


title: WorldBrush: interactive example-based synthesis of procedural virtual worlds

abstract: We present a novel approach for the interactive synthesis and editing of virtual worlds. Our method is inspired by painting operations and uses methods for statistical example-based synthesis to automate content synthesis and deformation. Our real-time approach takes a form of local inverse procedural modeling based on intermediate statistical models: selected regions of procedurally and manually constructed example scenes are analyzed, and their parameters are stored as distributions in a palette, similar to colors on a painter's palette. These distributions can then be interactively applied with brushes and combined in various ways, like in painting systems. Selected regions can also be moved or stretched while maintaining the consistency of their content. Our method captures both distributions of elements and structured objects, and models their interactions. Results range from the interactive editing of 2D artwork maps to the design of 3D virtual worlds, where constraints set by the terrain's slope are also taken into account.


title: Securing advanced metering infrastructure using intrusion detection system with data stream mining

abstract: Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is responsible for collecting, measuring, analyzing energy usage data, and transmitting these data to the data concentrator and then to a central system in the utility side. Therefore, the security of AMI is one of the most demanding issues in the smart grid implementation. In this paper, we propose an intrusion detection system (IDS) architecture for AMI which will act as a complimentary with other security measures. This IDS architecture consists of three local IDSs placed in smart meters, data concentrators, and central system (AMI headend). For detecting anomaly, we use data stream mining approach on the public KDD CUP 1999 data set for analysis the requirement of the three components in AMI. From our result and analysis, it shows stream data mining technique shows promising potential for solving security issues in AMI.


title: A Supervised Learning Approach for Authorship Attribution of Bengali Literary Texts

abstract: Authorship Attribution is a long-standing problem in Natural Language Processing. Several statistical and computational methods have been used to find a solution to this problem. In this article, we have proposed methods to deal with the authorship attribution problem in Bengali. More specifically, we proposed a supervised framework consisting of lexical and shallow features and investigated the possibility of using topic-modeling-inspired features, to classify documents according to their authors. We have created a corpus from nearly all the literary works of three eminent Bengali authors, consisting of 3,000 disjoint samples. Our models showed better performance than the state-of-the-art, with more than 98% test accuracy for the shallow features and 100% test accuracy for the topic-based features. Further experiments with GloVe vectors [Pennington et al. 2014] showed comparable results, but flexible patterns based on content words and high-frequency words [Schwartz et al. 2013] failed to perform as well as expected.


title: Automatic docking and recharging system for autonomous security robot

abstract: To keep autonomous mobile robot in continuously working condition, power supply is an important issue. Typically, rechargeable batteries may provide few hours of peak usage. Recharging is necessary before the power of the batteries has exhausted. In this paper, the docking station with an auto-recharging device and robot docking mechanism are implemented. We propose a docking control strategy and automatic recharging device for the security robot recharging. We also propose a power prediction algorithm to determine when the robot needs to navigate back to docking station for recharging. An artificial landmark is detected and recognized by the proposed image processing system. Then the geometrical relationship between the robot and the docking station (the depth and orientation) is estimated. The robot will move directly right in front of the docking station. Finally, the robot approaches to the docking station based on the proposed virtual spring model. In this model, we assume that the robot and the docking station are connected by a virtual spring. The compliant forces act both in the direction of the translation deformation and bending. The motion control parameters, which are velocities of the two wheels, can be derived from the model. Experimental results show that the proposed methods successfully perform the docking and automatic recharging.


title: Enriching Wikidata with Frame Semantics

abstract: Wikidata is a large-scale, multilingual and freely available knowledge base. It contains more than 14 million facts, however, it is still missing linguistic information. In this paper, we aim to bridge this gap by aligning Wikidata with FrameNet lexicon. We propose an approach based on word embedding to identify a mapping between Wikidata relations, called properties, and FrameNet frames and to annotate the arguments of each relation with the semantic roles of the matching frames. Early empirical results show the advantage of our approach compared to other baseline methods.


title: Parameterized strategy pattern

abstract: The Strategy pattern decouples algorithms from the class that uses them allowing the algorithms to vary independently. It does not, however, allow the algorithms to have different parameters. The parameterized strategy pattern presented in this paper addresses the case when the algorithms have different sets of parameters, and when the user is allowed to see and modify these parameters for each concrete algorithm before its execution. This is accomplished by introducing special parameter classes which encapsulate algorithms parameters. The abstract algorithm class is completely decoupled from parameters letting each concrete algorithm class to create its own list of parameter instances which mirrors its parameters.


title: An effect of electromagnetic force on acoustic noise of axial-gap in-wheel SR motor

abstract: Switched Reluctance (SR) motors are the motor that utilize the reluctance torque originated in magnetic saliency between stator and rotor composed of electromagnetic steel sheet and windings without permanent magnet. Because of its robustness and simple structure, SR motors are suited to in-wheel motor. In flat space, an axial-gap structure has advantage compared with conventional radial-gap structure in space factor of motor. An axial-gap in-wheel SR motor was tested for microbus and the bus could be driven. We had successful results on the motor characteristics. However, the motor emits loud acoustic noise directly to the environment. We proposed freewheeling mode as a control method to reduce vibration. Freewheeling mode has some advantages compared with conventional methods, for example, simple system, low cost, no torque reduction and high versatility. And this method improves acoustic noise. This paper discusses characterizing vibration mechanism of axial-gap SR motor and how to reduce acoustic noise with freewheeling mode.


title: Power electronics intensive solutions for advanced electric, hybrid electric, and fuel cell vehicular power systems

abstract: There is a clear trend in the automotive industry to use more electrical systems in order to satisfy the ever-growing vehicular load demands. Thus, it is imperative that automotive electrical power systems will obviously undergo a drastic change in the next 10-20 years. Currently, the situation in the automotive industry is such that the demands for higher fuel economy and more electric power are driving advanced vehicular power system voltages to higher levels. For example, the projected increase in total power demand is estimated to be about three to four times that of the current value. This means that the total future power demand of a typical advanced vehicle could roughly reach a value as high as 10 kW. In order to satisfy this huge vehicular load, the approach is to integrate power electronics intensive solutions within advanced vehicular power systems. In view of this fact, this paper aims at reviewing the present situation as well as projected future research and development work of advanced vehicular electrical power systems including those of electric, hybrid electric, and fuel cell vehicles (EVs, HEVs, and FCVs). The paper will first introduce the proposed power system architectures for HEVs and FCVs and will then go on to exhaustively discuss the specific applications of dc/dc and dc/ac power electronic converters in advanced automotive power systems


title: Wideband and Unidirectional Cavity-Backed Folded Triangular Bowtie Antenna

abstract: In this paper, a cavity-backed folded triangular bowtie antenna (FTBA) is proposed and investigated. Comparisons show that it has much smaller fluctuation of input impedance and much larger bandwidth (BW) for stable radiation patterns than cavity-backed ordinary TBA. The proposed antenna is fed by a conventional balun composed of a transition from a microstrip line to a parallel stripline. It achieves an impedance BW of 92.2% for |S11| les -10 dB, stable gain of around 9.5 dBi and unidirectional radiation patterns over the whole operating band. A close examination of the antenna aperture efficiency and the electric field distribution in the aperture provides an explanation for the stable gain across such large frequency band. Comparisons are made against several conventional antennas, including short backfire and microstrip patch antennas, to illustrate the salient features of the proposed antenna. A design guideline is also given for practical applications.


