Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.


Empirical analysis of programming language adoption

Some programming languages become widely popular while others fail to grow beyond their niche or disappear altogether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers.
 We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming market supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adoption. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages. The overall number of languages developers are familiar with is independent of age. Finally, when considering intrinsic aspects of languages, developers prioritize expressivity over correctness. They perceive static types as primarily helping with the latter, hence partly explaining the popularity of dynamic languages.


Symbolic Music Genre Transfer with CycleGAN

Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.


An effective way of word-level language identification for code-mixed facebook comments using word-embedding via character-embedding

Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as their medium for communication in earlier days. Despite the fact that content can be written in Unicode characters now, people find it easier to communicate by mixing two or more languages together or lean toward writing their native language in Roman script. These types of data are called code-mixed text. While processing such social-media data, recognizing the language of the text is an important task. In this work, we have developed a system for word-level language identification on code-mixed social media text. The work is accomplished for Tamil-English and Malayalam-English code-mixed Facebook comments. The methodology used for the system is a novel approach which is implemented based on features obtained from character-based embedding technique with the context information and uses a machine learning based classifier, Support Vector Machine for training and testing. An accuracy of 93% was obtained for Malayalam-English and 95% for Tamil-English code-mixed text.


Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study

BACKGROUND
Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden.

OBJECTIVE
The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy.

METHODS
A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability.

RESULTS
Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants.

CONCLUSIONS
Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.


Evaluating and exploring the MYO ARMBAND

Electromyography (EMG) is a technique that studies the electrical activity of the muscle. EMG is the basic component of Myo armband. The MYO ARMBAND is a device which can be used by humans to interact with computers. The myo armband involves the interaction of devices in VE (virtual environment). Working and characteristics of Myo are studied and analyzed. This paper surveys the work on visual analysis of gestures by using Myo armband and the input from keyboard and mouse. We have also performed two tests where participants are allowed to play a game, first by using keyboard and other by using MYO. Discussion and conclusion are also written at the end of this paper.


Multimedia learning in computer science: The effect of fifferent modes of instruction on student understanding

In a series of three experimental studies, we investigate the understanding achieved by learners when studying a complex domain in computer science, that is, the subject of memory management concepts in operating systems. We use three different media combinations to explain the subject of memory management concepts - Animation and text, animation and voice and a combination of animation, voice and text. The students who had no prior knowledge on the subject where given a test on recall and transfer knowledge after viewing the treatments. This paper reports the results of the experiment which may provide useful guidance for instructional designers specifically in the area of computer science.


Fast Hydraulic and Thermal Erosion on the GPU Balázs Jákó

Computer games, TV series, movies, simulators, and many other computer graphics applications use external scenes where a realistic looking terrain is a vital part of the viewing experience. Creating such terrains is a challenging task. In this paper we propose a method that generates realistic virtual terrains by simulation of hydraulic and thermal erosion on a predefined height field terrain. The model is designed to be executed interactively on parallel architectures like graphics processors.


Channel estimation in millimeter wave MIMO systems with one-bit quantization

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.


CRISP-DM 1.0: Step-by-step data mining guide

This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are the exclusive property of the partners of the CRISP-DM All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of the CRISP-DM consortium. Foreword CRISP-DM was conceived in late 1996 by three " veterans " of the young and immature data mining market. DaimlerChrysler (then Daimler-Benz) was already experienced, ahead of most industrial and commercial organizations, in applying data mining in its business operations. SPSS (then ISL) had been providing services based on data mining since 1990 and had launched the first commercial data mining workbench – Clementine – in 1994. NCR, as part of its aim to deliver added value to its Teradata data warehouse customers, had established teams of data mining consultants and technology specialists to service its clients' requirements. At that time, early market interest in data mining was showing signs of exploding into widespread uptake. This was both exciting and terrifying. All of us had developed our approaches to data mining as we went along. Were we doing it right? Was every new adopter of data mining going to have to learn, as we had initially, by trial and error? And from a supplier's perspective, how could we demonstrate to prospective customers that data mining was sufficiently mature to be adopted as a key part of their business processes? A standard process model, we reasoned, non-proprietary and freely available, would address these issues for us and for all practitioners. A year later we had formed a consortium, invented an acronym (CRoss-Industry Standard Process for Data Mining), obtained funding from the European Commission and begun to set out our initial ideas. As CRISP-DM was intended to be industry-, tool-and application-neutral, we knew we had to get input from as wide a range as possible of practitioners and others (such as data warehouse vendors and management consultancies) with a vested interest in data mining. We did this by creating the CRISP-DM Special Interest Group (" The SIG " , as it became known). We launched the SIG by broadcasting an invitation to interested parties to join us in Amsterdam for a day-long …


Translating Collocations for Bilingual Lexicons: A Statistical Approach

Collocations are notoriously difficult for non-native speakers to translate, primarily because they are opaque and cannot be translated on a word-by-word basis. We describe a program named Champollion which, given a pair of parallel corpora in two different languages and a list of collocations in one of them, automatically produces their translations. Our goal is to provide a tool for compiling bilingual lexical information above the word level in multiple languages,for different domains. The algorithm we use is based on statistical methods and produces p-word translations of n-word collocations in which n and p need not be the same. For example, Champollion translates make ... decision, employment equity, and stock market into prendre ... d6cision, 6quit6 en mati6re d'emploi, and bourse respectively. Testing Champollion on three years' worth of the Hansards corpus yielded the French translations of 300 collocations for each year, evaluated at 73% accuracy on average. In this paper, we describe the statistical measures used, the algorithm, and the implementation of Champollion, presenting our results and evaluation.


