title: Nurse perceptions of the use of seclusion in mental health inpatient facilities: have attitudes to Māori changed?

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to examine the results of New Zealand initiatives to reduce seclusion rates and report the attitudes of mental health nurses to seclusion, factors involved in seclusion use, and alternatives to seclusion. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: A questionnaire was circulated to mental health inpatient staff. Data from the Ministry of Health for seclusion numbers and rates for Ma¯ori and non-Ma¯ori were also collected. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The major barriers to reducing the use of seclusion related to staffing issues, a lack of management and medical support, and physical characteristics of the facility. Data from the Office of the Director of Mental Health annual reports from 2007-2014 clearly show a reduction in the total seclusion events, the number of patients secluded, and the percentage of total patients secluded. However, the percentage of Ma¯ori secluded compared to the total number of patients secluded showed little change from 2007 to 2013. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Further analysis of the nurse’s responses showed that four of the six least-used strategies incorporated Ma¯ori cultural approaches. The authors surmise that an inability to provide culturally sensitive care, either through staffing or education factors, may be implicated in the lack of change in the seclusion rates for Ma¯ori. This may also be pertinent to seclusion rates for indigenous peoples in other countries.


title: Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law

abstract: Massive content about user's social, personal and professional life stored on Online Social Networks (OSNs) has attracted not only the attention of researchers and social analysts but also the cyber criminals. These cyber criminals penetrate illegally into an OSN by establishing fake profiles or by designing bots and exploit the vulnerabilities of an OSN to carry out illegal activities. With the growth of technology cyber crimes have been increasing manifold. Daily reports of the security and privacy threats in the OSNs demand not only the intelligent automated detection systems that can identify and alleviate fake profiles in real time but also the reinforcement of the security and privacy laws to curtail the cyber crime. In this paper, we have studied various categories of fake profiles like compromised profiles, cloned profiles and online bots (spam-bots, social-bots, like-bots and influential-bots) on different OSN sites along with existing cyber laws to mitigate their threats. In order to design fake profile detection systems, we have highlighted different category of fake profile features which are capable to distinguish different kinds of fake entities from real ones. Another major challenges faced by researchers while building the fake profile detection systems is the unavailability of data specific to fake users. The paper addresses this challenge by providing extremely obliging data collection techniques along with some existing data sources. Furthermore, an attempt is made to present several machine learning techniques employed to design different fake profile detection systems.


title: Rehabilitation Program for Caregivers about Patients’ Post Autologous Bone Marrow Transplantation

abstract: Approximately 50 000 people undergo BMTs each year worldwide. With the move to more outpatient and home care, family caregivers are playing an ever-increasing role. However, there is little information regarding the needs and well-being of caregivers of individuals undergoing BMT. In Egypt, according to medical records of Sheikh Zayed Specialized Hospital, (2013). 390 cases of autologous transplant were carried out during the last 3 years in bone marrow transplantation unit. 75% of individuals receiving autologous transplants were between the ages of 21-50 years. The main source of support for patients is often their family as having access to this support which is important for patient outcomes. The community health nurse role includes assessing patients‟ needs to identify expectations of the patients and anticipate impact on family, providing psychosocial support and education during rehabilitation. Assessment and interventions are needed to support the sexual dysfunction and infertility as are major concern, monitoring the patient‟s condition with thorough physical assessments, assisting the patients and families to take responsibility for establishing, maintaining, and improving their health by adding to their knowledge and ability to influence health determinants. Specific information on how family can carry out these roles during rehabilitation encourages the adoption of health beliefs, attitudes, and behaviors that contribute to the overall health of the population.


title: Examination of the Mediational Influences of Peer Norms, Environmental Influences, and Parent Communications on Heavy Drinking in Athletes and Nonathletes

abstract: The present study used perspectives from the general literature on college alcohol consumption to examine mediational influences of peer, environmental, and parental variables on heavy drinking for student athlete and nonathlete samples. Eight hundred thirty-five freshmen who differed in organized sports involvement were compared on heavy drinking outcomes, peer norms, environmental influences, and parental communication. College athletes reported significantly more heavy drinking experiences than nonathletes. Peer norms, environmental influences, and parental communication were all significant mediators of the athlete–heavy drinking relationship. Athletes reported a higher perception of peer drinking, peer approval of drinking, higher alcohol availability, and direct drink offers, which, in turn, were related to higher rates of heavy drinking. Parental communication mediated the athlete–heavy drinking relationship differently, depending on the specific topic of conversation. Discussion surrounding the importance of incorporating a variety of interventions aimed at reducing collegiate athlete drinking on the basis of the peer, environmental, and parental influences observed in the present analyses are presented. Limitations and directions for future research are also noted.