title: Sentiment Classification Using Machine Learning Techniques with Syntax Features

abstract: Sentiment classification has adopted machine learning techniques to improve its precision and efficiency. However, the features are always produced by basic words-bag methods without much consideration for words' syntactic properties, which could play an important role in the judgment of sentiment meanings. To remedy this, we firstly generate syntax trees of the sentences, with the analysis of syntactic features of the sentences. Then we introduce multiple sentiment features into the basic words-bag features. Such features were trained on movie reviews as data, with machine learning methods (Naive Bayes and support vector machines). The features and factors introduced by syntax tree were examined to generate a more accurate solution for sentiment classification.


Training Neural Networks for Financial Forecasting: Backpropagation vs Particle Swarm Optimization


title: Using Patterns to Capture Architectural Decisions

abstract: Throughout the software design process, developers must make decisions and reify them in code. The decisions made during software architecting are particularly significant in that they have system-wide implications, especially on the quality attributes. However, architects often fail to adequately document their decisions because they don't appreciate the benefits, don't know how to document decisions, or don't recognize that they're making decisions. This lack of thorough documentation. This paper provides information about a decision's rationale and consequences, architecture patterns can help architects better understand and more easily record their decisions.


title: A stochastically verifiable decision making framework for autonomous ground vehicles

abstract: A framework is presented for probabilistic verification of Autonomous Vehicles' (AV) decisions by rational agents onboard. The AV's are assumed to be equipped with the necessary perception and control systems, which are needed for their awareness of the environment and enable them to make decisions. The decisions arrived at by the agents are verified by the probabilistic model checking techniques presented. The objective of the new framework is to reduce design complexity of decision making while ensuring verifiability of the decisions made by the rational agents. Probabilistic Timed Programs (PTPs) are used to model the environmental scenarios and Probabilistic Computational Tree Logic (PCTL) to specify the properties to be verified. Both PTP and PCTL are deployed by use of the PRISM model checker. For demonstration purposes, the Robot Operating System (ROS) and the Gazebo simulator are used to model and test the vehicle systems and their signal processing. sEnglish Publisher, the high abstraction level programming tool, is used along with a Jason agent architecture to provide rules and plans for decision making. Matlab is relied on to assist in programming the perception and control systems in the demonstration.


title: Genetically Modified Food: Knowledge and Attitude of Teachers and Students

abstract: The concepts behind the technology of genetic modification of organisms and its applications are complex. A diverse range of opinions, public concern and considerable media interest accompanies the subject. This study explores the knowledge and attitudes of science teachers and senior secondary biology students about the application of a rapidly expanding technology, genetic engineering, to food production. The results indicated significant difference in understanding of concepts related with genetically engineered food stuffs between teachers and students. The most common ideas about genetically modified food were that cross bred plants and genetically modified plants are not same, GM organisms are produced by inserting a foreign gene into a plant or animal and are high yielding. More teachers thought that genetically engineered food stuffs were unsafe for the environment. Both teachers and students showed number of misconceptions, for example, the pesticidal proteins produced by GM organisms have indirect effects through bioaccumulation, induces production of allergic proteins, genetic engineering is production of new genes, GM plants are leaky sieves and that transgenes are more likely to introgress into wild species than mutated species. In general, more students saw benefits while teachers were cautious about the advantages of genetically engineered food stuffs.


title: DataPlay: interactive tweaking and example-driven correction of graphical database queries

abstract: Writing complex queries in SQL is a challenge for users. Prior work has developed several techniques to ease query specification but none of these techniques are applicable to a particularly difficult class of queries: quantified queries. Our hypothesis is that users prefer to specify quantified queries interactively by trial-and-error. We identify two impediments to this form of interactive trial-and-error query specification in SQL: (i) changing quantifiers often requires global syntactical query restructuring, and (ii) the absence of non-answers from SQL's results makes verifying query correctness difficult. We remedy these issues with DataPlay, a query tool with an underlying graphical query language, a unique data model and a graphical interface. DataPlay provides two interaction features that support trial-and-error query specification. First, DataPlay allows users to directly manipulate a graphical query by changing quantifiers and modifying dependencies between constraints. Users receive real-time feedback in the form of updated answers and non-answers. Second, DataPlay can auto-correct a user's query, based on user feedback about which tuples to keep or drop from the answers and non-answers. We evaluated the effectiveness of each interaction feature with a user study and we found that direct query manipulation is more effective than auto-correction for simple queries but auto-correction is more effective than direct query manipulation for more complex queries.


Authoritative Citation KNN Learning with Noisy Training Datasets


title: Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference

abstract: Purpose ::: Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling.


Value delivery architecture modeling-A new approach for business modeling


title: A Score based Web Page Ranking Algorithm

abstract: the explosive growth of information in the Web, users face difficulties while finding their desired information. Search engine helps the user by retrieving useful information from this huge collection based on his/her search query and presents a list of relevant web pages as a search result. However, without proper ranking of pages in the result through the relevancy of pages to the search query, the user may need to explore the whole list for discovering the appropriate page(s), thereby involving huge search time. Although a number of ranking algorithms such as HITS, PageRank, Weighted PageRank and etc., are developed to assist the search engine, but none of them provides page ranking with high accuracy. In this paper, we propose a score- based web page ranking algorithm involving web content mining and usage information of the pages. Our algorithm considers both syntactical and semantic matches of the search query to the pages. For a web page, syntactical score is calculated based on the total number of exact matches of the search words in the page. Besides, semantic score is measured using synonym matches of the search words. Moreover, we incorporate the usage information of the pages as page popularity in order to comprise the user interest in the ranking order. The total relevant score of each page is calculated using the summation of the syntactical and semantic scores of the page and its page popularity. Finally, the pages are ranked according to their total relevant score. Based on several performance evaluation measures, experimental results show considerable improvement in the page ranking using our algorithm as compared to other known approaches.


