title: Signaling sarcasm : From hyperbole to hashtag

abstract: The use of hashtags such as #sarcasm reduces the further use of linguistic markers of sarcasm in tweets.Hashtags such as #sarcasm appear to be the extralinguistic equivalent of non-verbal expressions in live interaction.Sarcastic hashtags are 90% appropriate.Sarcastic tweets without hashtags are hard to distinguish from non-sarcastic hyperbolic tweets.In French tweets, the hashtag #sarcasme conveys a polarity switch less frequently than in Dutch. To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with a hashtag such as '#sarcasm'. We collected a training corpus of about 406 thousand Dutch tweets with hashtag synonyms denoting sarcasm. Assuming that the human labeling is correct (annotation of a sample indicates that about 90% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a sample of a day's stream of 2.25 million Dutch tweets. Of the 353 explicitly marked tweets on this day, we detect 309 (87%) with the hashtag removed. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 35% of the top-250 ranked tweets are indeed sarcastic. Analysis indicates that the use of hashtags reduces the further use of linguistic markers for signaling sarcasm, such as exclamations and intensifiers. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of non-verbal expressions that people employ in live interaction when conveying sarcasm. Checking the consistency of our finding in a language from another language family, we observe that in French the hashtag '#sarcasme' has a similar polarity switching function, be it to a lesser extent.


title: Iron loss reduction in an interior PM automotive alternator

abstract: This paper examines the iron loss characteristics of a high-flux interior PM machine. The machine was designed as a concept demonstrator for a 6 kW automotive alternator and has a wide field-weakening range. Initial experimental results revealed a high iron loss during field-weakening operation. Finite-element analysis was used to investigate the cause of the high iron losses and to predict their magnitude. The effects of changes in the machine design were examined in order to reduce iron losses and hence improve the machine performance.


title: Global Optimality in Neural Network Training

abstract: The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. This paper provides sufficient conditions to guarantee that local minima are globally optimal and that a local descent strategy can reach a global minima from any initialization. Our conditions require both the network output and the regularization to be positively homogeneous functions of the network parameters, with the regularization being designed to control the network size. Our results apply to networks with one hidden layer, where size is measured by the number of neurons in the hidden layer, and multiple deep subnetworks connected in parallel, where size is measured by the number of subnetworks.


GLASS AS A LOAD BEARING MATERIAL IN YACHT STRUCTURES


Data mining diabetic databases: Are rough sets a useful addition


title: A robotics-based approach to foster programming skills and computational thinking: Pilot experience in the classroom of early childhood education

abstract: This document has the purpose to present some results obtained in the pilot experience, play and program with Bee-Bot. The activities were developed in the framework of the doctoral research project whose purpose is the design and integration of learning activities with robotics to foster programming skills and computational thinking in the classroom of early childhood. Teachers and students of the second cycle of early childhood education of a concerted school participated in the experience during 2016-2017 academic period. School is in Salamanca, Spain. The activity allowed students to solve programming challenges using the Bee-Bot floor robot. Instruments were used to collect data, such as: questionnaires, interviews, rubrics and field diary. In general terms, the results obtained were positive. The technical, pedagogical and social aspects proposed in this research have received the favorable acceptance of teachers and students. Therefore, the information generated allowed to strengthen the design, structure and evaluation of the robotics program would be used in later stages of the investigation.


title: Stitching: pen gestures that span multiple displays

abstract: Stitching is a new interaction technique that allows users to combine pen-operated mobile devices with wireless networking by using pen gestures that span multiple displays. To stitch, a user starts moving the pen on one screen, crosses over the bezel, and finishes the stroke on the screen of a nearby device. Properties of each portion of the pen stroke are observed by the participating devices, synchronized via wireless network communication, and recognized as a unitary act performed by one user, thus binding together the devices. We identify the general requirements of stitching and describe a prototype photo sharing application that uses stitching to allow users to copy images from one tablet to another that is nearby, expand an image across multiple screens, establish a persistent shared workspace, or use one tablet to present images that a user selects from another tablet. We also discuss design issues that arise from proxemics, that is, the sociological implications of users collaborating in close quarters.


title: Sancus 2.0: A Low-Cost Security Architecture for IoT Devices

abstract: The Sancus security architecture for networked embedded devices was proposed in 2013 at the USENIX Security conference. It supports remote (even third-party) software installation on devices while maintaining strong security guarantees. More specifically, Sancus can remotely attest to a software provider that a specific software module is running uncompromised and can provide a secure communication channel between software modules and software providers. Software modules can securely maintain local state and can securely interact with other software modules that they choose to trust. Over the past three years, significant experience has been gained with applications of Sancus, and several extensions of the architecture have been investigated—both by the original designers as well as by independent researchers. Informed by these additional research results, this journal version of the Sancus paper describes an improved design and implementation, supporting additional security guarantees (such as confidential deployment) and a more efficient cryptographic core. We describe the design of Sancus 2.0 (without relying on any prior knowledge of Sancus) and develop and evaluate a prototype FPGA implementation. The prototype extends an MSP430 processor with hardware support for the memory access control and cryptographic functionality required to run Sancus. We report on our experience using Sancus in a variety of application scenarios and discuss some important avenues of ongoing and future work.


title: Multirate Digital Filters, Filter Banks, Polyphase Networks, and Applications:

abstract: The basic concepts and building blocks in multirate digital signal processing (DSP), including the digital polyphase representation, are reviewed. Recent progress, as reported by several authors in this area, is discussed. Several applications are described, including subband coding of waveforms, voice privacy systems, integral and fractional sampling rate conversion (such as in digital audio), digital crossover networks, and multirate coding of narrowband filter coefficients. The M-band quadrature mirror filter (QMF) bank is discussed in considerable detail, including an analysis of various errors and imperfections. Recent techniques for perfect signal reconstruction in such systems are reviewed. The connection between QMF banks and other related topics, such as block digital filtering and periodically time-varying systems, is examined in a pseudo-circulant-matrix framework. Unconventional applications of the polyphase concept are discussed. >


Flexible and Stretchable Electronics Paving the Way for Soft Robotics


title: Deep Multimodal Fusion: A Hybrid Approach

abstract: We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.


title: A Neural Stochastic Volatility Model

abstract: In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based model \emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol}, on average negative log-likelihood.


title: EEVi –Framework and Guidelines to Evaluate the Effectiveness of Cyber-Security Visualization

abstract: Cyber-security visualization aims to reduce security analysts’ workload by presenting information as visual analytics instead of a string of text and characters. However, the adoption of the resultant visualizations by security analysts, is not widespread. The literature indicates a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for the low adoption rate. Consequently, this article addresses the research gap by introducing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ is defined as the features of visualization that are critical for an analyst to competently perform a certain task. EEVi has been developed by analyzing qualitative data which led to the formation of cognitive relationships (called links) between data. These relationships acted as guidelines for effective cyber-security visualization to perform tasks. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis of the framework presented, demonstrates how EEVi can be put into practice using the guidelines for effective cyber- security visualization. The guidelines can be used to guide visualization developers to create effective visualizations for security analysts based on their requirements.