Wide Range Enable Level Shifter for Multi-Supply Voltage Designs


title: Digital footprints: predicting personality from temporal patterns of technology use

abstract: Psychometric modeling using digital data traces is a growing field of research with a breadth of potential applications in marketing, personalization and psychological assessment. We present a novel form of digital traces for user modeling: temporal patterns of smartphone and personal computer activity. We show that some temporal activity metrics are highly correlated with certain Big Five personality metrics. We then present a machine learning method for binary classification of each Big Five personality trait using these temporal activity patterns of both computer and smartphones as model features. Our initial findings suggest that Extroversion, Openness, Agreeableness, and Neuroticism can be classified using temporal patterns of digital traces at a similar accuracy to previous research that classified personality traits using different types of digital traces.


title: Action recognition by learning pose representations

abstract: Pose detection is one of the fundamental steps for the recognition of human actions. In this paper we propose a novel trainable detector for recognizing human poses based on the analysis of the skeleton. The main idea is that a skeleton pose can be described by the spatial arrangements of its joints. Starting from this consideration, we propose a trainable pose detector, that can be configured on a prototype skeleton in an automatic configuration process. The result of the configuration is a model of the position of the joints in the concerned skeleton. In the application phase, the joint positions contained in the model are compared with the ones of their homologous joints in the skeleton under test. The similarity of two skeletons is computed as a combination of the position scores achieved by homologous joints. In this paper we describe an action classification method based on the use of the proposed trainable detectors to extract features from the skeletons. We performed experiments on the publicly available MSDRA data set and the achieved results confirm the effectiveness of the proposed approach.


title: An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

abstract: Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.


title: Mining and modeling character networks

abstract: We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of Beveridge and Shan [4], we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King’s The Stand, and J.K. Rowling’s Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we conjecture why this may be the case.


title: Neural Cross-Lingual Entity Linking

abstract: A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.


title: A patch-based approach for random-valued impulse noise removal

abstract: In this paper, we show that a patch-based approach can successfully be applied for impulse noise removal. This requires careful choices for both the distance between patches and for the statistical estimator of the original patch. This method proves to be particularly powerful, especially for the restoration of textured areas, and compares favorably to recent restoration methods.


title: I Stand With You: Using Emojis to Study Solidarity in Crisis Events

abstract: We study how emojis are used to express solidarity in social media in the context of two major crisis events - a natural disaster, Hurricane Irma in 2017 and terrorist attacks that occurred on November 2015 in Paris. Using annotated corpora, we first train a recurrent neural network model to classify expressions of solidarity in text. Next, we use these expressions of solidarity to characterize human behavior in online social networks, through the temporal and geospatial diffusion of emojis. Our analysis reveals that emojis are a powerful indicator of sociolinguistic behaviors (solidarity) that are exhibited on social media as the crisis events unfold.


Isolation and Characterization of Actinomycetes from the Soil of Devathanam – A Foothill of Western Ghats


title: Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM)

abstract: Survival analysis, also known as failure time analysis or time-to-event analysis, is one of the most significant advancements of mathematical statistics in the last quarter of the 20th century. It has become the de facto standard in biomedical data analysis. Although reliability was conceived as a major application field by the mathematicians who pioneered survival analysis, survival analysis failed to establish itself as a major tool for reliability analysis. In this paper, we attempt to demonstrate, by reviewing and comparing the major mathematical models of both fields, that survival analysis and reliability theory essentially address the same mathematical problems. Therefore, survival analysis should become a major mathematical tool for reliability analysis and related fields such as Prognostics and Health Management (PHM). This paper is the first in a four part series in which we review state-of-the-art studies in survival (univariate) analysis, competing risks analysis, and multivariate survival analysis, with focusing on their applications to reliability and computer science. The present article discusses the univariate survival analysis (survival analysis hereafter).


title: Joint view expansion and filtering for automultiscopic 3D displays

abstract: Multi-view autostereoscopic displays provide an immersive, glasses-free 3D viewing experience, but they require correctly filtered content from multiple viewpoints. This, however, cannot be easily obtained with current stereoscopic production pipelines. We provide a practical solution that takes a stereoscopic video as an input and converts it to multi-view and filtered video streams that can be used to drive multi-view autostereoscopic displays. The method combines a phase-based video magnification and an interperspective antialiasing into a single filtering process. The whole algorithm is simple and can be efficiently implemented on current GPUs to yield a near real-time performance. Furthermore, the ability to retarget disparity is naturally supported. Our method is robust and works well for challenging video scenes with defocus blur, motion blur, transparent materials, and specularities. We show that our results are superior when compared to the state-of-the-art depth-based rendering methods. Finally, we showcase the method in the context of a real-time 3D videoconferencing system that requires only two cameras.