Adaptive RED : An Algorithm for Increasing the Robustness of RED ’ s Active Queue Management

The RED active queuemanagement algorithmallows network operatorsto simultaneouslyachieve high throughput and low averagedelay. However, the resulting average queuelength is quite sensiti ve to the level of congestion andto theREDparameter settings,andis thereforenotpredictable in advance. Delay being a major componentof thequalityof servicedeliveredto their customers, network operatorswould naturallylike to have a rougha priori estimateof the averagedelaysin their congestedrouters;to achieve suchpredictableaveragedelayswith RED would requireconstanttuning of the parametersto adjustto currenttraffic conditions. Our goal in this paperis to solve this problemwith minimal changesto the overall RED algorithm. To do so,we revisit theAdaptiveREDproposalof Fenget al. from 1997 [6, 7]. We make several algorithmicmodificationsto this proposal,while leaving thebasicideaintact,andthenevaluateits performanceusingsimulation.Wefind thatthis revisedversionof AdaptiveRED,whichcanbeimplemented asasimpleextensionwithin REDrouters,removesthesensitivity to parametersthat affect RED’s performanceand canreliablyachieve aspecifiedtargetaveragequeuelength in a wide variety of traffic scenarios.Basedon extensi ve simulations,we believe that Adaptive RED is sufficiently robustfor deploymentin routers.


Adaptive generic learning for face recognition from a single sample per person

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.


A 10Gb/s 4.1mW 2-IIR + 1-discrete-tap DFE in 28nm-LP CMOS

A half-rate decision feedback equalizer (DFE) with two infinite impulse response (IIR) filters and one discrete-time tap is presented. The two IIR filters have different time constants to cancel the long tail of the pulse response. The discrete-tap cancels the first post-cursor inter-symbol interference term. The system can operate with a low transmit swing of 150mVpp-diff and 24 dB channel loss at the Nyquist frequency while consuming 4.1mW at 10 Gb/s. The receiver, including the DFE, clock buffers and clock phase adjustment, occupies an area of 8,760 μm2 and was fabricated in an ST 28nm LP CMOS process.


A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.


DO INSTITUTIONS MATTER FOR REGIONAL GROWTH AND DEVELOPMENT? THE CASE OF TURKEY

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Beam-Steerable Planar Antenna Using Circular Disc and Four PIN-Controlled Tapered Stubs for WiMAX and WLAN Applications

A planar beam-steerable antenna aimed at WiMAX and WLAN applications is presented. The design uses a single-layer structure, which includes a central circular disc surrounded by four PIN-controlled tapered microstrip stubs. Using the PIN diodes, the stubs change their status from grounded to open-ended mode to provide pattern reconfigurability in four directions. To support WiMAX and WLAN applications, the antenna is designed to operate at IEEE 802.11b/g standard 2.4 GHz with a bandwidth of 150 MHz in all the four states. A prototype on FR4 substrate with thickness of 6.4 mm and radius of 50 mm is designed and tested. The measured and simulated results of the antenna indicate that the direction of the main beam can be successfully controlled at four specific directions ( φ = 0°, 90 °, 180 °, 270 °), at a deflection angle of 35 ° from the boresight with a stable gain of more than 5 dBi and front-to-back ratio of more than 20 dB across the band 2.38-2.53 GHz.


Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data

Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So far, two lines of work have tried to produce a"good" feature vector x from the customer's behavioraldata: (1) application-specific feature engineering using behavioral data and (2) representation learning (such as singular value decomposition or neuralembedding) on behavioral data. Although these approaches successfullyimprove the predictive performance, (1) designing a good feature requiresdomain experts to make a great effort and (2) features obtained fromrepresentation learning are hard to interpret. To overcome these problems, we present a Relational Infinite SupportVector Machine (R-iSVM), a mixture-of-experts model that can leveragebehavioral data. Instead of augmenting the feature vectors of customers, R-iSVM uses behavioral data to find out behaviorally similar customerclusters and constructs a local prediction model at each customer cluster. In doing so, R-iSVM successfully improves the predictive performance withoutrequiring application-specific feature designing and hard-to-interpretrepresentations. Experimental results on three real-world datasets demonstrate the predictiveperformance and interpretability of R-iSVM. Furthermore, R-iSVM can co-existwith previous demographics prediction methods to further improve theirpredictive performance.


Perceptual Design of Haptic Icons

The bulk of applications for haptic feedback employ direct rendering approaches wherein a user touches a virtual model of some “real” thing, often displayed graphically as well. We propose a new class of applications based on abstract messages, ranging from “haptic icons” – brief signals conveying an object’s or event’s state, function or content – to an expressive haptic language for interpersonal communication. Building this language requires us to understand how synthetic haptic signals are perceived, and what they can mean to us. Experiments presented here address the perception question by using an efficient version of Multidimensional Scaling (MDS) to extract perceptual axes for complex haptic icons: once this space is mapped, icons can be designed to maximize both differentiability and individual salience. Results show that a set of icons constructed by varying the frequency, magnitude and shape of 2-sec, time-invariant wave shapes map to two perceptual axes, which differ depending on the signals’ frequency range; and suggest that expressive capability is maximized in one frequency subspace.


Accuracy of deception judgments.

We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individuals regard their interaction partners as honest. We propose that people judge others' deceptions more harshly than their own and that this double standard in evaluating deceit can explain much of the accumulated literature.


LETOR : Benchmark Dataset for Research on Learning to Rank for Information Retrieval

This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that could be used in comparison of existing learning algorithms and in evaluation of newly proposed algorithms, which stood in the way of the related research. To deal with the problem, we have constructed a benchmark dataset referred to as LETOR and distributed it to the research communities. Specifically we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from the datasets, including both conventional features, such as term frequency, inverse document frequency, BM25, and language models for IR, and features proposed recently at SIGIR, such as HostRank, feature propagation, and topical PageRank. We have then packaged LETOR with the extracted features, queries, and relevance judgments. We have also provided the results of several state-ofthe-arts learning to rank algorithms on the data. This paper describes in details about LETOR.


CEET: A Compressed Encrypted & Embedded Technique for Digital Image Steganography

In this information era, digital information sharing and transfer plays a vital role and their use has increased exponentially with the development of technology. Thus providing security of data is a topic of concern. Data hiding is a powerful tool that provides mechanism for securing data over insecure channel by concealing information within information. Steganography inherits data hiding concept and passes information through host files in such a way that the existence of the embedded secret is unknown. This paper presents a joint application of compression, encryption and embedding techniques for implementing digital image steganography. Compression technique generates noise in the image. Inorder to retain the noise distortion to a minimum level, LSB insertion method is used for embedding purpose where the bits are inserted at the last 2 LSB’s of the image. In this proposed technique the secret information is initially compressed and then the resultant bits are encrypted. Finally these encrypted bits are embedded into an image. The main objective is to develop an application that increases the steganographic capacity and enhances the stego image quality while keeping the security intact.


Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.

Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Preand posttests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners’ insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.


Usability Heuristics for Transactional Web Sites

One of the most recognized methods to evaluate usability in software applications is the heuristic evaluation. In this inspection method, Nielsen's heuristics, are the most widely used evaluation instrument. However, there is evidence in the literature which establishes that these heuristics are not appropriate when they are used to measure the level of usability of new emerging categories of software applications. This paper presents a case study where this concept is verified for Transactional Web Sites. Therefore, given the present limitations, a new set of usability heuristics was proposed following a structured and systematic methodology. Finally, fifteen new usability heuristics were obtained as final product of this research. A validation phase allowed to contrast this new proposal with the Nielsen's principles under a real context, where a heuristic evaluation was performed to a Transactional Web Site. The results established that the new set of usability heuristics, which is presented in this study, provides more accurate and promising results than the current proposal of Nielsen.


Knowledge Management Issues in Malaysian Organizations : the Perceptions of Leaders

Knowledge management is a relatively young topic to other older strategic tools, and it has been attracting the attention of researchers and academics over the past decade or so. In this regard globalization is already in many countries including Malaysia like many other developing countries, Malaysia is also striving to fulfil its vision of becoming a developed nation. It is a fast growing country in terms of both economy and technology. However, obstacles for successful knowledge management initiatives will be there for this country, as well. Thus, exploration of the potential barriers is essential before any Malaysian company embarks on a knowledge management journey. This research has shed light to some of the important issues and challenges with reference to knowledge management in Malaysia by providing a more thorough and clear picture of the knowledge management status amongst Malaysian organizations. This could in turn help top managers and decision makers to develop a more insightful agenda to ensure success for their respective knowledge management initiatives.


Generating Extractive Summaries of Scientific Paradigms

Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scientific topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.


Muscular and postural synergies of the human hand.

Because humans have limited ability to independently control the many joints of the hand, a wide variety of hand shapes can be characterized as a weighted combination of just two or three main patterns of covariation in joint rotations, or "postural synergies." The present study sought to align muscle synergies with these main postural synergies and to describe the form of membership of motor units in these postural/muscle synergies. Seventeen joint angles and the electromyographic (EMG) activities of several hand muscles (both intrinsic and extrinsic muscles) were recorded while human subjects held the hand statically in 52 specific shapes (i.e., shaping the hand around 26 commonly grasped objects or forming the 26 letter shapes of a manual alphabet). Principal-components analysis revealed several patterns of muscle synergy, some of which represented either coactivation of all hand muscles, or reciprocal patterns of activity (above and below average levels) in the intrinsic index finger and thumb muscles or (to a lesser extent) in the extrinsic four-tendoned extensor and flexor muscles. Single- and multiunit activity was generally a multimodal function of whole hand shape. This implies that motor-unit activation does not align with a single synergy; instead, motor units participate in multiple muscle synergies. Thus it appears that the organization of the global pattern of hand muscle activation is highly distributed. This organization mirrors the highly fractured somatotopy of cortical hand representations and may provide an ideal substrate for motor learning and recovery from injury.


IT Governance Maturity: Developing a Maturity Model Using the Delphi Method

To advance in maturity, organizations should pay attention to both the hard and soft sides of IT governance (ITG). The hard side is related to processes and structure, the soft side to social aspects like behavior and organizational culture. This paper describes a study to develop an ITG maturity model (MM) that includes both. Our research method is based on literature study, the Delphi method and makes use of a Group Decision Support System. We chose to design a focus area MM. In this type of MM maturity is determined by a set of focus areas. The study reveals one MM as being appropriate for hard ITG. For soft ITG we found no single model appropriate. Soft governance needs more specific capabilities defined for each focus area individually. Based on knowledge from literature and experts we selected models for each focus area. Three alternatives for informal organization need further research.


A maturity model for information governance

Information Governance (IG) as defined by Gartner is the “specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. Includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals”. In this paper, we present how to create an IG maturity model based on existing reference documents. The process is based on existing maturity model development methods. These methods allow for a systematic approach to maturity model development backed up by a well-known and proved scientific research method called Design Science Research. Then, based on the maturity model proposed in this paper, an assessment is conducted and the results are presented, this assessment was conducted as a self-assessment in the context of the EC-funded E-ARK project for the seven pilots of the project. The main conclusion from this initial assessment is that there is much room for improvement with most pilots achieving results between maturity level two and three. As future work, the goal is to analyze other references from different domains, such as, records management. These references will enhance, detail and help develop the maturity model making it even more valuable for all types of organization that deal with information governance.


Method engineering: engineering of information systems development methods and tools

This paper proposes the term method engineering for the research field of the construction of information systems development methods and tools. Some research issues in method engineering are identified. One major research topic in method engineering is discussed in depth: situational methods, i.e. the configuration of a project approach that is tuned to the project at hand. A language and support tool for the engineering of situational methods are discussed.


Developing Maturity Models for IT Management


A Model of Data Warehousing Process Maturity

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.


3D Reconstruction from Hyperspectral Images

3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method to reconstruct a 3D model from hyper spectral images. Our proposed method first generates 3D point sets from images at each wavelength using the typical structure from motion approach. A structural descriptor is developed to characterize the spatial relationship between the points, which allows robust point matching between two 3D models at different wavelength. Then a 3D registration method is introduced to combine all band-level models into a single and complete hyper spectral 3D model. As far as we know, this is the first attempt in reconstructing a complete 3D model from hyper spectral images. This work allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications.


Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

Neural networks have great success in many machine learning applications, but the fundamental learning theory behind them remains largely unsolved. Learning neural networks is NP-hard, but in practice, simple algorithms like stochastic gradient descent (SGD) often produce good solutions. Moreover, it is observed that overparameterization (that is, designing networks whose number of parameters is larger than statistically needed to perfectly fit the data) improves both optimization and generalization, appearing to contradict traditional learning theory. In this work, we extend the theoretical understanding of two and three-layer neural networks in the overparameterized regime. We prove that, using overparameterized neural networks, one can (improperly) learn some notable hypothesis classes, including two and three-layer neural networks with fewer parameters. Moreover, the learning process can be simply done by SGD or its variants in polynomial time using polynomially many samples. We also show that for a fixed sample size, the generalization error of the solution found by some SGD variant can be made almost independent of the number of parameters in the overparameterized network. ∗V1 appears on this date and V2 polishes writing and tightens parameters. Authors sorted in alphabetical order.


A novel compact ka-band high-rejection diplexer based on substrate integrated waveguide

In this paper, a compact diplexer using substrate integrated waveguide (SIW) resonators is introduced. The diplexer consists of two filters and a circulator. The diplexer channels are 4% and 5% relative bandwidth at 32GHz and 33GHz, respectively. The filters are based on SIW resonators, which have higher Q values. This paper also analyzed the factors which affect the Q value of resonators, and describes the designing procedure of Generalized Chebyshev filters, which used in this paper. The circulator also is on account of SIW. It uses inductive windows to increase bandwidth. At last, the paper gives the diplexer's simulation and measure results. And two results are similar with each other.


B2B E-commerce: Frameworks for e-readiness assessment

This paper looks into the development of e- readiness model for B2B e-commerce of Small and Medium Enterprises (SMEs). Drawing on existing research on e-readiness models of ICT (information, Communications Technology) specifically B2B e-commerce technologies, this paper formulates a conceptual framework of B2B e-commerce assessment constructs together with the specific research approach towards the development of a more robust e-readiness model. The research presents a conceptual model and framework that highlight the key factors in B2B e-commerce implementation, index score formulations, focus group evaluations and the recommendation approaches to improve B2B e-commerce readiness.


Hopping of a monopedal robot with a biarticular muscle driven by electromagnetic linear actuators

The compliance of muscles with external forces and the structural stability given by biarticular muscles are important features of animals to realize dynamic whole body motions such as running and hopping in various environments. For this reason, we have been studying an electromagnetic linear actuator. This actuator can emulate the behavior of a human muscle such as the spring-damper characteristics by quick control of the output force (i.e. impedance control) and it is expected to be used as an artificial muscle. In this paper, we develop a monopedal robot possessing bi- and mono-articular muscles implemented by linear actuators. Thanks to the biarticular muscle, the bouncing direction of the robot can be controlled by changing the stiffness ellipse at the endpoint (i.e. foot) of the robot. We confirm that the bouncing direction of the robot can be controlled and hopping can be achieved by changing the stiffness ellipse.


The Influence of Context on Dialogue Act Recognition

This article presents a deep analysis of the influence of context information on dialogue act recognition. We performed experiments on the annotated subsets of three different corpora: the widely explored Switchboard Dialog Act Corpus, as well as the unexplored LEGO and Cambridge Restaurant corpora. In contrast with previous work, especially in what concerns the Switchboard corpus, we used an event-based classification approach, using Support Vector Machines (SVMs), instead of the more common sequential approaches, such as Hidden Markov Models (HMMs). We opted for such an approach so that we could control the amount of provided context information and, thus, explore its range of influence. Our base features consist of n-grams, punctuation, and whwords. Context information is obtained from previous utterances and provided in three ways – n-grams, n-grams tagged with relative position, and dialogue act classifications. A comparative study was conducted to evaluate the performance of the three approaches. From it, we were able to assess the importance of context information on dialogue act recognition, as well as its range of influence for each of the three selected representations. In addition to the conclusions originated by the analysis, this work also produced results that advance the state-of-the-art, especially considering previous work on the Switchboard corpus. Furthermore, since, to our knowledge, the Email addresses: eugenio.ribeiro@l2f.inesc-id.pt (Eugénio Ribeiro), ricardo.ribeiro@inesc-id.pt (Ricardo Ribeiro), david.matos@inesc-id.pt (David Martins de Matos) Preprint submitted to Computer Speech and Language June 3, 2015 ar X iv :1 50 6. 00 83 9v 1 [ cs .C L ] 2 J un 2 01 5 remaining datasets had not been previously explored for this task, our experiments can be used as baselines for future work on those corpora.


Are More Data Always Better for Factor Analysis ?

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors and that yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can also arise if forecasting power is provided by a factor that is dominant in a small dataset but is a dominated factor in a larger dataset. In a real time forecasting exercise, we find that factors extracted from as few as 40 pre-screened series often yield satisfactory or even better results than using all 147 series. Our simulation analysis is unique in that special attention is paid to cross-correlated idiosyncratic errors, and we also allow the factors to have weak loadings on groups of series. It thus allows us to better understand the properties of the principal components estimator in empirical applications. ∗Graduate School of Business, 719 Uris Hall, Columbia University, New York, NY. Email: jb903@columbia.edu †Corresponding author: Department of Economics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218. Email: Serena.Ng@jhu.edu The authors would like to thank National Science Foundation for financial support. We thank Pritha Mitra for useful research assistance.


Attention, please! A Critical Review of Neural Attention Models in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area.


Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

While Deep Neural Networks (DNNs) have achieved tremendous success for large vocabulary continuous speech recognition (LVCSR) tasks, training of these networks is slow. One reason is that DNNs are trained with a large number of training parameters (i.e., 10-50 million). Because networks are trained with a large number of output targets to achieve good performance, the majority of these parameters are in the final weight layer. In this paper, we propose a low-rank matrix factorization of the final weight layer. We apply this low-rank technique to DNNs for both acoustic modeling and language modeling. We show on three different LVCSR tasks ranging between 50-400 hrs, that a low-rank factorization reduces the number of parameters of the network by 30-50%. This results in roughly an equivalent reduction in training time, without a significant loss in final recognition accuracy, compared to a full-rank representation.