title: Interpretable Active Learning

abstract: Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. We demonstrate how LIME can be used to generate locally faithful explanations for an active learning strategy, and how these explanations can be used to understand how different models and datasets explore a problem space over time. In order to quantify the per-subgroup differences in how an active learning strategy queries spatial regions, we introduce a notion of uncertainty bias (based on disparate impact) to measure the discrepancy in the confidence for a model's predictions between one subgroup and another. Using the uncertainty bias measure, we show that our query explanations accurately reflect the subgroup focus of the active learning queries, allowing for an interpretable explanation of what is being learned as points with similar sources of uncertainty have their uncertainty bias resolved. We demonstrate that this technique can be applied to track uncertainty bias over user-defined clusters or automatically generated clusters based on the source of uncertainty.


title: Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

abstract: We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.


title: Ship resistance performance optimization design based on CAD / CFD

abstract: For the current limitations of ship parametric methods, using ship hull morphing method, this paper developed ship morphing parametric module, realized the integration of CAD/CFD software using iSIGHT and established integrated optimization platform. To verify the feasibility of ship morphing method, 1300TEU container ship bulbous bow's optimization was made using genetic algorithm. The results showed that the ship morphing method based on CAD has the following advantages: (1) fully using the function of CAD software's parametric modeling, optimization variables are less; (2) providing a precise hull shape for resistance analysis; (3) acquiring the information of displacement, longitudinal center of buoyancy and so on from CAD model; (4) directly reflecting the optimization results into three-dimensional ship model. These advantages made the method have practical significance.


title: Combining Software Metrics and Text Features for Vulnerable File Prediction

abstract: In recent years, to help developers reduce time and effort required to build highly secure software, a number of prediction models which are built on different kinds of features have been proposed to identify vulnerable source code files. In this paper, we propose a novel approach VULPREDICTOR to predict vulnerable files, it analyzes software metrics and text mining together to build a composite prediction model. VULPREDICTOR first builds 6 underlying classifiers on a training set of vulnerable and non-vulnerable files represented by their software metrics and text features, and then constructs a meta classifier to process the outputs of the 6 underlying classifiers. We evaluate our solution on datasets from three web applications including Drupal, PHPMyAdmin and Moodle which contain a total of 3,466 files and 223 vulnerabilities. The experiment results show that VULPREDICTOR can achieve F1 and EffectivenessRatio@20% scores of up to 0.683 and 75%, respectively. On average across the 3 projects, VULPREDICTOR improves the F1 and EffectivenessRatio@20% scores of the best performing state-of-the-art approaches proposed by Walden et al. by 46.53% and 14.93%, respectively.


title: Evaluation of the Intel® Core™ i7 Turbo Boost feature

abstract: The Intel® Core™ i7 processor code named Nehalem has a novel feature called Turbo Boost which dynamically varies the frequencies of the processor's cores. The frequency of a core is determined by core temperature, the number of active cores, the estimated power and the estimated current consumption. We perform an extensive analysis of the Turbo Boost technology to characterize its behavior in varying workload conditions. In particular, we analyze how the activation of Turbo Boost is affected by inherent properties of applications (i.e., their rate of memory accesses) and by the overall load imposed on the processor. Furthermore, we analyze the capability of Turbo Boost to mitigate Amdahl's law by accelerating sequential phases of parallel applications. Finally, we estimate the impact of the Turbo Boost technology on the overall energy consumption. We found that Turbo Boost can provide (on average) up to a 6% reduction in execution time but can result in an increase in energy consumption up to 16%. Our results also indicate that Turbo Boost sets the processor to operate at maximum frequency (where it has the potential to provide the maximum gain in performance) when the mapping of threads to hardware contexts is sub-optimal.


title: Bonding 1200 V, 150 A IGBT chips (13.5 mm × 13.5 mm) with DBC substrate by pressureless sintering nanosilver paste for power electronic packaging

abstract: The insulated gate bipolar transistor (IGBT) is a minority-carrier device with large bipolar current-carrying capability and high input impedance. The IGBT has been used in many applications in power electronics. In this study, pressureless sintering of nanosilver paste has been studied for bonding large area power chips, e.g., 12 kV 100 A IGBT applications. The sintering temperature was reduced to 250 °C for only 10 min. The heating rate was adjusted to 5 °C/min. Void ratio in the bond-line before and after sintering was analyzed by X-ray transmission. It was found that the void ratio before sintering was successfully controlled below 1% of the joint area. X-ray images also showed that the void ratio was lower than 2% after sintering. Such low void ratio should have almost no impact on the thermal and electric properties of the pressureless sintered nanosilver joint. The more the voids before sintering, the more the formation of voids after sintering. Die-shear tests were also performed for the 10 mm × 10 mm sintered joints of bonding dummy dies and substrates at a shear speed of 4 × 10−4 ms−1 at room temperature. Results showed that average shear strength of higher than 40 MPa could be obtained for the pressureless sintered joints. Fracture surfaces of the sintered nanosilver joints were observed by scanning electron microscopy (SEM). SEM images showed that significant plastic flow occurred in the sheared joint because of the presence of dimples and the fracture of the sheared silver joint was a cohesive failure.


title: Impact of the front-of-pack 5-colour nutrition label (5-CNL) on the nutritional quality of purchases: an experimental study

abstract: Front-of-pack (FOP) nutrition labelling has received growing attention from public health authorities, as a tool to promote healthier diets in the population. Recently, the French Health law introduced the principle of implementing a FOP nutrition labelling system. A scientific proposal has put forward the 5-Colour Nutrition Label (5-CNL), a five-category colour-coded summary system supported by research suggesting that it is well perceived and understood in the population. Our objective was to investigate the impact of the 5-CNL on the nutritional quality of purchases in experimental supermarkets. Participants (n = 901) were recruited using quota sampling between September and December 2015 and evenly distributed in three experimental conditions: 1) control situation; 2) Application of the 5-CNL on all food products in three specific sections: breakfast cereals, sweet biscuits and appetizers; 3) introduction of the 5-CNL accompanied by consumer information on use and understanding of the label. Main outcome was the nutritional quality of the shopping cart in the three sections combined, measured using the United Kingdom Food Standards Agency nutrient profiling system (FSA score). Significantly higher mean nutritional quality of the purchased items per section were observed for the sweet biscuits category in the intervention combining the label + communication (overall FSA score 21.01 vs. 20.23, P = 0.02). No significant effects were observed for the general mean over the three sections combined or other food categories. The results observed on purchase may be related to the high level of recall, self-reported and objective understanding of the label that were observed in the intervention groups as they are pre-requisites for use of a label in purchasing situations. These results suggest that the 5-CNL FOP nutrition label may have a limited impact on purchases, leading to healthier food choices in some food categories such as sweet biscuits. The trial was registered on Clinicaltrials.gov under the number NCT02546505.