title: Interactive exploratory search for multi page search results

abstract: Modern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in exploratory search using ad hoc queries are likely to access more than one page. Document rankings for such queries can be improved by allowing additional context to the query to be provided by the user herself using explicit ratings or implicit actions such as clickthroughs. Existing methods using this information usually involved detrimental UI changes that can lower user satisfaction. Instead, we propose a new feedback scheme that makes use of existing UIs and does not alter user's browsing behaviour; to maximise retrieval performance over multiple result pages, we propose a novel retrieval optimisation framework and show that the optimal ranking policy should choose a diverse, exploratory ranking to display on the first page. Then, a personalised re-ranking of the next pages can be generated based on the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session. TREC evaluations demonstrate that our optimal rank strategy (including approximative Monte Carlo Sampling) can naturally optimise the trade-off between exploration and exploitation and maximise the overall user's satisfaction over time against a number of similar baselines.


title: A Petri Net based Environment for the Design of Event-driven Interfaces

abstract: Modern window-based user interfaces are actually a special kind of reactive system, and Petri nets may be fruitfully used to design such user — computer dialogues. This paper describes a software engineering tool aimed at supporting the use of high-level Petri nets for the specification, design and implementation of user interfaces in an event-driven interface system. We assess the rationale for the use of Petri nets in such a perspective. We then detail the object-oriented software architecture of the environment, and present an original algorithm for interpreting high-level Petri nets in an event-driven environment.


title: Dual tree complex wavelet transform based denoising of optical microscopy images

abstract: Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fiber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.


title: Noise Adaptive Speech Enhancement using Domain Adversarial Training

abstract: In this study, we propose a novel noise adaptive speech enhancement (SE) system, which employs a domain adversarial training (DAT) approach to tackle the issue of a noise type mismatch between the training and testing conditions. Such a mismatch is a critical problem in deep-learning-based SE systems. A large mismatch may cause a serious performance degradation to the SE performance. Because we generally use a well-trained SE system to handle various unseen noise types, a noise type mismatch commonly occurs in real-world scenarios. The proposed noise adaptive SE system contains an encoder-decoder-based enhancement model and a domain discriminator model. During adaptation, the DAT approach encourages the encoder to produce noise-invariant features based on the information from the discriminator model and consequentially increases the robustness of the enhancement model to unseen noise types. Herein, we regard stationary noises as the source domain (with the ground truth of clean speech) and non-stationary noises as the target domain (without the ground truth). We evaluated the proposed system on TIMIT sentences. The experiment results show that the proposed noise adaptive SE system successfully provides significant improvements in PESQ (19.0%), SSNR (39.3%), and STOI (27.0%) over the SE system without an adaptation.


title: A Survey Of Cross-lingual Word Embedding Models

abstract: Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.


title: Management information systems and strategic performances: The role of top team composition

abstract: Organizations adopt sophisticated management information systems, which provide top managers with an ample range of information to achieve multiple strategic performances. However, organizations differ in the extent to which they improve their performance. This paper analyzes the role of top management team in the relationship between management information systems and strategic performance. Using data collected from 92 top management teams, it analyses how different team compositions interact with a sophisticated management information system, and how this interaction affects strategic performances, which are focused on cost reduction and flexibility. The findings show how the effect of management information system on strategic performance (focused on flexibility) is moderated by top management team diversity.


title: Discovering vessel activities at sea using AIS data: Mapping of fishing footprints

abstract: Maritime Situational Awareness (MSA), the capability of understanding events, circumstances and activities within and impacting the maritime environment, can be greatly improved by the automatic identification and classification of vessel activity. Enhancing coverage of existing technologies such as Automatic Identification System (AIS) provides the possibility to integrate and enrich services and information already available in the maritime domain. In this paper, we propose a method that automatically extracts knowledge from ship reporting positioning data. In particular, we apply this architecture to the practical scenario of automatically discovering fishing areas based on historical AIS data broadcast by fishing vessels.


title: A Context-Based Integrity Framework

abstract: Although there is significant research and development into information security areas such as confidentiality and availability, scope remains for attention to the third fundamental security property: integrity. The Biba and Clark-Wilson models are still the most recognised for managing integrity of data in systems. After identifying several desirable extensions to the original ideas in these models, we present a new integrity framework, which can allow for a more flexible, quantitative, and context sensitive management of data integrity on a system wide basis.