Single-step assembly of a gene and entire plasmid from large numbers of oligodeoxyribonucleotides.

Here, we describe assembly PCR as a method for the synthesis of long DNA sequences from large numbers of oligodeoxyribonucleotides (oligos). The method, which is derived from DNA shuffling [Stemmer, Nature 370 (1994a) 389-391], does not rely on DNA ligase but instead relies on DNA polymerase to build increasingly longer DNA fragments during the assembly process. A 1.1-kb fragment containing the TEM-1 beta-lactamase-encoding gene (bla) was assembled in a single reaction from a total of 56 oligos, each 40 nucleotides (nt) in length. The synthetic gene was PCR amplified and cloned in a vector containing the tetracycline-resistance gene (TcR) as the sole selectable marker. Without relying on ampicillin (Ap) selection, 76% of the TcR colonies were ApR, making this approach a general method for the rapid and cost-effective synthesis of any gene. We tested the range of assembly PCR by synthesizing, in a single reaction vessel containing 134 oligos, a high-molecular-mass multimeric form of a 2.7-kb plasmid containing the bla gene, the alpha-fragment of the lacZ gene and the pUC origin of replication. Digestion with a unique restriction enzyme, followed by ligation and transformation in Escherichia coli, yielded the correct plasmid. Assembly PCR is well suited for several in vitro mutagenesis strategies.


A Complete End-to-End Speaker Verification System Using Deep Neural Networks: From Raw Signals to Verification Result

End-to-end systems using deep neural networks have been widely studied in the field of speaker verification. Raw audio signal processing has also been widely studied in the fields of automatic music tagging and speech recognition. However, as far as we know, end-to-end systems using raw audio signals have not been explored in speaker verification. In this paper, a complete end-to-end speaker verification system is proposed, which inputs raw audio signals and outputs the verification results. A pre-processing layer and the embedded speaker feature extraction models were mainly investigated. The proposed pre-emphasis layer was combined with a strided convolution layer for pre-processing at the first two hidden layers. In addition, speaker feature extraction models using convolutionallayer and long short-term memory are proposed to be embedded in the proposed end-to-end system.


The Central Nervous System and the Gut Microbiome

Neurodevelopment is a complex process governed by both intrinsic and extrinsic signals. While historically studied by researching the brain, inputs from the periphery impact many neurological conditions. Indeed, emerging data suggest communication between the gut and the brain in anxiety, depression, cognition, and autism spectrum disorder (ASD). The development of a healthy, functional brain depends on key pre- and post-natal events that integrate environmental cues, such as molecular signals from the gut. These cues largely originate from the microbiome, the consortium of symbiotic bacteria that reside within all animals. Research over the past few years reveals that the gut microbiome plays a role in basic neurogenerative processes such as the formation of the blood-brain barrier, myelination, neurogenesis, and microglia maturation and also modulates many aspects of animal behavior. Herein, we discuss the biological intersection of neurodevelopment and the microbiome and explore the hypothesis that gut bacteria are integral contributors to development and function of the nervous system and to the balance between mental health and disease.


Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use

This research investigated the associations between personality traits and both normal and problematic massively multiplayer online role playing game (MMORPGs) play, as measured by a self report scale. A total of 225 participants completed the online questionnaire, 66 of whom played MMORPGs. Correlational analyses indicated that low levels of functional impulsivity and agreeableness alongside high levels of verbal aggression and video game dependency were associated with greater amount of time spent playing MMORPGs. When comparing problematic and non-problematic MMORPG players directly, problematic players were found to be lower in self regulation, dysfunctional impulsivity and agreeableness, suggesting that these traits may be important in the development and maintenance of problematic


A Graduated Assignment Algorithm for Graph Matching

A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(/m), where land mare the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.


Vehicle black box with 24GHz FMCW radar

This paper presents vehicle black box using camera image and 24GHz Frequency Modulation Continuous Wave (FMCW) radar. Currently, almost all of vehicle black boxes are recording driving data using a single camera. They have some problems on low image quality and narrow viewing angle. In addition, it is difficult to record a clean image when the weather is in bad condition due to heavy rain, snow and night. These problems may have some troubles to investigate car accident accurately. In this paper, we propose a novel black box fusing data from radar and cameras. We estimate the distance and velocity of obstacle using the Doppler Effect and FMCW radar. In particular, we compare various Direction of Arrival (DOA) algorithms for estimation of obstacle angle. The experimental results indicate that the Multiple Signal Classification (MUSIC) is more accurate compared to other methods. Moreover, we formulate the vehicle tracking image through the camera image processing and radar information. This image can be used to widen viewing angle and it may be helpful to investigate a car accident. The experimental study results indicate that the new black box overcome the disadvantages of the current black boxes and may be useful for safe driving and accurate accident investigation.


Exploring Consumers ’ Payment Behaviours at Completing MicroTransactions with Vending Machines in Sweden