title: Variable-Stride Multi-Pattern Matching For Scalable Deep Packet Inspection

abstract: Accelerating multi-pattern matching is a critical issue in building high-performance deep packet inspection systems. Achieving high-throughputs while reducing both memory-usage and memory-bandwidth needs is inherently difficult. In this paper, we propose a pattern (string) matching algorithm that achieves high throughput while limiting both memory-usage and memory-bandwidth. We achieve this by moving away from a byte-oriented processing of patterns to a block-oriented scheme. However, different from previous block-oriented approaches, our scheme uses variable-stride blocks. These blocks can be uniquely identified in both the pattern and the input stream, hence avoiding the multiplied memory costs which is intrinsic in previous approaches. We present the algorithm, tradeoffs, optimizations, and implementation details. Performance evaluation is done using the Snort and ClamAV pattern sets. Using our algorithm, the throughput of a single search engine can easily have a many-fold increase at a small storage cost, typically less than three bytes per pattern character.


title: Exercise and well-being: a review of mental and physical health benefits associated with physical activity

abstract: Purpose of reviewThis review highlights recent work evaluating the relationship between exercise, physical activity and physical and mental health. Both cross-sectional and longitudinal studies, as well as randomized clinical trials, are included. Special attention is given to physical conditions, i


title: Explanation Methods for Bayesian Networks : review and application to a maritime scenario

abstract: Surveillance systems analyze and present vast amounts of heterogeneous sensor data. In order to support operators while monitoring such systems, the identification of anomalous behavior or situatio ...


abstract: A broadband printed bow-tie antenna with a simplified balanced feeding network and modified tapering is presented. Microstrip and parallel-strip transmission lines printed on the substrate with high dielectric permittivity realize the proposed feeding network. The ground-plane transition between the microstrip line and the parallel-strip line is exponentially tapered so as to reduce the reflection losses and produce a balanced feed for the antenna. This printed bow-tie antenna achieves a 68% measured bandwidth and a stable radiation pattern within the X-band. Commercial FEM software is used for optimization of the bow-tie antenna and the simulation results agree very well with the experiment. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 47: 534–536, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21221


A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine


PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems


title: Millimeter-wave wideband circularly polarized antenna arrays using travelling-wave elements

abstract: Using travelling-wave elements, two planar antenna arrays are developed for millimeter-wave wideband circularly polarized (CP) applications. First, the aperture-fed travelling-wave element is designed. Stacked curl element is printed on the surface of the substrate to realize wideband axial ratio (AR) and impedance performance. Then, two antenna arrays using substrate integrated waveguide (SIW) and microstrip feeding networks are developed, respectively. Performance comparison between these two arrays are discussed. Finally, the wideband CP antenna array which is fed by the SIW network is fabricated and measured. The measured impedance bandwidth of the array is 35.4%, 3-dB AR bandwidth is 33.8%, and 3-dB gain bandwidth is 32.2% with a peak gain of 23.5 dBic. The overall bandwidth is 30.6% and good efficiency of about 70% is obtained because of the low-loss full-corporate SIW feeding scheme.


title: X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

abstract: In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network—thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2–6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.


title: The description logic handbook: theory, implementation, and applications

abstract: Description logics are embodied in several knowledge-based systems and are used to develop various real-life applications. Now in paperback, The Description Logic Handbook provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications. Its appeal will be broad, ranging from more theoretically oriented readers, to those with more practically oriented interests who need a sound and modern understanding of knowledge representation systems based on description logics. As well as general revision throughout the book, this new edition presents a new chapter on ontology languages for the semantic web, an area of great importance for the future development of the web. In sum, the book will serve as a unique resource for the subject, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses.


title: Multi sensor system for automatic fall detection

abstract: To reduce elderly falling risk in the area where video surveillance system is unavailable due to privacy reason and so on, we need a monitor system which does no recognize what they are doing. This paper presents an elderly-falling detection system using ultrasonic sensors. The ultrasonic technology-based multi sensors are couple of receiver and transmitter together, which are connected to Arduino microcontroller in order to send the elderly person's fall related signal using WiFi to the processing unit. The sensors are positioned as an array on the roof and wall in the room. The signal is analyzed to recognize human by sensing distance, and detect the action such as standing, sitting, and falling by pattern matching with the standard templates of top and side signal. In experiments, the proposed system can recognize accuracy of falling detection is 93% approximately.


title: Ultra-Fast Multi-Scale Shape Estimation of Light Transport Matrix for Complex Light Reflection Objects

abstract: 3D measurement of target objects characterized by specular reflection or subsurface scatterings cannot be measured by traditional 3D measurement methods because these targets have multiple light paths that make it difficult to determine the unique surface. We define these objects as complex light reflection objects. In this case, 3D measurement methods based on Light Transport (LT) Matrix estimation may be a solution to measure these complex light reflection objects, because LT Matrix captures every light path, and we can identify all 3D points on the target shape by using LT Matrix. However, these methods either provide low resolution results, or they are too slow for use in robot vision in practice. In this paper, we suppress the computational cost of LT Matrix estimation by dividing LT Matrix estimation into multi-scale. The proposed method reduces the number of candidate combinations between camera pixels and projector pixels greatly by using the information given by low resolution observations. The proposed algorithm allows high resolution measurement of the LT Matrix very efficiently. Furthermore, careful implementation of our method by using a sparse matrix representation achieves memory efficiency. We evaluated our method by measuring 3D points for a 256 × 256 resolution projector and camera system, which is an LT matrix 4096 times larger than that developed in our previous study [1] and 100 times faster than our naive implementation of [2].