title: Model-based control of a 6-dof electrohydraulic Stewart – Gough platform

abstract: In this paper, a novel model-based controller for a six Degree-of-Freedom (dof) electrohydraulic Stewart–Gough platform is developed. Dynamic models of low complexity are employed that describe the salient dynamics of the main electrohydraulic components. Rigid body equations of motion and hydraulics dynamics, including friction and servovalve models are used. The developed feedback controller uses the system dynamic and hydraulic model to yield servovalve currents, so that the error dynamics converge asymptotically to zero, independent of load variations. In this approach, force, pressure or acceleration feedback is not required. Simulations with typical desired trajectory inputs are presented and a good performance of the controller is obtained. The proposed methodology can be extended to electrohydraulic serial or closed-chain manipulators and simulators. 2007 Elsevier Ltd. All rights reserved.


title: Exploring the vectorization of python constructs using pythran and boost SIMD

abstract: The Python language is highly dynamic, most notably due to late binding. As a consequence, programs using Python typically run an order of magnitude slower than their C counterpart. It is also a high level language whose semantic can be made more static without much change from a user point of view in the case of mathematical applications. In that case, the language provides several vectorization opportunities that are studied in this paper, and evaluated in the context of Pythran, an ahead-of-time compiler that turns Python module into C++ meta-programs.


title: Clinical Natural Language Processing with Deep Learning.

abstract: The emergence and proliferation of electronic health record (EHR) systems has incrementally resulted in large volumes of clinical free text documents available across healthcare networks. The huge amount of data inspires research and development focused on novel clinical natural language processing (NLP) solutions to optimize clinical care and improve patient outcomes. In recent years, deep learning techniques have demonstrated superior performance over traditional machine learning (ML) techniques for various general-domain NLP tasks, e.g., language modeling, parts-of-speech (POS) tagging, named entity recognition, paraphrase identification, sentiment analysis, etc. Clinical documents pose unique challenges compared to general-domain text due to the widespread use of acronyms and nonstandard clinical jargons by healthcare providers, inconsistent document structure and organization, and requirement for rigorous de-identification and anonymization to ensure patient data privacy. This tutorial chapter will present an overview of how deep learning techniques can be applied to solve NLP tasks in general, followed by a literature survey of existing deep learning algorithms applied to clinical NLP problems. Finally, we include a description of various deep learning-driven clinical NLP applications developed at the artificial intelligence (AI) lab in Philips Research in recent years—such as diagnostic inferencing from unstructured clinical narratives, relevant biomedical article retrieval based on clinical case scenarios, clinical paraphrase generation, adverse drug event (ADE) detection from social media, and medical image caption generation.


title: Target discovery

abstract: Target discovery, which involves the identification and early validation of disease-modifying targets, is an essential first step in the drug discovery pipeline. Indeed, the drive to determine protein function has been stimulated, both in industry and academia, by the completion of the human genome project. In this article, we critically examine the strategies and methodologies used for both the identification and validation of disease-relevant proteins. In particular, we will examine the likely impact of recent technological advances, including genomics, proteomics, small interfering RNA and mouse knockout models, and conclude by speculating on future trends.


title: Violence detection using Oriented VIolent Flows

abstract: Nowadays, with so many surveillance cameras having been installed, the market demand for intelligent violence detection is continuously growing, while it is still a challenging topic in research area. Therefore, we attempt to make some improvements of existing violence detectors. The primary contributions of this paper are two-fold. Firstly, a novel feature extraction method named Oriented VIolent Flows (OViF), which takes full advantage of the motion magnitude change information in statistical motion orientations, is proposed for practical violence detection in videos. The comparison of OViF and baseline approaches on two public databases demonstrates the efficiency of the proposed method. Secondly, feature combination and multi-classifier combination strategies are adopted and excellent results are obtained. Experimental results show that using combined features with AdaBoost+Linear-SVM achieves improved performance over the state-of-the-art on the Violent-Flows benchmark. Display Omitted A novel feature named OViF for violence detection.OViF makes full use of the orientation information of optical flows compared to ViF.We use AdaBoost as a feature selector and Linear SVM as a classifier.Experiments show that ViF+OViF obtain the state-of-the-art violence detection performance when using AdaBoost with SVM.


title: Effect of Active Music Therapy and Individualized Listening to Music on Dementia: A Multicenter Randomized Controlled Trial