title: Executive Functioning in Highly Talented Soccer Players

abstract: Executive functions might be important for successful performance in sports, particularly in team sports requiring quick anticipation and adaptation to continuously changing situations in the field. The executive functions motor inhibition, attention and visuospatial working memory were examined in highly talented soccer players. Eighty-four highly talented youth soccer players (mean age 11.9), and forty-two age-matched amateur soccer players (mean age 11.8) in the age range 8 to 16 years performed a Stop Signal task (motor inhibition), the Attention Network Test (alerting, orienting, and executive attention) and a visuospatial working memory task. The highly talented soccer players followed the talent development program of the youth academy of a professional soccer club and played at the highest national soccer competition for their age. The amateur soccer players played at a regular soccer club in the same geographical region as the highly talented soccer players and play in a regular regional soccer competition. Group differences were tested using analyses of variance. The highly talented group showed superior motor inhibition as measured by stop signal reaction time (SSRT) on the Stop Signal task and a larger alerting effect on the Attention Network Test, indicating an enhanced ability to attain and maintain an alert state. No group differences were found for orienting and executive attention and visuospatial working memory. A logistic regression model with group (highly talented or amateur) as dependent variable and executive function measures that significantly distinguished between groups as predictors showed that these measures differentiated highly talented soccer players from amateur soccer players with 89% accuracy. Highly talented youth soccer players outperform youth amateur players on suppressing ongoing motor responses and on the ability to attain and maintain an alert state; both may be essential for success in soccer.


title: PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories

abstract: This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems. Specifically, we tackle the task of categorization of visual input from different domains by learning projections from each domain to a latent (shared) space jointly with the classifier in the latent space, which simultaneously minimizes the domain disparity while maximizing the classifier's discriminative power. Furthermore, the non-parametric nature of our adaptation model makes it possible to infer the latent space dimension automatically from data. We also develop a novel regularized Variational Bayes (VB) algorithm for efficient estimation of the model parameters. We compare the proposed model with the state-of-the-art methods for the tasks of visual domain adaptation using both handcrafted and deep-net features. Our experiments show that even with a simple softmax classifier, our model outperforms several state-of-the-art methods that take advantage of more sophisticated classification schemes.


title: Secure Wallet-Assisted Offline Bitcoin Payments with Double-Spender Revocation

abstract: Bitcoin seems to be the most successful cryptocurrency so far given the growing real life deployment and popularity. While Bitcoin requires clients to be online to perform transactions and a certain amount of time to verify them, there are many real life scenarios that demand for offline and immediate payments (e.g., mobile ticketing, vending machines, etc). However, offline payments in Bitcoin raise non-trivial security challenges, as the payee has no means to verify the received coins without having access to the Bitcoin network. Moreover, even online immediate payments are shown to be vulnerable to double-spending attacks. In this paper, we propose the first solution for Bitcoin payments, which enables secure payments with Bitcoin in offline settings and in scenarios where payments need to be immediately accepted. Our approach relies on an offline wallet and deploys several novel security mechanisms to prevent double-spending and to verify the coin validity in offline setting. These mechanisms achieve probabilistic security to guarantee that the attack probability is lower than the desired threshold. We provide a security and risk analysis as well as model security parameters for various adversaries. We further eliminate remaining risks by detection of misbehaving wallets and their revocation. We implemented our solution for mobile Android clients and instantiated an offline wallet using a microSD security card. Our implementation demonstrates that smooth integration over a very prevalent platform (Android) is possible, and that offline and online payments can practically co-exist. We also discuss alternative deployment approach for the offline wallet which does not leverage secure hardware, but instead relies on a deposit system managed by the Bitcoin network.


title: Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

abstract: How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks.


title: Are Social Bots on Twitter Political Actors? Empirical Evidence from a Ukrainian Social Botnet

abstract: A considerable amount of data in social networks like Twitter is not generated by humans but by automatic programs (bots). Some of these bots are mimicking humans (socialbots) and can hardly be identified. In this article, we analyze a social botnet involved in the Ukrainian/Russian conflict. Based on text mining and unsupervised learning, we can identify three different behaviors: mimicry, window dressing, and reverberation.


title: Inferring social ties between users with human location history

abstract: The location-based social networks have been becoming flourishing in recent years. In this paper, we aim to estimate the similarity between users according to their physical location histories (represented by GPS trajectories). This similarity can be regarded as a potential social tie between users, thereby enabling friend and location recommendations. Different from previous work using social structures or directly matching users’ physical locations, this approach model a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. When matching SLHs, we consider the sequential property, the granularity and the popularity of semantic locations. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. The results show that SLH outperforms a physical-location-based approach and MTM is more effective than several widely used sequence matching approaches given this application scenario.


Precision medicine in the diagnosis and treatment of male lower urinary tract symptoms suggestive of benign prostatic hyperplasia


title: Evaluating FrameNet-style semantic parsing: the role of coverage gaps in FrameNet

abstract: Supervised semantic role labeling (SRL) systems are generally claimed to have accuracies in the range of 80% and higher (Erk and Pado, 2006). These numbers, though, are the result of highly-restricted evaluations, i.e., typically evaluating on hand-picked lemmas for which training data is available. In this paper we consider performance of such systems when we evaluate at the document level rather than on the lemma level. While it is well-known that coverage gaps exist in the resources available for training supervised SRL systems, what we have been lacking until now is an understanding of the precise nature of this coverage problem and its impact on the performance of SRL systems. We present a typology of five different types of coverage gaps in FrameNet. We then analyze the impact of the coverage gaps on performance of a supervised semantic role labeling system on full texts, showing an average oracle upper bound of 46.8%.


title: A Broadband Flexible Implantable Loop Antenna With Complementary Split Ring Resonators

abstract: In this letter, a novel flexible implantable loop antenna with complementary split ring resonators (CSRRs) is proposed and designed for biomedical telemetric applications. The antenna has a very wide bandwidth to cover the Medical Device Radiocommunications service (MedRadio 401-406 MHz) and Industrial, Scientific and Medical (ISM 433.1-434.8, 868.0-868.8, 902.8-928.0 MHz and 2.45 GHz) bands which support the functions of wireless data transmission, wireless power transfer and saving (wakeup receiver). It is optimized over a two-step design process for cylindrical implants for a robust communication over 2 m. The CSRRs are introduced to reduce the absorbed power inside the body and improve the antenna impedance matching. This results in a larger radiation efficiency, gain and transmission coefficient (over 2 dB in comparison with the structure without the CSRRs). The specific absorption rate (SAR) is also decreased significantly. Measurements in a pork phantom are conducted and a good agreement with the simulation results is demonstrated. This novel antenna could be an excellent candidate for a range of implantable applications.


title: The influence of alexithymia on mobile phone addiction: the role of depression, anxiety and stress

abstract: Abstract Background Alexithymia is an important predictor of mobile phone addiction. Enhancing and improving college students’ mental health can reduce the rate of mobile phone addiction. However, it is not clear about the role of depression, anxiety and stress in the relationship between college students’ alexithymia and mobile phone addiction. Methods A total of 1105 college students were tested with the Toronto Alexithymia Scale, the Depression Anxiety Stress Scale and the Mobile Phone Addiction Index. Results An individual’s level of alexithymia was significantly correlated with depression, anxiety, stress and mobile phone addiction. Alexithymia had a significantly positive prediction effect on mobile phone addiction, and depression, anxiety, and stress on mobile phone are positive predictors. Depression, anxiety or stress had partially mediating effects between alexithymia and mobile phone addiction. Alexithymia not only directly had a positively impact on mobile phone addiction, but both also had an indirect effect on mobile phone addiction through depression, anxiety or stress. Limitations Limitations included sampling method and modest sample size, self-report measures, and unmeasured potential confounders. Conclusion Alexithymia is an important correlate of mobile phone addiction, and depression, anxiety or stress is an important mediator in this relationship.