Adaptive Context-aware Reinforced Agent for Handwritten Text Recognition


title: Brain Cancer Stem Cells in Adults and Children: Cell Biology and Therapeutic Implications

abstract: Brain tumors represent some of the most malignant cancers in both children and adults. Current treatment options target the majority of tumor cells but do not adequately target self-renewing cancer stem cells (CSCs). CSCs have been reported to resist the most aggressive radiation and chemotherapies, and give rise to recurrent, treatment-resistant secondary malignancies. With advancing technologies, we now have a better understanding of the genetic, epigenetic and molecular signatures and microenvironmental influences which are useful in distinguishing between distinctly different tumor subtypes. As a result, efforts are now underway to identify and target CSCs within various tumor subtypes based on this foundation. This review discusses progress in CSC biology as it relates to targeted therapies which may be uniquely different between pediatric and adult brain tumors. Studies to date suggest that pediatric brain tumors may benefit more from genetic and epigenetic targeted therapies, while combination treatments aimed specifically at multiple molecular pathways may be more effective in treating adult brain tumors which seem to have a greater propensity towards microenvironmental interactions. Ultimately, CSC targeting approaches in combination with current clinical therapies have the potential to be more effective owing to their ability to compromise CSCs maintenance and the mechanisms which underlie their highly aggressive and deadly nature.


title: MIR In Matlab: The MIDI Toolbox

abstract: 150-200 words) The MIDI Toolbox is a compilation of functions for analyzing and visualizing MIDI files in the Matlab computing environment. In this article, the basic issues of the Toolbox are summarized and demonstrated with examples ranging from melodic contour, similarity, keyfinding, meter-finding to segmentation. The Toolbox is based on symbolic musical data but signal processing methods are applied to cover such aspects of musical behaviour as geometric representations and short-term memory. Besides simple manipulation and filtering functions, the toolbox contains cognitively inspired analytic techniques that are suitable for contextdependent musical analysis, a prerequisite for many music information retrieval applications.


title: Workload characterization on a production Hadoop cluster: A case study on Taobao

abstract: MapReduce is becoming the state-of-the-art computing paradigm for processing large-scale datasets on a large cluster with tens or thousands of nodes. It has been widely used in various fields such as e-commerce, Web search, social networks, and scientific computation. Understanding the characteristics of MapReduce workloads is the key to achieving better configuration decisions and improving the system throughput. However, workload characterization of MapReduce, especially in a large-scale production environment, has not been well studied yet. To gain insight on MapReduce workloads, we collected a two-week workload trace from a 2,000-node Hadoop cluster at Taobao, which is the biggest online e-commerce enterprise in Asia, ranked 14th in the world as reported by Alexa. The workload trace covered 912,157 jobs, logged from Dec. 4 to Dec. 20, 2011. We characterized the workload at the granularity of job and task, respectively and concluded with a set of interesting observations. The results of workload characterization are representative and generally consistent with data platforms for e-commerce websites, which can help other researchers and engineers understand the performance and job characteristics of Hadoop in their production environments. In addition, we use these job analysis statistics to derive several implications for potential performance optimization solutions.


title: Using automatically labelled examples to classify rhetorical relations: An assessment. Natural Language Engineering

abstract: Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples).


title: Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model

abstract: Recently, wavelet transforms have gained very high attention in many fields and applications such as physics, engineering, signal processing, applied mathematics and statistics. In this paper, we present the advantage of wavelet transforms in forecasting financial time series data. Amman stock market (Jordan) was selected as a tool to show the ability of wavelet transform in forecasting financial time series, experimentally. This article suggests a novel technique for forecasting the financial time series data, based on Wavelet transforms and ARIMA model. Daily return data from 1993 until 2009 is used for this study.


title: Crime hotspot mapping using the crime related factors—a spatial data mining approach

abstract: The technique of Hotspot Mapping is widely used in analysing the spatial characteristics of crimes. The spatial distribution of crime is considered to be related with a variety of socio-economic and crime opportunity factors. But existing methods usually focus on the target crime density as input without utilizing these related factors. In this study, we introduce a new crime hotspot mapping tool--Hotspot Optimization Tool (HOT). HOT is an application of spatial data miming to the field of hotspot mapping. The key component of HOT is the Geospatial Discriminative Patterns (GDPatterns) concept, which can capture the differences between two classes in a spatial dataset. Experiments are done using a real world dataset from a northeastern city in the United States and the pros and cons of utilizing related factors in hotspot mapping are discussed. Comparison studies with the Hot Spot Analysis tool implemented by Esri ArcMap 10.1 validate that HOT is capable of accurately mapping crime hotspots.


title: How to Estimate Forecasting Quality: A System- Motivated Derivation of Symmetric Mean Absolute Percentage Error (SMAPE) and Other Similar Characteristics

abstract: When comparing how well different algorithms forecast time series, researchers use an average value of the ratio |x− y| (|x|+ |y|)/2 , known as the Symmetric Mean Absolute Percentage Error (SMAPE). In this paper, we provide a system-motivated explanation for this formula. We also explain how this formula explains the use of geometric mean to combine different forecasts.


Chitin and Chitosan Preparation from Marine Sources. Structure, Properties and Applications


title: Domain Adaption via Feature Selection on Explicit Feature Map

abstract: In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rather than using all features. However, invariant features across domains may lie in a higher order space, instead of in the original feature space. Moreover, the discriminative ability of some invariant features such as shared background information is weak, and needs to be further filtered. Therefore, in this paper, we propose a novel domain adaption algorithm based on an explicit feature map and feature selection. The data are first represented by a kernel-induced explicit feature map, such that high-order invariant features can be revealed. Then, by minimizing the marginal distribution difference, conditional distribution difference, and the model error, the invariant discriminative features are effectively selected. This problem is NP-hard to be solved, and we propose to relax it and solve it by a cutting plane algorithm. Experimental results on six real-world benchmarks have demonstrated the effectiveness and efficiency of the proposed algorithm, which outperforms many state-of-the-art domain adaption approaches.


title: Pupil dilation but not microsaccade rate robustly reveals decision formation

abstract: Pupil dilation, an indicator of arousal that is generally regarded as unspecific, amongst others reflects decision formation and reveals choice. Employing letter selection in a Go/NoGo task, we show that choice can robustly be predicted by the pupillary signal, even under the presence of strong interfering factors such as changes in brightness or motor execution. In addition, a larger difference in pupil dilation between target and distractor conditions for NoGo compared to Go was demonstrated, underlining the particular appropriateness of the paradigm for decision research. Incorporating microsaccades, a variable that is suggested to covary with pupil diameter, we show that decision formation can only be observed in pupil diameter. However, microsaccade rate and pupil size covaried for motor execution and both reflected choice after key press with smaller effect size for microsaccade rate. We argue that combining pupil dilation and microsaccade rate may help dissociating decision-related changes in pupil diameter from interfering factors. Considering the interlinked main neural correlates of pupil dilation and microsaccade generation, these findings point to a selective role of locus coeruleus compared to superior colliculus in decision formation.