abstract: Objectives ::: To assess the effects of active music therapy (MT) and individualized listening to music (LtM) on behavioral and psychological symptoms of dementia (BPSDs) in persons with dementia (PWDs). ::: ::: Design ::: Randomized controlled trial. ::: ::: Setting ::: Nine Italian institutions. ::: ::: Participants ::: Persons with moderate to severe dementia and BPSDs (N = 120) were randomized to one of three treatments. ::: ::: Interventions ::: All groups received standard care (SC), and two groups attended 20 individualized MT or LtM sessions, twice a week, in addition to SC. ::: ::: Measurements ::: The Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), and Cornell-Brown Scale for Quality of Life in Dementia (CBS-QoL) were administered before treatment, after treatment, and at follow-up to evaluate behavioral and psychological outcomes. A specific coding scheme (Music Therapy Check List—Dementia) was used to evaluate the MT process. ::: ::: Results ::: Behavioral assessment did not show significant differences between groups. All groups showed a reduction over time in NPI global score (P ≤ .001), CSDD (P = .001), and CBS-QoL (P = .01). The NPI global score fell 28% in the MT group, 12% in the LtM group, and 21% in the SC group at the end of treatment. An exploratory post hoc analysis showed similar within-group improvements for the NPI Delusion, Anxiety, and Disinhibition subscales. In the MT group, communication and relationships between the music therapists and PWDs showed a positive albeit nonsignificant trend during treatment. ::: ::: Conclusion ::: The addition of MT or LtM to standard care did not have a significant effect on BPSDs in PWDs. Further studies on the effects of the integration of standard care with different types of music interventions on BPSD in PWD are warranted.


title: Clinical parameters in male genital lichen sclerosus: a case series of 329 patients

abstract: Background The dermatological aspects of male genital lichen sclerosus (MGLSc) have not received much prominence in the literature. Sexual morbidity appears under-appreciated, the role of histology is unclear, the relative places of topical medical treatment and circumcision are not established, the prognosis for sexual function, urinary function and penis cancer is uncertain and the pathogenesis has not been specifically studied although autoimmunity (as in women) and HPV infection have been mooted. ::: ::: ::: ::: Objective To illuminate the above by analysing the clinical parameters of a large series of patients with MGLSc. ::: ::: ::: ::: Methods A total of 329 patients with a clinical diagnosis of MGLSc were identified retrospectively from a dermatology-centred multidisciplinary setting. Their clinical and histopathological features and outcomes have been abstracted from the records and analysed by simple descriptive statistics. ::: ::: ::: ::: Results The collation and analysis of clinical data derived from the largest series of men with MGLSc ever studied from a dermatological perspective has been achieved. These data allow the conclusions below to be drawn. ::: ::: ::: ::: Conclusions MGLSc is unequivocally a disease of the uncircumcised male; the adult peak is late in the fourth decade; dyspareunia is a common presenting complaint; non-specific histology requires careful interpretation; most men are either cured by topical treatment with ultrapotent steroid (50–60%) or by circumcision (>75%); effective and definitive management appears to abrogate the risk of developing penile squamous cell carcinoma; urinary contact is implicated in the pathogenesis of MGLSc; HPV infection and autoimmunity seem unimportant.


title: Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art

abstract: In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.


title: Free hand-drawn sketch segmentation

abstract: In this paper, we study the problem of how to segment a freehand sketch at the object level. By carefully considering the basic principles of human perceptual organization, a real-time solution is presented to automatically segment a user's sketch during his/her drawing. First, a graph-based sketch segmentation algorithm is proposed to segment a cluttered sketch into multiple parts based on the factor of proximity. Then, to improve the ability of detecting semantically meaningful objects, a semantic-based approach is introduced to simulate the past experience in the perceptual system by leveraging a web-scale clipart database. Finally, other important factors learnt from past experience, such as similarity, symmetry, direction, and closure, are also taken into account to make the approach more robust and practical. The proposed sketch segmentation framework has ability to handle complex sketches with overlapped objects. Extensive experimental results show the effectiveness of the proposed framework and algorithms.


title: AdaBoost-Based Algorithm for Network Intrusion Detection

abstract: Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.


Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes


Modeling Churn and Usage Behavior in Contractual Settings


The Analysis of the Mozart Effect on Visual Search


title: Development of a Flexible 3-D Tactile Sensor System for Anthropomorphic Artificial Hand

abstract: In this paper, we report a novel flexible tactile sensor array for an anthropomorphic artificial hand with the capability of measuring both normal and shear force distributions using quantum tunneling composite as a base material. There are four fan-shaped electrodes in a cell that decompose the contact force into normal and shear components. The sensor has been realized in a 2ntn 6 array of unit sensors, and each unit sensor responds to normal and shear stresses in all three axes. By applying separated drops of conductive polymer instead of a full layer, cross-talk between the sensor cells is decreased. Furthermore, the voltage mirror method is used in this circuit to avoid crosstalk effect, which is based on a programmable system-on-chip. The measurement of a single sensor shows that the full-scale range of detectable forces are about 20, 8, and 8 N for the x-, y-, and z-directions, respectively. The sensitivities of a cell measured with a current setup are 0.47, 0.45, and 0.16 mV/mN for the x-, y-, and y-directions, respectively. The sensor showed a high repeatability, low hysteresis, and minimum tactile crosstalk. The proposed flexible three-axial tactile sensor array can be applied in a curved or compliant surface that requires slip detection and flexibility, such as a robotic finger.