title: Optical character recognition for self-service banking

abstract: Self-service automatic teller machines (ATMs) have dramatically altered the ways in which customers interact with banks. ATMs provide the convenience of completing some banking transactions remotely and at any time. AT&T Global Information Solutions (GIS) is the world's leading provider of ATMs. These machines support such familiar services as cash withdrawals and balance inquiries. Further technological development has extended the utility and convenience of ATMs produced by GIS by facilitating check cashing and depositing, as well as direct bill payment, using an on-line system. These enhanced services, discussed in this paper, are made possible primarily through sophisticated optical character recognition (OCR) technology. Developed by an AT&T team that included GIS, AT&T Bell Laboratories Quality, Engineering, Software, and Technologies (QUEST), and AT&T Bell Laboratories Research, OCR technology was crucial to the development of these advanced ATMs.


title: Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools

abstract: One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resource-intensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game’s equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.


title: Anticipatory and Consummatory Pleasure and Displeasure in Major Depressive Disorder: An Experience Sampling Study

abstract: Pleasure and displeasure can be parsed into anticipatory and consummatory phases. However, research on pleasure and displeasure in major depressive disorder (MDD), a disorder characterized by anhedonia, has largely focused on deficits in the consummatory phase. Moreover, most studies in this area have been laboratory-based, raising the question of how component processes of pleasure and displeasure are experienced in the daily lives of depressed individuals. Using experience sampling, we compared anticipatory and consummatory pleasure and displeasure for daily activities reported by adults with MDD (n = 41) and healthy controls (n = 39). Participants carried electronic devices for one week and were randomly prompted eight times a day to answer questions about activities to which they most and least looked forward. Compared to healthy controls, MDD participants reported blunted levels of both anticipatory and consummatory pleasure and elevated levels of both anticipatory and consummatory displeasure for daily activities. Independent of MDD status, participants accurately predicted pleasure but overestimated displeasure. These results are the first to provide evidence that, across both anticipatory and consummatory phases, individuals with MDD experience blunted pleasure and elevated displeasure for daily activities. Our findings clarify the disturbances in pleasure and displeasure that characterize MDD and may inform treatment for this debilitating disorder.


title: Automated Big Text Security Classification

abstract: In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other governments and with its own citizens. Data Leak Prevention (DLP) is a solution for detecting and preventing information leaks from within an organization's network. However, state-of-art DLP detection models are only able to detect very limited types of sensitive information, and research in the field has been hindered due to the lack of available sensitive texts. Many researchers have focused on document-based detection with artificially labeled "confidential documents" for which security labels are assigned to the entire document, when in reality only a portion of the document is sensitive. This type of whole-document based security labeling increases the chances of preventing authorized users from accessing non-sensitive information within sensitive documents. In this paper, we introduce Automated Classification Enabled by Security Similarity (ACESS), a new and innovative detection model that penetrates the complexity of big text security classification/detection. To analyze the ACESS system, we constructed a novel dataset, containing formerly classified paragraphs from diplomatic cables made public by the WikiLeaks organization. To our knowledge this paper is the first to analyze a dataset that contains actual formerly sensitive information annotated at paragraph granularity.


title: Segmentation of subcellular compartments combining superpixel representation with Voronoi diagrams

abstract: We propose and implement a computer vision algorithm for detecting individual cells, including the ability to distinguish subcellular compartments, such as nucleus and cytoplasm. Our approach consists of three main steps: (a) cellular mass estimation, and (b) nuclei detection through superpixel representation and, (c) cytoplasm detection through nuclear narrow-band seeding, graph-based region growing and Voronoi diagrams. We test our implementation on both real and simulated cervical cell images, containing an assortment of cells and configurations that often present occlusion and/or poor contrast. Our results show both qualitative and quantitative assessment of the datasets, using a completely automated computer program. The quantitative performance presents average Dice Coefficient equals to 0.85.


title: Evolutionary many-objective optimization: A quick-start guide

abstract: Abstract Multi-objective optimization problems having more than three objectives are referred to as many-objective optimization problems. Many-objective optimization brings with it a number of challenges that must be addressed, which highlights the need for new and better algorithms that can efficiently handle the growing number of objectives. This article reviews the different challenges associated with many-objective optimization and the work that has been done in the recent-past to alleviate these difficulties. It also highlights how the existing methods and body of knowledge have been used to address the different real world many-objective problems. Finally, it brings focus to some future research opportunities that exist with many-objective optimization. We report in this article what is commonly used, be it algorithms or test problems, so that the reader knows what are the benchmarks and also what other options are available. We deem this to be especially useful for new researchers and for researchers from other fields who wish to do some work in the area of many-objective optimization.


title: Side-channel attacks and machine learning approach

abstract: Most modern devices and cryptoalgorithms are vulnerable to a new class of attack called side-channel attack. It analyses physical parameters of the system in order to get secret key. Most spread techniques are simple and differential power attacks with combination of statistical tools. Few studies cover using machine learning methods for pre-processing and key classification tasks. In this paper, we investigate applicability of machine learning methods and their characteristic. Following theoretical results, we examine power traces of AES encryption with Support Vector Machines algorithm and decision trees and provide roadmap for further research.


title: Coercion and type inference

abstract: A simple semantic model of automatic coercion is proposed. This model is used to explain four rules for inferring polymorphic types and providing automatic coercions between types. With the addition of a fifth rule, the rules become semantically complete but the set of types associated with an expression may be undecidable. An efficient type checking algorithm based on the first four rules is presented. The algorithm is guaranteed to find a type whenever a type can be deduced using the four inference rules. The type checking algorithm may be modified so that calls to type conversion functions are inserted at compile time.


title: Performance pay and top management incentives

abstract: Our estimates of the pay-performance relation (including pay, options, stockholdings, and dismissal) for chief executive officers indicate that CEO wealth changes $3.25 for every $1,000 change in shareholder wealth. Although the incentives generated by stock ownership are large relative to pay and dismissal incentives, most CEOs hold trivial fractions of their firms' stock, and ownership levels have declined over the past 50 years. We hypothesize that public and private political forces impose constraints that reduce the payperformance sensitivity. Declines in both the pay-performance relation and the level of CEO pay since the 1930s are consistent with this hypothesis.


title: Public Displays of Connection

abstract: Participants in social network sites create self-descriptive profiles that include their links to other members, creating a visible network of connections — the ostensible purpose of these sites is to use this network to make friends, dates, and business connections. In this paper we explore the social implications of the public display of one's social network. Why do people display their social connections in everyday life, and why do they do so in these networking sites? What do people learn about another's identity through the signal of network display? How does this display facilitate connections, and how does it change the costs and benefits of making and brokering such connections compared to traditional means? The paper includes several design recommendations for future networking sites.


title: Book Reviews Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

abstract: From the Publisher: ::: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.


title: Faster Kernel Ridge Regression Using Sketching and Preconditioning

abstract: Kernel Ridge Regression (KRR) is a simple yet powerful technique for non-parametric regression whose computation amounts to solving a linear system. This system is usually dense and highly ill-conditioned. In addition, the dimensions of the matrix are the same as the number of data points, so direct methods are unrealistic for large-scale datasets. In this paper, we propose a preconditioning technique for accelerating the solution of the aforementioned linear system. The preconditioner is based on random feature maps, such as random Fourier features, which have recently emerged as a powerful technique for speeding up and scaling the training of kernel-based methods, such as kernel ridge regression, by resorting to approximations. However, random feature maps only provide crude approximations to the kernel function, so delivering state-of-the-art results by directly solving the approximated system requires the number of random features to be very large. We show that random feature maps can be much more effective in forming preconditioners, since under certain conditions a not-too-large number of random features is sufficient to yield an effective preconditioner. We empirically evaluate our method and show it is highly effective for datasets of up to one million training examples.