title: Analysis of Time Delay Attacks against Power Grid Stability

abstract: The modern power grid, as a critical national infrastructure, is operated as a cyber-physical system. While the Wide-Area Monitoring, Protection and Control Systems (WAMPCS) in the power grid ensures stable dynamical responses by allowing real-time remote control and collecting measurement over across the power grid, they also expose the power grid to potential cyber-attacks. In this paper, we analyze the effects of Time Delay Attacks (TDAs), which disturb stability of the power grid by simply delaying the transfer of measurement and control demands over the grid's cyber infrastructure. Different from the existing work which simulates TDAs' impacts under specific scenarios, we come up with a generic analytical framework to derive the TDAs' effective conditions. In particular, we propose three concepts of TDA margins, TDA boundary, and TDA surface to define the insecure zones where TDAs are able to destabilize the grid. The proposed concepts and analytical results are exemplified in the context of Load Frequency Control (LFC), but can be generalized to other power control applications.


title: Language Modeling for Code-Switched Data: Challenges and Approaches

abstract: Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research. In bilingual communities, the speakers commonly embed the words and phrases of a non-native language into the syntax of a native language in their day-to-day communications. The code-switching is a global phenomenon among multilingual communities, still very limited acoustic and linguistic resources are available as yet. For developing effective speech based applications, the ability of the existing language technologies to deal with the code-switched data can not be over emphasized. The code-switching is broadly classified into two modes: inter-sentential and intra-sentential code-switching. In this work, we have studied the intra-sentential problem in the context of code-switching language modeling task. The salient contributions of this paper includes: (i) the creation of Hindi-English code-switching text corpus by crawling a few blogging sites educating about the usage of the Internet (ii) the exploration of the parts-of-speech features towards more effective modeling of Hindi-English code-switched data by the monolingual language model (LM) trained on native (Hindi) language data, and (iii) the proposal of a novel textual factor referred to as the code-switch factor (CS-factor), which allows the LM to predict the code-switching instances. In the context of recognition of the code-switching data, the substantial reduction in the PPL is achieved with the use of POS factors and also the proposed CS-factor provides independent as well as additive gain in the PPL.


title: Rendering fur with three dimensional textures

abstract: We present a method for rendering scenes with fine detail via an object called a texel, a rendering primitive inspired by volume densities mixed with anisotropic lighting models. This technique solves a long outstanding problem in image synthesis: the rendering of furry surfaces.


title: A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection

abstract: We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.


Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs


title: Fraud Data Analytics Tools and Techniques in Big Data Era

abstract: Fraudulent activities (e.g., suspicious credit card transaction, financial reporting fraud, and money laundering) are critical concerns to various entities including bank, insurance companies, and public service organizations. Typically, these activities lead to detrimental effects on the victims such as a financial loss. Over the years, fraud analysis techniques underwent a rigorous development. However, lately, the advent of Big data led to vigorous advancement of these techniques since Big Data resulted in extensive opportunities to combat financial frauds. Given that the massive amount of data that investigators need to sift through, massive volumes of data integrated from multiple heterogeneous sources (e.g., social media, blogs) to find fraudulent patterns is emerging as a feasible approach.


Image of Adam, Son of God: Genesis 5:3 and Luke 3:38 in Intercanonical Dialogue


title: Research Portfolio Analysis and Topic Prominence

abstract: Stakeholders in the science system need to decide where to place their bets. Example questions include: Which areas of research should get more funding? Who should we hire? Which projects should we abandon and which new projects should we start? Making informed choices requires knowledge about these research options. Unfortunately, to date research portfolio options have not been defined in a consistent, transparent and relevant manner. Furthermore, we don’t know how to define demand for these options. In this article, we address the issues of consistency, transparency, relevance and demand by using a model of science consisting of 91,726 topics (or research options) that contain over 58 million documents. We present a new indicator of topic prominence – a measure of visibility, momentum and, ultimately, demand. We assign over $203 billion of project-level funding data from STAR METRICS® to individual topics in science, and show that the indicator of topic prominence, explains over one-third of the variance in current (or future) funding by topic. We also show that highly prominent topics receive far more funding per researcher than topics that are not prominent. Implications of these results for research planning and portfolio analysis by institutions and researchers are emphasized.


title: Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective

abstract: Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.


title: Splitting GSM schemas: A framework for outsourcing of declarative artifact systems

abstract: Case Management is emerging as an important paradigm for Business Process Management. The Guard-Stage-Milestone (GSM) model is a recent case management approach that substantially influences OMG@?s emerging Case Management Modeling Notation standard. We study the problem of outsourcing part of a GSM schema to another party, and develop a formal framework that supports splitting and outsourcing of GSM schemas. One element of the framework focuses on restructuring the GSM schema to facilitate outsourcing while preserving the semantics of the original schema; the second focuses on locking protocols that define how the distributed parties should operate. Additionally, the framework allows parties to keep local parts of their GSM subschema private without affecting the outcomes of the global execution. The rules restructuring developed here enables a crisp separation of concerns, which allows reuse of existing GSM (and thus Case Management) engines for executing the subschemas. Both elements of the framework are formally proven correct.


title: New applications in power electronics for highly integrated high-speed magnetoresistive current sensors

abstract: The usage of magnetoresistive current sensors is increasing steadily in the field of power electronics. Sensors must not only be accurate and dynamic, but must also be compact and robust. An integrated SMT magneto-resistive current sensor as well as a 2 MHz bandwidth THT current sensor is introduced. Various future applications from the wide field of power electronics are shown that open up new possibilities in the fields of industrial automation, electro mobility and more electric aircraft.


title: Estimation of land surface temperature over Delhi using Landsat-7 ETM+

abstract: Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.


title: Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores

abstract: In-memory key-value stores play a critical role in data processing to provide high throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data intensive operations demanding high memory bandwidth for fast data accesses, (2) high data parallelism and simple computing operations demanding many slim parallel computing units, and (3) a large working set. As data volume continues to increase, our experiments show that conventional and general-purpose multicore systems are increasingly mismatched to the special properties of key-value stores because they do not provide massive data parallelism and high memory bandwidth; the powerful but the limited number of computing cores do not satisfy the demand of the unique data processing task; and the cache hierarchy may not well benefit to the large working set. ::: ::: In this paper, we make a strong case for GPUs to serve as special-purpose devices to greatly accelerate the operations of in-memory key-value stores. Specifically, we present the design and implementation of Mega-KV, a GPU-based in-memory key-value store system that achieves high performance and high throughput. Effectively utilizing the high memory bandwidth and latency hiding capability of GPUs, Mega-KV provides fast data accesses and significantly boosts overall performance. Running on a commodity PC installed with two CPUs and two GPUs, Mega-KV can process up to 160+ million key-value operations per second, which is 1.4-2.8 times as fast as the state-of-the-art key-value store system on a conventional CPU-based platform.