title: Revealing the API ecosystem and enterprise strategy via visual analytics

abstract: Seeking better understanding of digital transformation.


title: Toward Abnormal Trajectory and Event Detection in Video Surveillance

abstract: In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches.


title: Chapter 1 An Introduction to Privacy-Preserving Data Mining

abstract: The field of privacy has seen rapid advances in recent years because of the increases in the ability to store data. In particular, recent advances in the data mining field have lead to increased concerns about privacy. While the topic of privacy has been traditionally studied in the context of cryptography and information-hiding, recent emphasis on data mining has lead to renewed interest in the field. In this chapter, we will introduce the topic of privacy-preserving data mining and provide an overview of the different topics covered in this book.


title: VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems

abstract: Continuous word and phrase vectors have proven useful in a number of NLP tasks. Here we describe our experience using them as a source of features for the SemEval-2015 task 3, consisting of two community question answering subtasks: Answer Selection for categorizing answers as potential, good, and bad with regards to their corresponding questions; and YES/NO inference for predicting a yes, no, or unsure response to a YES/NO question using all of its good answers. Our system ranked 6th and 1st in the English answer selection and YES/NO inference subtasks respectively, and 2nd in the Arabic answer selection subtask.


title: A W-band micro coaxial passive monopulse comparator network with integrated cavity-backed patch antenna array

abstract: Summary form only given, as follows. An integrated W-band passive monopulse comparator network is constructed using four rat-race hybrids, designed using the PolystrataTM 3-D sequential micromachining process. The comparator network has a simulated azimuth null of 44.6 dB and elevation null of 44.4 dB. The network is monolithically integrated with an array of cavity backed patch antennas and diode detectors for measurement purposes. An azimuth null of 31.4 dB and an elevation null of 22.5 dB are measured at 93.5 GHz.


title: Usability testing method in augmented reality application

abstract: Evaluation on system or software is important thing to do, when developing a software or system. This step will ensures yours developed system or software has high quality in functionality or non-functionality needs. Several methods can be used to evaluate the Augmented Reality application: subjective measurement using human perception or objective measure from observation, or evaluation by expert through cognitive walkthrough, heuristic evaluation, lab observation, and questionnaire. An evaluation is more valid if evaluation was done in multiple methods in order to confirm the result of evaluation.


title: Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

abstract: This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q -learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q -learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.


title: Occupancy Grid Mapping in Urban Environments from a Moving On-Board Stereo-Vision System

abstract: Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. Furthermore, when moving in a real dynamic world, traditional occupancy grid mapping is required not only with the ability to detect occupied areas, but also with the capability to understand dynamic environments. The paper addresses this issue by presenting a stereo-vision-based framework to create a dynamic occupancy grid map, which is applied in an intelligent vehicle driving in an urban scenario. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The proposed framework consists of two components. The first is motion estimation for the moving vehicle itself and independent moving objects. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. This is very practical in real applications. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments.


title: Phase-Based UHF RFID Tracking With Nonlinear Kalman Filtering and Smoothing

abstract: In this paper, we present an UHF RFID location tracking system, which is based on measuring the phases of back scattered signals from RFID tag using multiple spatially distributed antennas at a single carrier frequency. The wavelength ambiguity of the phase measurements is resolved by using the extended Kalman filter (EKF) and the Rauch-Tung-Striebel (RTS) smoother, where the state includes the position, velocity and the phase offsets of antennas. The performance of the method is experimentally verified at 890 MHz using a commercially available RFID reader.


title: DOING COMPETENCIES WELL: BEST PRACTICES IN COMPETENCY MODELING

abstract: The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from the major perspectives on this topic (including applied, academic, and professional). Competency models are defined, and their key advantages are explained. Then, the many uses of competency models are described. The bulk of the article is a set of 20 best practices divided into 3 areas: analyzing competency information, organizing and presenting competency information, and using competency information. The best practices are described and explained, practice advice is provided, and then the best practices are illustrated with numerous practical examples. Finally, how competency modeling differs from and complements job analysis is explained throughout. The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from all the major perspectives on this topic including two major companies, a major consulting firm, a major university, and the Society for Industrial and Organizational Psychology (SIOP) taskforce on competency modeling. From all the different perspectives, we will delineate a set of 20 best practices and then illustrate them with practical examples from actual organizations. For the interested reader, we also link the practices to the existing literature which consists mostly of writings based on practical experience (e.g., case studies, commentaries) because little empirical