title: Understanding Biological Regulation Through Synthetic Biology

abstract: Engineering synthetic gene regulatory circuits proceeds through iterative cycles of design, building, and testing. Initial circuit designs must rely on often-incomplete models of regulation established by fields of reductive inquiry—biochemistry and molecular and systems biology. As differences in designed and experimentally observed circuit behavior are inevitably encountered, investigated, and resolved, each turn of the engineering cycle can force a resynthesis in understanding of natural network function. Here, we outline research that uses the process of gene circuit engineering to advance biological discovery. Synthetic gene circuit engineering research has not only refined our understanding of cellular regulation but furnished biologists with a toolkit that can be directed at natural systems to exact precision manipulation of network structure. As we discuss, using circuit engineering to predictively reorganize, rewire, and reconstruct cellular regulation serves as the ultimate means of testing and ...


title: Learning the Distribution Preserving Semantic Subspace for Clustering

abstract: This paper proposes a new clustering method for images called distribution preserving indexing (DPI). It aims to find a lower dimensional semantic space approximating the original image space in the sense of preserving the distribution of the data. In the theory, the intrinsic structure of the data clusters can be described by the distribution of the data effectively. Therefore, the cluster structure of the data in a lower dimensional semantic space derived by the DPI becomes clear. Unlike these distance-based clustering methods, which reveal the intrinsic Euclidean structure of data, our method attempts to discover the intrinsic cluster structure of the data space that actually is the union of some sub-manifolds. Moreover, we propose a revised kernel density estimator for the case of high-dimensional data, which is a crucial step in DPI. In addition, we provide a theoretical analysis of the bound of our method. Finally, the extensive experiments compared with other algorithms, on COIL20, CBCL, and MNIST demonstrate the effectiveness of our proposed approach.


title: Critical Success Factors in IT Service Management Implementation: People, Process, and Technology Perspectives

abstract: IT Service Management (ITSM) projects often involve huge investment but the failure rate of them continues to be high. With an increasing number of ITSM implementation in developing countries such as China, it is necessary for them to understand the critical success factors that exist in ITSM project. ITSM is a combination of people, process, and technology that seeks to provide high-quality IT services. Many organizations have difficulty in implementing ITSM successfully, mostly because they have not attached enough importance to people, process, and technology. This research uses Delphi method to investigate the critical success factors in ITSM implementation based on these three perspectives and generates a list of 48 critical success factors. Then the 12 most critical factors are discussed. This paper not only validates the results of previous studies, but also provides a more systematic and comprehensive analysis about the factors that influences successful ITSM implementation.


title: High-gain active composite right/left-handed leaky-wave antenna

abstract: A novel high-gain active composite right/left-handed (CRLH) metamaterial leaky-wave antenna (LWA) is presented. This antenna, which is designed to operate at broadside, is constituted by passive CRLH leaky-wave sections interconnected by amplifiers, which regenerate the power progressively leaked out of the structure in the radiation process in order to increase the effective aperture of the antenna and thereby its gain. The gain is further enhanced by a matching regeneration effect induced by the quasi-unilateral nature of the amplifiers. Both the cases of quasi-uniform and binomial field distributions, corresponding to maximum directivity and minimum side-lobe level, respectively, have been described. An active LWA prototype is demonstrated in transmission mode with a gain enhancement of 8.9 dB compared to its passive counterpart. The proposed antenna can attain an arbitrarily high gain by simple increase of the length of the structure, without penalty in terms of return loss and without requiring a complicated feeding network like conventional array antennas


title: Off-Policy Shaping Ensembles in Reinforcement Learning

abstract: In this work we propose learning an ensemble of policies related through potential-based shaping rewards via the off-policy Horde framework.


title: DeepSeek: Content Based Image Search&Retrieval

abstract: Most of the internet today is composed of digital media that includes videos and images. With pixels becoming the currency in which most transactions happen on the internet, it is becoming increasingly important to have a way of browsing through this ocean of information with relative ease. YouTube has 400 hours of video uploaded every minute and many million images are browsed on Instagram, Facebook, etc. Inspired by recent advances in the field of deep learning and success that it has gained on various problems like image captioning and, machine translation , word2vec , skip thoughts, etc, we present DeepSeek a natural language processing based deep learning model that allows users to enter a description of the kind of images that they want to search, and in response the system retrieves all the images that semantically and contextually relate to the query. Two approaches are described in the following sections.


title: Health benefits of fruit and vegetables are from additive and synergistic combinations of phytochemicals1–4

abstract: Cardiovascular disease and cancer are ranked as the first and second leading causes of death in the United States and in most industrialized countries. Regular consumption of fruit and vegetables is associated with reduced risks of cancer, cardiovascular disease, stroke, Alzheimer disease, cataracts, and some of the functional declines associated with aging. Prevention is a more effective strategy than is treatment of chronic diseases. Functional foods that contain significant amounts of bioactive components may provide desirable health benefits beyond basic nutrition and play important roles in the prevention of chronic diseases. The key question is whether a purified phytochemical has the same health benefit as does the whole food or mixture of foods in which the phytochemical is present. Our group found, for example, that the vitamin C in apples with skin accounts for only 0.4% of the total antioxidant activity, suggesting that most of the antioxidant activity of fruit and vegetables may come from phenolics and flavonoids in apples. We propose that the additive and synergistic effects of phytochemicals in fruit and vegetables are responsible for their potent antioxidant and anticancer activities, and that the benefit of a diet rich in fruit and vegetables is attributed to the complex mixture of phytochemicals present in whole foods.


title: Transfer Learning for Heterogeneous One-Class Collaborative Filtering

abstract: Various memory- and model-based collaborative filtering algorithms have been designed for multiclass feedback (such as grade scores) in the past two decades. Recently, one-class feedback (such as positive feedback and implicit examination) has been recognized as a more pervasive and important source of information in many real recommendation systems. Previous work along these lines mainly focus on homogenous one-class positive feedback, such as likes on Facebook or transactions on Amazon, which might not capture a user's true preferences due to the sparsity of such data. To alleviate this sparsity problem, the authors study positive feedback and implicit examinations simultaneously, coined as heterogeneous one-class collaborative filtering (HOCCF). Specifically, they designed a novel transfer learning algorithm for HOCCF, called transfer via joint similarity learning (TJSL), that jointly learns a similarity between a candidate item and a preferred item, and a similarity between a candidate item and an identified likely-to-prefer examined item. Joint similarity learning has the merit of being able to connect two seemingly unrelated items along sparse positive feedback only. Empirical studies on three real-world datasets show that TJSL can recommend items more accurately than other state-of-the-art methods.


title: Sensitive Data Leakage Detection in Pre-Installed Applications of Custom Android Firmware

abstract: There are many custom Android firmware (custom ROMs) which are shared on the Internet. Several recent studies aim their efforts at analyzing pre-installed applications in these firmware. However, they analyzed separate pre-installed applications. In this study we propose a system, uitXROM, to detect sensitive data leakage in custom Android firmware by analyzing relationships of pre-installed applications. The experimental results show that the system can detect all sensitive data leakage in our custom Android firmware. Secondly, it detects several pre-installed applications which leak sensitive data from 290 custom ROMs downloaded from the Internet.