title: Empathy and compassion

abstract: As humans we are a highly social species: in order to coordinate our joint actions and assure successful communication, we use language skills to explicitly convey information to each other, and social abilities such as empathy or perspective taking to infer another person's emotions and mental state. The human cognitive capacity to draw inferences about other peoples' beliefs, intentions and thoughts has been termed mentalizing, theory of mind or cognitive perspective taking. This capacity makes it possible, for instance, to understand that people may have views that differ from our own. Conversely, the capacity to share the feelings of others is called empathy. Empathy makes it possible to resonate with others' positive and negative feelings alike--we can thus feel happy when we vicariously share the joy of others and we can share the experience of suffering when we empathize with someone in pain. Importantly, in empathy one feels with someone, but one does not confuse oneself with the other; that is, one still knows that the emotion one resonates with is the emotion of another. If this self-other distinction is not present, we speak of emotion contagion, a precursor of empathy that is already present in babies.


title: Secure Deduplication with Efficient and Reliable Convergent Key Management

abstract: Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.


title: Comparison of neural network and genetic algorithms for a resource allocation problem

abstract: We compare neural network and genetic algorithm solutions of a resource allocation problem and discuss the relative asymptotic scaling of the two approaches in terms of time and space complexity. We show that for this problem neural networks and genetic algorithms perform similarly. >


title: Using the robotic sixth finger and vibrotactile feedback for grasp compensation in chronic stroke patients

abstract: This paper presents a wearable robotic extra finger used by chronic stroke patients to compensate for the missing hand functions of the paretic limb. The extra finger is worn on the paretic forearm by means of an elastic band, and it is coupled with a vibrotactile ring interface worn on the healthy hand. The robotic finger and the paretic hand act like the two parts of a gripper working together to hold an object. The human user is able to control the flexion/extension of the robotic finger through a switch placed on the ring, while being provided with vibrotactile feedback about the forces exerted by the robotic finger on the environment. To understand how to control the vibrotactile interface to evoke the most effective cutaneous sensations, we carried out perceptual experiments to evaluate its absolute and differential thresholds. Finally, we performed a qualitative experiment, the Franchay Arm Test, with a chronic post-stroke patient presenting a partial loss of sensitivity on the paretic limb. Results show that the proposed system significantly improves the performance of the considered test.


title: Distributing Learning Over Time: The Spacing Effect in Children’s Acquisition and Generalization of Science Concepts

abstract: The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum.


title: Anorexia nervosa: aetiology, assessment, and treatment

abstract: Summary Anorexia nervosa is an important cause of physical and psychosocial morbidity. Recent years have brought advances in understanding of the underlying psychobiology that contributes to illness onset and maintenance. Genetic factors influence risk, psychosocial and interpersonal factors can trigger onset, and changes in neural networks can sustain the illness. Substantial advances in treatment, particularly for adolescent patients with anorexia nervosa, point to the benefits of specialised family-based interventions. Adults with anorexia nervosa too have a realistic chance of achieving recovery or at least substantial improvement, but no specific approach has shown clear superiority, suggesting a combination of re-nourishment and anorexia nervosa-specific psychotherapy is most effective. To successfully fight this enigmatic illness, we have to enhance understanding of the underlying biological and psychosocial mechanisms, improve strategies for prevention and early intervention, and better target our treatments through improved understanding of specific disease mechanisms.


Culture and International Relations


title: The Effect of a Single Episode of Short Duration Heart Rate Variability Biofeedback on Measures of Anxiety and Relaxation States

abstract: This study examines the acute effect of heart rate variability (HRV) biofeedback on anxiety and relaxation states. Eighteen healthy males exposed to work-related stress were randomized into an HRV biofeedback group (BIO) or comparative group (COM). The device used by the COM group appeared identical to the HRV biofeedback device, however, it did not provide biofeedback, rather it displayed a wave which the subjects were instructed to observe. Subjects completed a modified Stroop task before and after a 10-min intervention. The Spielberger Trait Anxiety Inventory (STAIT) was completed at recruitment, and the Spielberger State Anxiety Inventory (STAIS) and Smith Relaxation States Inventory 3 (SRSI3) were completed before and after testing. Subjects rated perceived efficacy and feelings of sleepiness using a visual analogue scale (VAS). Analysis of the VAS scores revealed that the COM group felt sleepier than the BIO group after using the intervention. After testing, HRV biofeedback resulted in large improvements in scores for mindfulness, energized positive feelings and basic relaxation, and the comparative intervention resulted in a moderate improvement in basic relaxation, small improvement in mindfulness, and no improvement in energized positive feelings. Both groups had moderate improvements in transcendence. There was a large decrease in state anxiety in the BIO group and moderate decrease in the COM group. These findings suggest that although there was benefit to both interventions, a single episode of short duration HRV biofeedback led to greater benefit and may be a valuable tool to include in the


title: Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces

abstract: Road traffic jam is one of the major problems related to transportation field in big cities around the globe. The main purpose of our article is providing drivers with an intelligent system to predict the congestion states of the roads. In this paper, we present the architecture of our intelligent congestion prediction system based on the ANN and the data fusion. Our system does not take into account only the historical GPS data but also the real time unpredictable events which have impacts on traffic jams such as accidents. ANN has demonstrated its efficiency in forecasting traffic congestion. The fusion of predicted congestion state, the real time GPS information and the anomalous events using decisional tree has more improved the results. A real time mobile application is provided to drivers in order to help them to discover the traffic state of their destination. The model has been evaluated and validated using big GPS datasets gathered from vehicles circulating in very crowded urban city in Tunisian territory.


title: Lymphoedema: an underestimated health problem

abstract: Summary Background: Lymphoedema/chronic oedema is an important cause of morbidity in the population, but little is known of its epidemiology and impact on patients or health services. Aim: To determine the magnitude of the problem of chronic oedema in the community, and the likely impact of oedema on use of health resources, employment and patient’s quality of life.


title: The Medial Temporal Lobe and Recognition Memory

abstract: AbstractThe ability to recognize a previously experienced stimulus is supported by two processes: recollection of the stimulus in the context of other information associated with the experience, and a sense of familiarity with the features of the stimulus. Although familiarity and recollection are functionally distinct, there is considerable debate about how these kinds of memory are supported by regions in the medial temporal lobes (MTL). Here, we review evidence for the distinction between recollection and familiarity and then consider the evidence regarding the neural mechanisms of these processes. Evidence from neuropsychological, neuroimaging, and neurophysiological studies of humans, monkeys, and rats indicates that different subregions of the MTL make distinct contributions to recollection and familiarity. The data suggest that the hippocampus is critical for recollection but not familiarity. The parahippocampal cortex also contributes to recollection, possibly via the representation and retrieval ...