.........................................................................................................................................
  2 SAMMANFATTNING
  ........................................................................................................................
  2 ACKNOWLEDGMENTS
  ...................................................................................................................
  3 CONTENT
  ...........................................................................................................................................
  4 1.
   INTRODUCTION
  .......................................................................................................................
  6 1.1.
   PROBLEM
  FORMULATION
  AND
  PURPOSE
  ........................................................................................
  7 2.
   BACKGROUND
  ..........................................................................................................................
  8 2.1.
   VENDING
  MACHINE
  IN
  SWEDEN
  ........................................................................................................
  8 2.1.1.
   Payment
  Procedures
  at
  Selecta
  Vending
  Machine
  ........................................................
  9 2.2.
   RELEVANT
  STUDIES
  ..........................................................................................................................
  14 3.
   THEORETICAL
  FRAMWORK
  .............................................................................................
  16 3.1.
   ROGERS
  –
  DIFFUSION
  OF
  INNOVATION
  THEORY
  ..........................................................................
  16 3.1.1.
   Relative
  Advantage
  ..................................................................................................................
  16 3.1.2.
   Compatibility
  ..............................................................................................................................
  17 3.1.3.
   Complexity
  ...................................................................................................................................
  17 3.2.
   TRUST
  AND
  SECURITY
  .......................................................................................................................
  17 3.3.
   USE
  SITUATION
  ..................................................................................................................................
  17 3.4.
   MONEY’S
  PAYMENT
  RITUAL
  ............................................................................................................
  18 4.
   METHODOLOGY
  AND
  DATA
  COLLECTION
  ...................................................................
  19 4.1.
   OBSERVATIONS
  ..................................................................................................................................
  19 4.2.
   SEMI-‐STRUCTURED
  INTERVIEWS
  ....................................................................................................
  19 4.3.
   DATA
  ANALYSES
  ................................................................................................................................
  19 4.4.
   VALIDITY
  AND
  RELIABILITY
  ............................................................................................................
  20 4.5.
   ETHICS
  .................................................................................................................................................
  20 5.
   RESULTS
  .................................................................................................................................
  21 5.1.
   CONSUMERS’
  CHARACTERISTICS
  .....................................................................................................
  21 5.2.
   OBSERVATION
  AND
  INTERVIEW
  RESULTS
  ....................................................................................
  22 5.2.1.
   Relative
  Advantage
  ..................................................................................................................
  22 5.2.2.
   Compatibility
  ..............................................................................................................................
  22 5.2.3.
   Complexity
  ...................................................................................................................................
  23 5.2.4.
   Trust
  and
  Security
  ....................................................................................................................
  24 5.2.5.
   Impact
  of
  Unexpected
  Circumstances
  ..............................................................................
  26 5.2.6.
   Impact
  of
  Money’s
  Payment
  Ritual
  ...................................................................................
  27 6.
   DISCUSSION
  ...........................................................................................................................
  28 6.1.
   RELATIVE
  ADVANTAGE
  .....................................................................................................................
  28 6.2.
   COMPATIBILITY
  ..................................................................................................................................
  28


An analysis of the GTZAN music genre dataset

A significant amount of work in automatic music genre recognition has used a dataset whose composition and integrity has never been formally analyzed. For the first time, we provide an analysis of its composition, and create a machine-readable index of artist and song titles. We also catalog numerous problems with its integrity, such as replications, mislabelings, and distortions.


The status of HTS ship propulsion motor developments

The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor


Standoff Detection of Weapons and Contraband in the 100 GHz to 1 THz Region

The techniques and technologies currently being investigated to detect weapons and contraband concealed on persons under clothing are reviewed. The basic phenomenology of the atmosphere and materials that must be understood in order to realize such a system are discussed. The component issues and architectural designs needed to realize systems are outlined. Some conclusions with respect to further technology developments are presented.


Beyond pixels: Exploiting camera metadata for photo classification

Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. We consider three problems, indoor-outdoor classification, sunset detection, and manmade-natural classification. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for each problem. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the three problems using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.


Outlier Rejection for Absolute Pose Estimation with Known Orientation

Estimating the pose of a camera is a core problem in many geometric vision applications. While there has been much progress in the last two decades, the main difficulty is still dealing with data contaminated by outliers. For many scenes, e.g. with poor lightning conditions or repetitive textures, it is common that most of the correspondences are outliers. For real applications it is therefore essential to have robust estimation methods. In this paper we present an outlier rejection method for absolute pose estimation. We focus on the special case when the orientation of the camera is known. The problem is solved by projecting to a lower dimensional subspace where we are able to efficiently compute upper bounds on the maximum number of inliers. The method guarantees that only correspondences which cannot belong to an optimal pose are removed. In a number of challenging experiments we evaluate our method on both real and synthetic data and show improved performance compared to competing methods.


MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites

The increased use of the Internet as a new tool in communication has changed the way people interact. This fact is even more evident in the recent development and use of friend-networking sites. However, no research has evaluated these sites and their impact on college students. Therefore, the present study was conducted to evaluate: (a) why people use these friend-networking sites, (b) what the characteristics are of the typical college user, and (c) what uses and gratifications are met by using these sites. Results indicated that the vast majority of college students are using these friend-networking sites for a significant portion of their day for reasons such as making new friends and locating old friends. Additionally, both men and women of traditional college age are equally engaging in this form of online communication with this result holding true for nearly all ethnic groups. Finally, results showed that many uses and gratifications are met by users (e.g., "keeping in touch with friends"). Results are discussed in light of the impact that friend-networking sites have on communication and social needs of college students.


Is e-Learning the Solution for Individual Learning?.

Despite the fact that e-Learning exists for a relatively long time, it is still in its infancy. Current eLearning systems on the market are limited to technical gadgets and organizational aspects of teaching, instead of supporting the learning. As a result the learner has become deindividualized and demoted to a noncritical homogenous user. One way out of this drawback is the creation of individual e-Learning materials. For this purpose a flexible multidimensional data model and the generation of individual content are the solution. It is necessary to enable the interaction between the learners and the content in e-Learning systems in the same manner.


Human memory ; A proposed system and its control processes


Answering Location-Aware Graph Reachability Queries on GeoSocial Data

Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.


Distributed Representation of Subgraphs

Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to nd network embeddings in order to exploit machine learning algorithms for mining tasks like node classication and edge prediction. However, most of the work focuses on nding distributed representations of nodes, which are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we propose Sub2Vec, an unsupervised scalable algorithm to learn feature representations of arbitrary subgraphs. We provide means to characterize similarties between subgraphs and provide theoretical analysis of Sub2Vec and demonstrate that it preserves the so-called local proximity. We also highlight the usability of Sub2Vec by leveraging it for network mining tasks, like community detection. We show that Sub2Vec gets signicant gains over stateof-the-art methods and node-embedding methods. In particular, Sub2Vec oers an approach to generate a richer vocabulary of features of subgraphs to support representation and reasoning. ACM Reference format: Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan and B. Aditya Prakash. 2016. Distributed Representation of Subgraphs. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 9 pages. DOI: 10.1145/nnnnnnn.nnnnnnn


Employee job attitudes and organizational characteristics as predictors of cyberloafing