title: Learning to diagnose from scratch by exploiting dependencies among labels

abstract: The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies. Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset. This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks. We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training. Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.


title: The Effects of Staffing and Training on Firm Productivity and Profit Growth Before, During, and After the Great Recession

abstract: This study integrates research from strategy, economics, and applied psychology to examine how organizations may leverage their human resources to enhance firm performance and competitive advantage. Staffing and training are key human resource management practices used to achieve firm performance through acquiring and developing human capital resources. However, little research has examined whether and why staffing and training influence firm-level financial performance (profit) growth under different environmental (economic) conditions. Using 359 firms with over 12 years of longitudinal firm-level profit data, we suggest that selective staffing and internal training directly and interactively influence firm profit growth through their effects on firm labor productivity, implying that staffing and training contribute to the generation of slack resources that help buffer and then recover from the effects of the Great Recession. Further, internal training that creates specific human capital resources is more beneficial for prerecession profitability, but staffing is more beneficial for postrecession recovery, apparently because staffing creates generic human capital resources that enable firm flexibility and adaptation. Thus, the theory and findings presented in this article have implications for the way staffing and training may be used strategically to weather economic uncertainty (recession effects). They also have important practical implications by demonstrating that firms that more effectively staff and train will outperform competitors throughout all pre- and postrecessionary periods, even after controlling for prior profitability.


title: Shard ranking and cutoff estimation for topically partitioned collections

abstract: Large document collections can be partitioned into 'topical shards' to facilitate distributed search. In a low-resource search environment only a few of the shards can be searched in parallel. Such a search environment faces two intertwined challenges. First, determining which shards to consult for a given query: shard ranking. Second, how many shards to consult from the ranking: cutoff estimation. In this paper we present a family of three algorithms that address both of these problems. As a basis we employ a commonly used data structure, the central sample index (CSI), to represent the shard contents. Running a query against the CSI yields a flat document ranking that each of our algorithms transforms into a tree structure. A bottom up traversal of the tree is used to infer a ranking of shards and also to estimate a stopping point in this ranking that yields cost-effective selective distributed search. As compared to a state-of-the-art shard ranking approach the proposed algorithms provide substantially higher search efficiency while providing comparable search effectiveness.


title: An iterative algorithm for de novo optical map assembly

abstract: Optical mapping is a high-throughput sequencing technology which carries long-range genome information at no risk of PCR artifacts. On account of its long span, optical maps leave much fewer gaps when used for genome assembly. However, a high risk of errors poses an enormous challenge to optical map assembly. Here we propose an iterative algorithm for de novo optical map assembly. In any iteration, only significant pairwise alignments beyond strict thresholds are used to construct accurate contigs. These contigs act as input molecules for the next iteration of assembly. Strict thresholds ensures a good quality of the local assembly. The iterative method retains the connectivity between contigs in a progressive manner. In practice, our IOMA (iterative optical map assembler) outperforms two popular assemblers being used in the community on both simulated and real E. coli datasets.


title: Websites that satisfy users: a theoretical framework for Web user interface design and evaluation

abstract: With the fast development and increasing use of the World Wide Web as both an information seeking and an electronic commerce tool, Web usability studies are growing in importance. While Web designers have largely focused on the functional aspects of Web sites, there has been little systematic attention to (1) the motivational issues of Web user interface design or (2) a theoretically-driven approach to Web user satisfaction studies. The objective of this paper is to provide a conceptual framework and foundation for systematically investigating features in the Web environment that contribute to user satisfaction with a Web interface. This research uses F. Herzberg's (1966) motivation-hygiene theory to guide the identification of these features. Among the implications and contributions of this research are the identification of Web design features that may maximize the likelihood of user satisfaction and return visits to the Web site.


title: Stitching Planning in Laparoscopic Surgery: Towards Robot-assisted Suturing

abstract: The work presented in this paper addresses the problem of the stitching task in laparoscopic surgery using a circular needle and a conventional four-degree-of-freedom needle holder. This task is particularly difficult for surgeons because of the kinematic constraints generated by the fulcrum in the abdominal wall of the patient. In order to assist the surgeons during suturing, we propose to compute possible paths for the needle through the tissues, which limit tissues deformation as much as possible while driving the needle towards the desired target. We propose a kinematic analysis and geometric modeling of the stitching task from which useful information can be obtained to assist the surgeon. The description of the task with appropriate state variables allows us to propose a simple practical method for computing optimal paths. Simulations show that the obtained paths are satisfactory even under difficult configurations and short computation times make the technique useful for intra-operative planning. The use of the stitching planning is shown for manual as well as for robot-assisted interventions in in vitro experiments.


An Evaluation of Synthetic Speech Using the PESQ Measure


title: Telepresence robot system for English tutoring

abstract: This paper introduced a telepresence robot system for English tutoring. The system consists of the user system, the server system and the robot system. The robot is controlled by the collision prevention system, direct interface, server interface with assistant teacher and remote interface with native teacher. A remote native teacher had given lessons in English using this system for a month and several issues of telepresence robot system for remote lecture are found from the lecture.


title: Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

abstract: This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1–2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.


title: Filler Placement and the Fat Compartments

abstract: Understanding the anatomy and distribution of facial fat and the alterations that occur during the aging process is essential to effectively and precisely achieve facial rejuvenation. Over the past several years, through cadaveric dissections and computed tomographic studies, much has been discovered concerning the adipose tissue of the face and how it influences the dynamic process of aging. Site-specific augmentation with fillers can now be used to refine facial shape and topography in a more predictable and precise fashion. The purpose of this article is to provide an introduction to facial fat compartmentalization along with clinical examples to illustrate how the knowledge of underlying


title: How much does industry matter, really

abstract: In this paper, we examine the importance of year, industry, corporate‐parent, and business‐specific effects on the profitability of U.S. public corporations within specific 4‐digit SIC categories. Our results indicate that year, industry, corporate‐parent, and business‐specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate‐parent and industry effects. A detailed analysis suggests that industry, corporate‐parent, and business‐specific effects are related in complex ways. © 1997 by John Wiley & Sons, Ltd.


title: Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing

abstract: Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches – a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.


title: Epistemic Petrification and the Restoration of Epistemic Trust: A New Conceptualization of Borderline Personality Disorder and Its Psychosocial Treatment

abstract: A new developmental model of borderline personality disorder (BPD) and its treatment is advanced based on evolutionary considerations concerning the role of attachment, mentalizing, and epistemic trust in the development of psychopathology. We propose that vulnerability to psychopathology in general is related to impairments in epistemic trust, leading to disruptions in the process of salutogenesis, the positive effects associated with the capacity to benefit from the social environment. BPD is perhaps the disorder par excellence that illustrates this view. We argue that this conceptualization makes sense of the presence of both marked rigidity and instability in BPD, and has far-reaching implications for intervention.


title: Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

abstract: The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.


title: Integration of a Highly Scalable, Multi-FPGA-Based Hardware Accelerator in Common Cluster Infrastructures

abstract: Heterogeneous systems consisting of general-purpose processors and different types of hardware accelerators are becoming more and more common in HPC systems. Especially Field Programmable Gate Arrays (FPGAs) provide an energy-efficient way to achieve high performance. Numerous application areas, including bio- and neuroinformatics, require enormous processing capability and employ simple computation cores, elementary data structures and algorithms highly suitable for FPGAs. To allow an efficient work with distributed FPGAs, it is necessary to provide a simple and scalable integration of these FPGAs in a common cluster architecture and to permit an easy access to these resources. Our approach enables a system-wide dynamic partitioning, a batch-based administration and the monitoring of FPGA resources. The system can easily be reconfigured to user-specific requirements and provides a high degree of flexibility and performance.


title: Expert finding in community question answering: a review

abstract: The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.


title: Exact Soft Confidence-Weighted Learning

abstract: In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive accuracy, but enjoys significant advantage of computational efficiency (i.e., smaller number of updates and lower time cost).