3D bipedal robotic walking: Models, feedback control, and open problems


title: Distributed algorithms for cooperative control

abstract: Expressing a cooperative control system requires combining tools from control theory and distributed computation. After reviewing several possible formalisms appropriate for the job, the authors settle on the computation and control language and illustrate its main features and advantages using a cooperative tracking task. We demonstrate some of the concepts involved in using a multirobot task. We also discuss CCL's ability to be a programming language as well as a modeling tool, which lets us directly simulate or execute CCL models.


title: Instance-Level Salient Object Segmentation

abstract: Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation.


title: Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice

abstract: Modern applications of stereo vision, such as advanced driver assistance systems and autonomous vehicles, require highest precision when determining the location and velocity of potential obstacles. Subpixel disparity accuracy in selected image regions is therefore essential. Evaluation benchmarks for stereo correspondence algorithms, such as the popular Middlebury and KITTI frameworks, provide important reference values regarding dense matching performance, but do not sufficiently treat local sub-pixel matching accuracy. In this paper, we explore this important aspect in detail. We present a comprehensive statistical evaluation of selected state-of-the-art stereo matching approaches on an extensive dataset and establish reference values for the precision limits actually achievable in practice. For a carefully calibrated camera setup under real-world imaging conditions, a consistent error limit of 1/10 pixel is determined. We present guidelines on algorithmic choices derived from theory which turn out to be relevant to achieving this limit in practice.


title: Simulations of Trapping of Diesel and Gasoline Particulate Matter in Flow-through Devices

abstract: Eulerian–Lagrangian CFD modeling has been used to track eight types of typical diesel and gasoline soot particles through a flow-through substrate with protrusions. It is shown that there exists a minimum trapping efficiency for medium-sized soot particles (dp ≈ 150 nm). Small and large particles are trapped by different mechanisms, and thus appear in different patterns on the substrate walls.


title: Ultrasonic potential field sensor for obstacle avoidance

abstract: We introduce an ultrasonic sensor system that measures artificial potential fields (APFs) directly. The APF is derived from the traveling-times of the transmitted pulses. Advantages of the sensor are that it needs only three transducers, that its design is simple, and that it measures a quantity that can be used directly for simple navigation, such as collision avoidance.


title: Cycle Analysis and Construction of Protographs for QC LDPC Codes With Girth Larger Than 12

abstract: A quasi-cyclic (QC) low-density parity-check (LDPC) code can be viewed as the protograph code with circulant permutation matrices. In this paper, we find all the subgraph patterns of protographs of QC LDPC codes having inevitable cycles of length 2i, i = 6,7,8,9,10, i.e., the cycles existing regardless of the shift values of circulants. It is also derived that if the girth of the protograph is 2g, g ges 2, its protograph code cannot have the inevitable cycles of length smaller than 6g. Based on these subgraph patterns, we propose new combinatorial construction methods of the protographs, whose protograph codes can have girth larger than or equal to 14.


title: Development of a Novel Hybrid-Type Rotary Steerable System for Directional Drilling

abstract: Unlike conventional resources, unconventional resources, such as shale gas and coal bed methane, are situated horizontally under geological formations. To exploit these resources, directional drilling and hydraulic fracturing technologies are required. In directional drilling, the dog leg severity (DLS), which indicates how much the angle changes while drilling 100 ft, is a major issue with respect to the cost and time of drilling. In this paper, we briefly review different types of directional drilling methods and propose a new hybrid-type rotary steerable system (RSS). DLS calculations based on three-point geometry for these systems are suggested. This hybrid RSS combining two conventional types of RSSs: point-the-bit and push-the-bit systems, achieves better steerability. The hybrid mechanism is implemented using hybrid pads with hydraulic cylinders and a spherical joint. The advantages of the proposed system are demonstrated by performing small-scale prototype and cement block drilling tests.


title: Context-Aware Recommendation Based On Review Mining

abstract: Recommender systems (RS) play an important role in many areas including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has attracted many researchers to focus on designing systems that produce personalized recommendations in accordance with available contextual information of users. Comparing with traditional RS which mainly utilize users’ rating history, the context# aware recommender systems (CARS) can result in better performance in various applications. In this paper, we present a context#aware recommender system that extracts contextual information from a textual description of user current situation and use it in combination with user ratings history to compute a utility function over the set of items. The item utility shows how much it is preferable regarding user current context. In our system, the context inference is modeled as a supervised topic#modeling problem in which the set of categories for a contextual attribute constitutes the topic set. As an example application, we used our method to mine hidden contextual data from customers' reviews for hotels and use it to produce context#aware recommendations. Our evaluations suggest that our system can help produce better recommendations in comparison to a standard kNN recommender system.


title: Hand Spring Operated Movement Enhancer (HandSOME) device for hand rehabilitation after stroke

abstract: Hand rehabilitation after stroke is essential for restoring functional independent lifestyles. After stroke, patients often have flexor hypertonia, making it difficult to open their hand for functional grasp. The development and initial testing of a passive hand rehabilitation device is discussed. The device, Hand Spring Operated Movement Enhancer (HandSOME), assists with opening the patient's hand using a series of bungee cords that apply extension torques to the finger joints that compensate for the flexor hypertonia. This results in significant increase in range of motion and functional use when wearing HandSOME, even in severely impaired subjects. Device design, calibration, and range of motion are described as well as functional and usability testing with stroke subjects.


title: Precision grasping based on probabilistic models of unknown objects

abstract: Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasps and executing grasps without displacement. We propose to explicitly take perceptual uncertainty into account during grasp execution. The underlying object representation is a probabilistic signed distance field, which includes both signed distances to the surface and spatially interpretable variances. Based on this representation, we propose a two-stage grasp generation method, which is specifically designed for generating precision grasps. In order to evaluate the whole approach, we perform extensive real world grasping experiments on a set of hard-to-grasp objects. Our approach achieves 78% success rate and shows robustness to the placement orientation.


title: Neural Vector Spaces for Unsupervised Information Retrieval

abstract: We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.