Understanding Satellite-Imagery-Based Crop Yield Predictions


title: The design of an intelligent mechanical Active Prosthetic Knee

abstract: With the cost of active prostheses continuing to rise, war amputees, specifically in developing war-torn countries are unable to partake in the advancement of intelligent limbs. Many of the commercial prosthetic knee joints available are either wholly passive or provide only partial active feedback and control. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint. Utilizing gait trend detection and streaming it to an intelligent logic, allows autonomous movement that mimics human locomotion. This study involves the design methodology of both the mechanical aspects as well as the fuzzy-based control system. The logic behind the fuzzy system can replicate the human gait cycle by dividing the cycle into its own unique phases.


title: Key Technologies for Big Data Stream Computing

abstract: As a new trend for data-intensive computing, real-time stream computing is gaining significant attention in the big data era. In theory, stream computing is an effective way to support big data by providing extremely low-latency processing tools and massively parallel processing architectures in real-time data analysis. However, in most existing stream computing environments, how to efficiently deal with big data stream computing, and how to build efficient big data stream computing systems are posing great challenges to big data computing research. First, the data stream graphs and the system architecture for big data stream computing, and some related key technologies, such as system structure, data transmission, application interfaces, and high availability, are systemically researched. Then, we give a classification of the latest research and depict the development status of some popular big data stream computing systems, including Twitter Storm, Yahoo! S4, Microsoft TimeStream, and Microsoft Naiad. Finally, the potential challenges and future directions of big data stream computing are discussed. 11.


The Characterisation of TLC NAND Flash Memory, Leading to a Definable Endurance/Retention Trade-Off


title: Suitability of existing Software development Life Cycle (SDLC) in context of Mobile Application Development Life Cycle (MADLC)

abstract: Mobile technology has seen a steep rise in its demand and usage, which has further increased the need for specialized software for mobile devices. For developing mobile applications, existing software development process models are incorporated. But looking at the remarkable growth of mobile devices, the complexity involved in development of mobile applications needs a dedicated life cycle process model, instead of using traditional life cycle process models. Although many existing life cycle models are adapted for mobile apps development but there are certain constraints that needs to be addressed specifically, such as Performance Factor, Power Factor, Band Factor, Connectivity Factor, Context Factor, Graphic Interface Factor, Input Interface Factor, etc. It is a stimulating task to develop mobile application due to these technical constraints. In this paper, a comparative analysis of traditional software life cycle models that are widely used in software development and also discussed the strengths and weakness of using these models for mobile application development. It also emphasize on need for dedicated mobile application development life cycle process in order to provide high quality mobile apps and minimize mobile app development problems and errors that are faced at later stage.


title: An Integrated Simulation Tool for Computer Architecture and Cyber-Physical Systems

abstract: Simulating computer architecture as a cyber-physical system has many potential use cases including simulation of side channels and software-in-the-loop modeling and simulation. This paper presents an integrated simulation tool using a computer architecture simulator, gem5 and Ptolemy II. As a case study of this tool, we build a power and thermal model for a DRAM using the proposed tool integration approach where architectural aspects are modeled in gem5 and physical aspects are modeled in Ptolemy II. We also demonstrate simulation results of power and temperature of a DRAM with software benchmarks.


title: Effects of reduced sulfur compounds on Pd-catalytic hydrodechlorination of trichloroethylene in groundwater by cathodic H2 under electrochemically induced oxidizing conditions.

abstract: Reduced sulfur compounds (RSCs) poison Pd catalysts for catalytic hydrodechlorination of contaminants in anoxic groundwater. This study investigates the effects of RSCs on Pd-catalytic hydrodechlorination of trichloroethylene (TCE) in oxic groundwater. Water electrolysis in an undivided electrolytic cell is used to produce H2 for TCE hydrodechlorination under oxidizing conditions. TCE is efficiently hydrodechlorinated to ethane, with significant accumulation of H2O2 under acidic conditions. The presence of sulfide at concentrations less than 93.8 μM moderately inhibits TCE hydrodechlorination and H2O2 production. The presence of sulfite at low concentrations (≤1 mM) significantly enhances TCE decay, while at high concentration (3 mM) inhibits initially and enhances afterward when sulfite concentration declines to less than 1 mM. Using radical scavenging experiments and an electron spin resonance assay, SO3(•-), which is generated from sulfite under oxidizing conditions, is validated as the new reactive species contributing to the enhancement. This study reveals a distinct mechanism of effect of sulfite on TCE hydrodechlorination by Pd and H2 in oxic groundwater and presents an alternative approach to increasing resistance of Pd to RSCs poisoning.