Incompatible: The GDPR in the Age of Big Data


title: EvoRiver: Visual Analysis of Topic Coopetition on Social Media

abstract: Cooperation and competition (jointly called “coopetition”) are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., “topic leaders”) affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).


title: Biologically Inspired Snake-like Robots

abstract: We have developed the snake-like robot since 1972. The body of snake has "the function of an arm" when it holds something by coiling itself and also has "the function of legs" when it moves by creeping. The body of ACM has several functions, which are fulfilled one after another according to the situation. Especially in this paper, it introduces about the various move method realized about the move function using 3-dimensional type ACM, and its feature


title: Creation of spiking neuron models applied in pattern recognition problems

abstract: Some spiking neuron models have proved to solve different linear and non-linear pattern recognition problems. Indeed, only one spiking neuron can generate comparable results as classical artificial neural network. However, depending on the classification problem, one spiking model could be better or less efficient than other. In this paper we propose a methodology to create spiking neuron models using Gene Expression Programming. The new models created are applied in eight pattern recognition problems. The results obtained are compared with previous results generated adopting the Izhikevich spiking neuron model. This first effort will help us to generate spiking neuron models which will be adaptable to a specific pattern recognition problem.


Constraint-Based Question Answering with Knowledge Graph


title: Dynamic Social Network Analysis of a Dark Network: Identifying Significant Facilitators

abstract: "Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.


title: Client attachment, attachment to the therapist and client-therapist attachment match: How do they relate to change in psychodynamic psychotherapy?

abstract: AbstractObjective: We examined the associations between client attachment, client attachment to the therapist, and symptom change, as well as the effects of client-therapist attachment match on outcome. Clients (n = 67) and their therapists (n = 27) completed the ECR to assess attachment. Method: Clients completed also the Client Attachment to Therapist scale three times (early, middle, and late sessions) and the OQ-45 at intake and four times over the course of a year of psychodynamic psychotherapy. Results: Clients characterized by avoidant attachment and by avoidant attachment to their therapist showed the least improvement. A low-avoidant client-therapist attachment match led to a greater decrease in symptom distress than when a low-avoidant therapist treated a high-avoidant client. Conclusions: These findings suggest the importance of considering client-therapist attachment matching and the need to pay attention to the special challenges involved in treating avoidant clients in order to facilitate pr...


title: Using Shared Stories and Individual Response Modes to Promote Comprehension and Engagement in Literacy for Students with Multiple, Severe Disabilities

abstract: This study investigated the effects of scripted task analytic lessons with systematic prompting on engagement and comprehension of students with a multiple, severe disability using a multiple probe single case design. Three teachers followed the scripts to include a target student in a story based lesson to increase comprehension and engagement. All three students had both a severe intellectual disability and either a severe physical or sensory impairment and relied primarily on nonsymblic communication prior to the study. Each student used a different response mode to participate in the story based lesson (i.e., eye gaze response for a student with inconsistent hand use, point response for a student who grabbed, and object response for a student with visual impairments). Results indicated increases in both comprehension and engagement for all three students. Limitations and implications for research and practice are discussed.


title: Depth Recovery from Light Field Using Focal Stack Symmetry

abstract: We describe a technique to recover depth from a light field (LF) using two proposed features of the LF focal stack. One feature is the property that non-occluding pixels exhibit symmetry along the focal depth dimension centered at the in-focus slice. The other is a data consistency measure based on analysis-by-synthesis, i.e., the difference between the synthesized focal stack given the hypothesized depth map and that from the LF. These terms are used in an iterative optimization framework to extract scene depth. Experimental results on real Lytro and Raytrix data demonstrate that our technique outperforms state-of-the-art solutions and is significantly more robust to noise and under-sampling.


title: Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan

abstract: The International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) is a scientific organization that encourages sound clinical practice, teaching and research for diagnostic imaging in women’s healthcare. The ISUOG Clinical Standards Committee (CSC) has a remit to develop Practice Guidelines and Consensus Statements as educational recommendations that provide healthcare practitioners with a consensus-based approach for diagnostic imaging. They are intended to reflect what is considered by ISUOG to be the best practices at the time at which they were issued. Although ISUOG has made every effort to ensure that guidelines are accurate when issued, neither the Society nor any of its employees or members accepts any liability for the consequences of any inaccurate or misleading data, opinions or statements issued by the CSC. They are not intended to establish a legal standard of care because interpretation of the evidence that underpins the guidelines may be influenced by individual circumstances and available resources. Approved guidelines can be distributed freely with the permission of ISUOG (info@isuog.org).


title: Performance Comparison Study of Routing Protocols for Mobile Grid Environment

abstract: Integration of mobile wireless consumer devices into the Grid initially seems unlikely due to limitation such as CPU ::: performance, small secondary storage, heightened battery ::: consumption sensitivity and unreliable low-bandwidth ::: communication. The current grid architecture and algorithm ::: also do not take into account the mobile computing ::: environment since mobile devices have not been seriously ::: considered as valid computing resources or interfaces in grid communities. This paper presents the results of simulation done in identifying suitable ad hoc routing protocol that can be used for the target mobile grid application. The simulation comparing three ad hoc routing protocols named DSDV, DSR and AODV.


title: Cognitive Internet of Things: A New Paradigm Beyond Connection

abstract: Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.


title: Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks

abstract: Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling. In this work, we aim to improve the performance of resource-constrained filter pruning by merging two sub-problems commonly considered, i.e., (i) how many filters to prune for each layer and (ii) which filters to prune given a per-layer pruning budget, into a global filter ranking problem. Our framework entails a novel algorithm, dubbed layer-compensated pruning, where meta-learning is involved to determine better solutions. We show empirically that the proposed algorithm is superior to prior art in both effectiveness and efficiency. Specifically, we reduce the accuracy gap between the pruned and original networks from 0.9% to 0.7% with 8x reduction in time needed for meta-learning, i.e., from 1 hour down to 7 minutes. To this end, we demonstrate the effectiveness of our algorithm using ResNet and MobileNetV2 networks under CIFAR-10, ImageNet, and Bird-200 datasets.


title: A Compact Size, Multi Octave Bandwidth Power Amplifier, Using LDMOS Transistors

abstract: A broadband push-pull power amplifier is presented that operates from 30MHz to 1000MHz. Coaxial cable transmission line baluns are used to split and combine the signal differentially. The balun acts also as a broadband 2:1 impedance transformer. The matching circuit consists of a piece of coaxial cable and some lumped elements. Using a packaged laterally diffused metal oxide semiconductor (LDMOS) transistor, a one dB compression output power of 39 dBm and a gain of 10 dB have been measured at 900 MHz. The input return loss is less than -10 dB across the entire band. The transistor is not internally matched and broadband matching techniques are used to match the transistor over multi octave bandwidth.