Cyberloafing is the personal use of the Internet by employees while at work. The purpose of this study is to examine whether employee job attitudes, organizational characteristics, attitudes towards cyberloafing, and other non-Internet loafing behaviors serve as antecedents to cyberloafing behaviors. We hypothesize that the employee job attitudes of job involvement and intrinsic involvement are related to cyberloafing. In addition, we hypothesize that organizational characteristics including the perceived cyberloafing of one’s coworkers and managerial support for internet usage are related to cyberloafing. We also hypothesize that attitudes towards cyberloafing and the extent to which employees participate in non-Internet loafing behaviors (e.g., talking with coworkers, running personal errands) will both be related to cyberloafing. One hundred and forty-three working professional from a variety of industries were surveyed regarding their Internet usage at work. As hypothesized, the employee job attitudes of job involvement and intrinsic involvement were negatively related to cyberloafing. Also as predicted, the organizational characteristics of the perceived cyberloafing of one’s coworkers and managerial support for internet usage were positively related to cyberloafing. Finally, results showed that attitudes towards cyberloafing and participation in non-Internet loafing behaviors were positively related to cyberloafing. Implications for both organizations and employees are discussed. 2011 Elsevier Ltd. All rights reserved.


Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove

This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.


Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study

Face recognition has become a ubiquitous way of establishing identity in many applications. Gender transformation therapy induces changes to face on both for structural and textural features. A challenge for face recognition system is, therefore, to reliably identify the subjects after they undergo gender change while the enrolment images correspond to pre-change. In this work, we propose a new framework based on augmenting and fine-tuning deep Residual Network-50 (ResNet-50). We employ YouTube database with 37 subjects whose images are self-captured to evaluate the performance of state-of-the-schemes. Obtained results demonstrate the superiority of the proposed scheme over twelve different state-of-the-art schemes with an improved Rank — 1 recognition rate.


Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness

Since Internet has become an excellent source of consumer reviews, the area of sentiment analysis (also called sentiment extraction, opinion mining, opinion extraction, and sentiment mining) has seen a large increase in academic interest over the last few years. Sentiment analysis mines opinions at word, sentence, and document levels, and gives sentiment polarities and strengths of articles. As known, the opinions of consumers are expressed in sentiment Chinese phrases. But due to the fuzziness of Chinese characters, traditional machine learning techniques can not represent the opinion of articles very well. In this paper, we propose a multi-strategy sentiment analysis method with semantic fuzziness to solve the problem. The results show that this hybrid sentiment analysis method can achieve a good level of effectiveness.


Online shopping behavior model: A literature review and proposed model

In this study, we conducted extensive reviews of online shopping literatures and proposed a hierarchy model of online shopping behavior. We collected 47 studies and classified them by variables used. Some critical points were found that research framework, methodology, and lack of cross-cultural comparison, etc So we developed a cross-cultural model of online shopping including shopping value, attitudes to online retailer's attributes and online purchasing based on the integrated V-A-B model.


Spinal curvature determination from scoliosis X-Ray image using sum of squared difference template matching

Scoliosis is a disorder in which there is a sideways curve of the spine. Curve are often S-shaped or C-shaped. One of the methods to ascertain a patient with scoliosis is through using Cobb angle measurement. The importance of the automatic spinal curve detection system is to detect any spinal disorder quicker and faster. This study is intended as a first step based on computer-aided diagnosis. The spinal detection method that we propose is using template matching based on Sum of Squared Difference (SSD). This method is used to estimate the location of the vertebra. By using polynomial curve fitting, a spinal curvature estimation can be done. This paper discusses the performance of SSD method used to detect a variety of data sources of X-Ray from numerous patients. The results from the implementation indicate that the proposed algorithm can be used to detect all the X-ray images. The best result in this experiment has 96.30% accuracy using 9-subdivisions poly 5 algorithm, and the average accuracy is 86.01%.


Multitask Compressive Sensing

Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N << M this is performed under the assumption that u is sparse in the basis represented by the matrix Psi RMtimesM. It has been demonstrated that with appropriate design of the compressive measurements used to define v, the decompressive mapping vrarru may be performed with error ||u-u||2 2 having asymptotic properties analogous to those of the best adaptive transform-coding algorithm applied in the basis Psi. The mapping vrarru constitutes an inverse problem, often solved using l1 regularization or related techniques. In most previous research, if L > 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.


Two cortical systems for memory-guided behaviour

Although the perirhinal cortex (PRC), parahippocampal cortex (PHC) and retrosplenial cortex (RSC) have an essential role in memory, the precise functions of these areas are poorly understood. Here, we review the anatomical and functional characteristics of these areas based on studies in humans, monkeys and rats. Our Review suggests that the PRC and PHC–RSC are core components of two separate large-scale cortical networks that are dissociable by neuroanatomy, susceptibility to disease and function. These networks not only support different types of memory but also appear to support different aspects of cognition.


Sexual arousal patterns of bisexual men.

There has long been controversy about whether bisexual men are substantially sexually aroused by both sexes. We investigated genital and self-reported sexual arousal to male and female sexual stimuli in 30 heterosexual, 33 bisexual, and 38 homosexual men. In general, bisexual men did not have strong genital arousal to both male and female sexual stimuli. Rather, most bisexual men appeared homosexual with respect to genital arousal, although some appeared heterosexual. In contrast, their subjective sexual arousal did conform to a bisexual pattern. Male bisexuality appears primarily to represent a style of interpreting or reporting sexual arousal rather than a distinct pattern of genital sexual arousal.


Towards a Distributed Large-Scale Dynamic Graph Data Store

In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.


Automatic assessment of macular edema from color retinal images

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.


Shoulder impingement revisited: evolution of diagnostic understanding in orthopedic surgery and physical therapy

“Impingement syndrome” is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that “impingement syndrome” is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of “impingement syndrome”. Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of “anterior shoulder pain”. While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of “impingement syndrome”. For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.


Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images

Recently, several machine learning methods for gender classification from frontal facial images have been proposed. Their variety suggests that there is not a unique or generic solution to this problem. In addition to the diversity of methods, there is also a diversity of benchmarks used to assess them. This gave us the motivation for our work: to select and compare in a concise but reliable way the main state-of-the-art methods used in automatic gender recognition. As expected, there is no overall winner. The winner, based on the accuracy of the classification, depends on the type of benchmarks used.