Reconfigurable Single-Armed Square Spiral Microstrip Antenna Design


title: Insular seizures causing sleep-related breathlessness

abstract: A 52-year-old woman was referred for assessment for sleep apnoea in October, 2010, with excessive daytime sleepiness, loud snoring, and regular waking from sleep with choking and breath lessness. She described regular events—occurring shortly after falling asleep—whereby she would suddenly wake up with a feeling of choking or being strangled for several seconds, accompanied by a sharp, central chest pain. These events were always associated with a feeling of dyspnoea and anxiety, lasting 1–2 min. Rarely she would feel a stiff ening sensation of her right leg as the event subsided. After prompting, she recalled that with more severe events, she may have bitten the lateral margins of her tongue. She remained fully aware of her surroundings throughout, and was able to return to sleep afterwards. These events would happen once, and at most twice, during the night, 5 or 6 nights a week, and would worsen in severity during the late luteal phase of her menstrual cycle. She remembered having very similar, less frequent, and less severe events from the age of 11 years, which had been treated as panic attacks with intermittent oral chlorpromazine. Her birth, childhood, and developmental history were unremarkable. She had a background of mild asthma. There was no family history of epilepsy. Physical examination was normal. Standard overnight poly somnography demonstrated fragmented sleep and an apnoea-hypopnoea index of 5·4/h. An MRI scan of the brain was normal. A further full-montage overnight sleep EEG, and separate sleep-deprived wake EEG, showed brief focal bursts of irregular theta activity over the left temporal area during frequent awakenings from stage N2 sleep. An FDG-PET scan of the brain showed an asymmetrical hypometabolic focus in the region of the left insula extending into the left temporal lobe (fi gure). A diagnosis of sleep-related focal epilepsy, localised to the left insular cortex, was made. Initiation of oral levetiracetam, escalated to a dose of 1500 mg nightly, resulted in suppression of almost all the patient’s seizures, with great improvement in sleep quality and daytime functioning. At last follow-up in September, 2013, she was experiencing only very occasional attacks of much reduced severity, which would occur only in the few days preceding her menses. The insular cortex lies deeply folded within the lateral sulcus of each hemisphere, covered by overlying areas of frontal, temporal, and parietal lobes. Penfi eld and Jasper were the fi rst to report the insular cortex’s prominent roles in viscerosensory, visceromotor, and autonomic control, as well as somatosensory, pain, auditory, and speech processing. With the location of the insula and its functional connectivity with the central autonomic net work, seizures arising from it can mimic temporal and frontal seizures, and may more rarely cause ictal arrhyth mias, including asystole. Scalp EEG may show interictal discharges of value in lateralising the side of the epi lep tic focus, but is otherwise unhelpful at diff erentiating seizures of insular origin from those originating from overlying areas of cortex. Isnard and coworkers directly recorded insular seizures using im planted cortical EEG in six patients, and described key characteristics, thought to be highly specifi c for these attacks. All patients maintained full consciousness and immediately noted laryngeal dis comfort as a choking or strangling sensation that was marked by anxiety and subjective dyspnoea. Perioral or truncal paraesthesiae were described, together with dys phonia and mutism in some patients, lasting up to a minute. The attacks typically ended with a stiff ening sen sation in a limb contralateral to the focus. Nocturnal choking and dyspnoea are frequently described symp toms of common disorders, such as obstructive sleep apnoea, heart failure, gastro-oesophageal refl ux, laryngospasm, and panic disorder. Careful ap praisal of additional symp toms suggestive of seizure activity should be done during the assessment of such patients.


title: Differential Effects of Stress and Glucocorticoids on Adult Neurogenesis

abstract: Stress is known to inhibit neuronal growth in the hippocampus. In addition to reducing the size and complexity of the dendritic tree, stress and elevated glucocorticoid levels are known to inhibit adult neurogenesis. Despite the negative effects of stress hormones on progenitor cell proliferation in the hippocampus, some experiences which produce robust increases in glucocorticoid levels actually promote neuronal growth. These experiences, including running, mating, enriched environment living, and intracranial self-stimulation, all share in common a strong hedonic component. Taken together, the findings suggest that rewarding experiences buffer progenitor cells in the dentate gyrus from the negative effects of elevated stress hormones. This chapter considers the evidence that stress and glucocorticoids inhibit neuronal growth along with the paradoxical findings of enhanced neuronal growth under rewarding conditions with a view toward understanding the underlying biological mechanisms.


title: Analysis of the residential, commercial and industrial electricity consumption

abstract: This work explains how to analyze the aggregate electricity consumption of many consumers, and extract key components such as heating, ventilation and air conditioning (HVAC), residential lighting, and street lighting consumption from the total consumption. To avoid explicit modeling of dependencies on time of day and on working versus non-working days, least-squares fitting for outside temperature and natural illumination dependency proceeds independently for each hour of the day. Cubic polynomials model dependencies on Steadman apparent temperature and on log-scale illumination, but spline surfaces are best when considering these variables jointly. The primary focus is on residential consumption, but the same techniques can be used for studying street lighting, commercial and industrial consumption.


title: Model-building semi-Markov adaptive critics

abstract: Adaptive or actor critics are a class of reinforcement learning (RL) or approximate dynamic programming (ADP) algorithms in which one searches over stochastic policies in order to determine the optimal deterministic policy. Classically, these algorithms have been studied for Markov decision processes (MDPs) in the context of model-free updates in which transition probabilities are avoided altogether. A model-free version for the semi-MDP (SMDP) for discounted reward in which the transition time of each transition can be a random variable was proposed in Gosavi [1]. In this paper, we propose a variant in which the transition probability model is built simultaneously with the value function and action-probability functions. While our new algorithm does not require the transition probabilities apriori, it generates them along with the estimation of the value function and the action-probability functions required in adaptive critics. Model-building and model-based versions of algorithms have numerous advantages in contrast to their model-free counterparts. In particular, they are more stable and may require less training. However the additional steps of building the model may require increased storage in the computer's memory. In addition to enumerating potential application areas for our algorithm, we will analyze the advantages and disadvantages of model building.


title: Accurate thigh inter-muscular adipose quantification using a data-driven and sparsity-constrained deformable model

abstract: The thigh inter-muscular adipose tissue (IMAT) quantification plays a critical role in various medical analysis tasks, e.g., the analysis of physical performance or the diagnose of knee osteoarthritis. In recent years, several techniques have been proposed to perform automated thigh tissues quantification. However, nobody has provided effective methods to track fascia lata, which is an important anatomic trail to distinguish between subcutaneous adipose tissue (SAT) and I-MAT in thigh. As a result, the estimation of IMAT may not be accurate for subjects with pathological conditions. On the other hand, tissue prior information, e.g., intensity, orientation and scale, becomes critical to infer and refine the fascia lata boundary from image appearance cues. In this paper, we propose a novel data-driven and sparsity-constrained de-formable model to obtain accurate fascia lata labeling. The model deformation is driven by the target points on fascia lata detected by a local discriminative classifier in a narrowband fashion. By using a sparsity-constrained optimization, the deformation is solved with errors and outliers suppression. The proposed approach has been evaluated on a set of 3D MR thigh volumes. In a comparison with another state-of-art framework, our approach produces superior performance.