Efficient Support Vector Classifiers for Named Entity Recognition

NamedEntity (NE) recognitionis a task in which proper nouns and numerical information are extractedfrom documentsandareclassifiedinto categoriessuchas person,organization,and date. It is a key technologyof InformationExtractionand Open-DomainQuestionAnswering.First,weshow thatanNE recognizerbasedonSupportVectorMachines(SVMs)givesbetterscoresthanconventional systems.However, off-the-shelfSVM classifiersare too inefficient for this task.Therefore,we presenta methodthat makes the systemsubstantiallyfaster . This approachcan also be applied to other similar taskssuchaschunkingandpart-of-speechtagging. We alsopresentanSVM-basedfeatureselection methodandanefficient trainingmethod.


The Bad Boys of Cyberspace: Deviant Behavior in a Multimedia Chat Community

A wide variety of deviant behavior may arise as the population of an online multimedia community increases. That behavior can span the range from simple mischievous antics to more serious expressions of psychopathology, including depression, sociopathy, narcissism, dissociation, and borderline dynamics. In some cases the deviant behavior may be a process of pathological acting out—in others, a healthy attempt to work through. Several factors must be taken into consideration when explaining online deviance, such as social/cultural issues, the technical infrastructure of the environment, transference reactions, and the effects of the ambiguous, anonymous, and fantasy-driven atmosphere of cyberspace life. In what we may consider an "online community psychology," intervention strategies for deviant behavior can be explored along three dimensions: preventative versus remedial, user versus superuser based, and automated versus interpersonal.


Bayesian Optimization with Empirical Constraints ( PhD Proposal )

This work is motivated by the experimental design problem of optimizing the power output of nano-enhanced microbial fuel cells. Microbial fuel cells (MFCs) (Bond and Lovley, 2003; Fan et al., 2007; Park and Zeikus, 2003; Reguera, 2005) use micro-organisms to break down organic matter and generate electricity. For a particular MFC design, it is critical to optimize the biological energetics and the microbial/electrode interface of the system, which research has shown to depend strongly on the surface properties of the anodes (Park and Zeikus, 2003; Reguera, 2005). This motivates the design of nano-enhanced anodes, where nano-structures (e.g. carbon nano-wire) are grown on the anode surface to improve the MFC’s power output. Unfortunately, there is little understanding of the interaction between various possible nano-enhancements and MFC capabilities for different micro-organisms. Thus, optimizing anode design for a particular application is largely guess work. Our goal is to develop algorithms to aid this process. Bayesian optimization (Jones, 2001a; Brochu et al., 2009) has been widely used for experimental design problems where the goal is to optimize an unknown function f(·), that is costly to evaluate. In general, we are interested in finding the point x∗ ∈ X d ⊂ Rd such that: x∗ = argmax x∈X d f(x), (1)


I know what you did on your smartphone: Inferring app usage over encrypted data traffic

Smartphones and tablets are now ubiquitous in many people's lives and are used throughout the day in many public places. They are often connected to a wireless local area network (IEEE 802.11 WLANs) and rely on encryption protocols to maintain their security and privacy. In this paper, we show that even in presence of encryption, an attacker without access to encryption keys is able to determine the users' behavior, in particular, their app usage. We perform this attack using packet-level traffic analysis in which we use side-channel information leaks to identify specific patterns in packets regardless of whether they are encrypted or not. We show that just by collecting and analyzing small amounts of wireless traffic, one can determine what apps each individual smartphone user in the vicinity is using. Furthermore, and more worrying, we show that by using these apps the privacy of the user is more at risk compared to using online services through browsers on mobile devices. This is due to the fact that apps generate more identifiable traffic patterns. Using random forests to classify the apps we show that we are able to identify individual apps, even in presence of noise, with great accuracy. Given that most online services now provide native apps that may be identified by this method, these attacks represent a serious threat to users' privacy.


An Intrusion-Detection Model

A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system.


Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection

Lee, Stolfo, and Mok have previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security itself is fuzzy. We present a modification of a previously reported algorithm for mining fuzzy association rules, define the concept of fuzzy frequency episodes, and present an original algorithm for mining fuzzy frequency episodes. We add a normalization step to the procedure for mining fuzzy association rules in order to prevent one data instance from contributing more than others. We also modify the procedure for mining frequency episodes to learn fuzzy frequency episodes. Experimental results show the utility of fuzzy association rules and fuzzy frequency episodes in intrusion detection. Draft: Updated version published in the International Journal of Intelligent Systems, Volume 15, No. I, August 2000 3


Context-Aware Time Series Anomaly Detection for Complex Systems

Systems with several components interacting to accomplish challenging tasks are ubiquitous; examples include large server clusters providing “cloud computing”, manufacturing plants, automobiles, etc. Our relentless efforts to improve the capabilities of these systems inevitably increase their complexity as we add more components or introduce more dependencies between existing ones. To tackle this surge in distributed system complexity, system operators collect continuous monitoring data from various sources including hardware and software-based sensors. A large amount of this data is either in the form of time-series or contained in logs, e.g., operators’ activity, system event, and error logs, etc. In this paper, we propose a framework for mining system operational intelligence from massive amount of monitoring data that combines the time series data with information extracted from text-based logs. Our work is aimed at systems where logs capture the context of a system’s operations and the time-series data record the state of different components. This category includes a wide variety of systems including IT systems (compute clouds, web services’ infrastructure, enterprise computing infrastructure, etc.) and complex physical systems such as manufacturing plants. We instantiate our framework for Hadoop. Our preliminary results using both real and synthetic datasets show that the proposed context-aware approach is more effective for detecting anomalies compared to a time series only approach.


On Cognitive Computing

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing. [Article copies are available for purchase from InfoSci-on-Demand.com]


Market-Based Multirobot Coordination: A Survey and Analysis

Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges


GraphLab: A New Framework For Parallel Machine Learning

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.