title: What we know about dyslexia and Web accessibility: a research review

abstract: Compared to the online interaction behavior of other users, little is known about the difficulties dyslexic Web users encounter online. This paper reviews existing literature at the intersection of dyslexia and accessibility research to determine what useful knowledge exists regarding this important and relatively large group of users. This review uncovers that, although there are few published usability tests with dyslexic users, there is a considerable body of knowledge on dyslexia as well as many design guidelines for authoring dyslexic-accessible interfaces. Through a comparison of existing accessibility guidelines for dyslexic and non-dyslexic users and discussion of the plain language movement, it is argued that dyslexic-accessible practices may redress difficulties encountered by all Internet users. This conclusion suggests that usability testing yielding a clearer profile of the dyslexic user would further inform the practice of universal design, but also that enough knowledge is already available to allow doing more to increase accessibility for dyslexic Internet users.


title: Adult Attachment and the Link To Relationship Satisfaction: A Review of the Literature

abstract: Relationship dissatisfaction and divorce occur at alarming rates leading to many mental health consequences. This places a burden on mental health providers to understand and implement effective treatment for relationship problems. Attachment theory provides a conceptual framework for understanding relationship difficulties and intervening appropriately to treat them. This literature review describes the history and current research in the area of attachment, and its influence on relationship satisfaction. Suggestions for further application of the theory to improve current interventions and treatment for relationship problems are offered.


title: Summary of Usability Evaluations of an Educational Augmented Reality Application

abstract: We summarize three evaluations of an educational augmented reality application for geometry education, which have been conducted in 2000, 2003 and 2005 respectively. Repeated formative evaluations with more than 100 students guided the redesign of the application and its user interface throughout the years. We present and discuss the results regarding usability and simulator sickness providing guidelines on how to design augmented reality applications utilizing head-mounted displays.


title: Daytime sleepiness affects prefrontal regulation of food intake

abstract: Abstract The recent epidemic of obesity corresponds closely with the decline in the average number of hours of sleep obtained nightly. While growing research suggests that sleep loss may affect hormonal and other physiological systems related to food intake, no studies have yet explored the role that sleepiness may play in reducing prefrontal inhibitory control over food intake. Because evidence suggests that women may be more prone to obesity and eating disorders, as well as more likely to suffer from sleep problems, we examined the relation between general daytime sleepiness, brain responses to food stimuli, and self-reported overeating separately for men and women. Thirty-eight healthy adults (16 women; 22 men) aged 18 to 45 underwent functional magnetic resonance imaging (fMRI) while viewing pictures of high- and low-calorie foods. Subjects completed the Epworth Sleepiness Scale (ESS) and provided a rating to the query “how often do you eat more than you intend to.” Contrast images comparing brain activation derived from the high- versus low-calorie conditions were correlated voxel-wise with scores from the ESS in a second-level regression model, the output of which was used to predict self-reported overeating. As hypothesized, daytime sleepiness correlated with reduced activation in the ventromedial prefrontal cortex during perception of high- versus low-calorie food images. Moreover, activation within this cluster predicted overeating, but only for women. Findings suggest that normal fluctuations in sleepiness may be sufficient to affect brain regions important for regulating food intake, but that these effects may differ between men and women.


title: The science on front-of-package food labels

abstract: Objective The US Food and Drug Administration and Institute of Medicine are currently investigating front-of-package (FOP) food labelling systems to provide science-based guidance to the food industry. The present paper reviews the literature on FOP labelling and supermarket shelf-labelling systems published or under review by February 2011 to inform current investigations and identify areas of future research. Design A structured search was undertaken of research studies on consumer use, understanding of, preference for, perception of and behaviours relating to FOP/shelf labelling published between January 2004 and February 2011. Results Twenty-eight studies from a structured search met inclusion criteria. Reviewed studies examined consumer preferences, understanding and use of different labelling systems as well as label impact on purchasing patterns and industry product reformulation. Conclusions The findings indicate that the Multiple Traffic Light system has most consistently helped consumers identify healthier products; however, additional research on different labelling systems’ abilities to influence consumer behaviour is needed.


title: A New Framework for Direct Saliency Detection and Segmentation Based on Graph Methods

abstract: Saliency detection is an important research topic in computer vision. The traditional methods compute image saliency map, then salient segmentation is based on the corresponding saliency map. Unfortunately, overall performance of this method is poor due to the reason of losing some fine details and spatial information within image. This paper presents a new framework to overcome the drawback, named FDSRDS(Framework for Directly Salient Region Detection and Segmentation based on graph methods). Under FDSRSD, firstly, we get the foreground image by segmenting the original image via our extended grabcut algorithm. Mostly, the saliency region is within the foreground part. Secondly, we segment the foreground image into regions by means of graph based segmentation and nearest neighbor graph . Thirdly, we use relative weber's luminance rules to calculate every region’s luminance. Finally, we get the maximum luminance region which is the saliency region. Under FDSRSD framework, algorithms we proposed capture fine details and spatial relationships in saliency computation. We demonstrate impressive results by evaluating our method with other five state-of-the-art methods on the publicly available data set.


title: Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

abstract: Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.


title: Techniques for assessing polygonal approximations of curves

abstract: Given the enormous number of available methods for finding polygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Instead, we suggest assessing an algorithm's results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the application of 23 algorithms to a curve first used by Teh and Chin (1989); their integral square errors (ISEs) are assessed relative to the optimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation can be used to select an appropriate assessment criterion.


Voice-based Data Exploration : Chatting with your Database


title: Defeating return-oriented rootkits with "Return-Less" kernels

abstract: Targeting the operating system (OS) kernel, kernel rootkits pose a formidable threat to computer systems and their users. Recent efforts have made significant progress in blocking them from injecting malicious code into the OS kernel for execution. Unfortunately, they cannot block the emerging so-called return-oriented rootkits (RORs). Without the need of injecting their own malicious code, these rootkits can discover and chain together "return-oriented gadgets" (that consist of only legitimate kernel code) for rootkit computation. In this paper, we propose a compiler-based approach to defeat these return-oriented rootkits. Our approach recognizes the hallmark of return-oriented rootkits, i.e., the ret instruction, and accordingly aims to completely remove them in a running OS kernel. Specifically, one key technique named return indirection is to replace the return address in a stack frame into a return index and disallow a ROR from using their own return addresses to locate and assemble return-oriented gadgets. Further, to prevent legitimate instructions that happen to contain return opcodes from being misused,we also propose two other techniques, register allocation and peephole optimization, to avoid introducing them in the first place. We have developed a LLVM-based prototype and used it to generate a return-less FreeBSD kernel. Our evaluation results indicate that the proposed approach is generic, effective, and can be implemented on commodity hardware with a low performance overhead.


title: Indoor Terahertz Communications: How Many Antenna Arrays Are Needed?

abstract: Terahertz (THz) communications promise to be the next frontier for wireless networks. Novel solutions should be explored to overcome the hardware constraints and the severe path loss. In this paper, we study a low-complexity indoor THz communication system with antenna subarrays. The Saleh-Valenzuela (S-V) channel model is modified to characterize the THz channel. By exploiting the hybrid beamforming with multiple subarrays, we analyze the ergodic capacity of the system and obtain an upper bound. Furthermore, with the analysis of performance degradation for the uncertainty in THz phase shifters, we provide a guidance on the design of antenna subarray size and number for certain long-term data rate requirements with different distances. Simulation results validate the effectiveness of the ergodic capacity upper bound, and show that the proposed THz system and antenna array structure can efficiently achieve capacity gains and support THz communications.