title: Detection, Tracking and Recognition of Traffic Signs from Video Input

abstract: In this paper a comprehensive approach to the recognition of traffic signs from video input is proposed. A trained attentive classifier cascade is used to scan the scene in order to quickly establish regions of interest (ROI). Sign candidates within ROIs are captured by detecting the instances of equiangular polygons using a Hough Transform-style shape detector. To ensure a stable tracking of the likely traffic signs, especially in cluttered background, we propose a Pixel Relevance Model, where the pixel relevance is defined as a confidence measure for a pixel being part of a sign's contour. The relevance of the hypothesized contour pixels is updated dynamically within a small search region maintained by a Kalman filter, which ensures faster computation. Gradient magnitude is used as an observable evidence for this update process. In the classification stage, a temporally integrated template matching technique based on the class-specific discriminative local region representation of an image is adopted. We have evaluated the proposed approach on a large database of 135 traffic signs and numerous real traffic video sequences. A recognition accuracy of over 93% in near real-time has been achieved.


title: The Impact of Tangible User Interfaces on Designers' Spatial Cognition

abstract: ABSTRACT Most studies on tangible user interfaces for the tabletop design systems are being undertaken from a technology viewpoint. Although there have been studies that focus on the development of new interactive environments employing tangible user interfaces for designers, there is a lack of evaluation with respect to designers' spatial cognition. In this research we study the effects of tangible user interfaces on designers' spatial cognition to provide empirical evidence for the anecdotal views of the effect of tangible user interfaces. To highlight the expected changes in spatial cognition while using tangible user interfaces, we compared designers using a tangible user interface on a tabletop system with 3D blocks to designers using a graphical user interface on a desktop computer with a mouse and keyboard. The ways in which designers use the two different interfaces for 3D design were examined using a protocol analysis method. The result reveals that designers using 3D blocks perceived more spatia...


title: An automated student attendance tracking system based on voiceprint and location

abstract: Taking attendance has been widely used as a method to track students' academic behaviours. But conventional approaches are usually time consuming and inaccurate. In this paper, we have proposed an automated attendance tracking system, where students can use smartphones to submit attendance in parallel. Identity of a student is collaboratively verified by voiceprint and realtime location. Service isolation and token-based mechanism is applied specifically in the system as well, for the purpose of protecting users' privacy and the system's security. Tests in real world environment has demonstrated that our system is characterised as high efficiency and accuracy in tracking attendance.


title: Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification

abstract: The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new hand-crafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.


title: Reconceptualizing Situations, Dispositions, Dynamics, and Invariance in Personality Structure

abstract: A theory was proposed to reconcile paradoxical findings on the invariance of personality and the variability of behavior across situations. For this purpose, individuals were assumed to differ in (a) the accessibility of cognitive-affective mediating units (such as encodings, expectancies and beliefs, affects, and goals) and (b) the organization of relationships through which these units interact with each other and with psychological features of situations. The theory accounts for individual differences in predictable patterns of variability across situations (e.g., if A. then she X, but ifE then she Y), as well as for overall average levels of behavior, as essential expressions or behavioral signatures of the same underlying personality system. Situations, personality dispositions, dynamics, and structure were reconceptualized from this perspective. The construct of personality rests on the assumption that individuals are characterized by distinctive qualities that are relatively invariant across situations and over time. In a century of personality research, however, abundant evidence has documented that individual differences in social behaviors tend to be surprisingly variable across different situations. Although this finding has been interpreted as evidence against the utility of the personality construct, we show that it need not be and, on the contrary, that this variability reflects some of the essence of personality coherence. When personality is conceptualized as a stable system that mediates how the individual selects, construes, and processes social information and generates social behaviors, it becomes possible to account simultaneously for both the invariant qualities of the underlying personality and the predictable variability across situations in some of its characteristic behavioral expressions. In this article, we begin with a review of recent empirical data demonstrating that individuals are characterized not only by stable individual differences in their overall levels of behavior, but also by distinctive and stable patterns of behavior variability across situations. These findings invite a new conception of personality in which such patterns of variability are seen not as


title: Online learning over graphs

abstract: We apply classic online learning techniques similar to the perceptron algorithm to the problem of learning a function defined on a graph. The benefit of our approach includes simple algorithms and performance guarantees that we naturally interpret in terms of structural properties of the graph, such as the algebraic connectivity or the diameter of the graph. We also discuss how these methods can be modified to allow active learning on a graph. We present preliminary experiments with encouraging results.


title: National archives and records – the legal and policy considerations for the UK

abstract: Public Sector Information (PSI) is a strategic resource without which government could not function. It feeds directly into policy formation and growing volumes of personal and cultural information are in constant demand, fulfilling a wide variety of personal and business needs. Whereas traditional conduits for the supply and publication of such material were confined to the printed page and broadcast media, digitisation has opened up substantial new means for delivering access to these resources. This 'access revolution' has itself created a new array of issues about what types of material should be captured to form the national archive and what intellectual property rights apply. One important aspect is the less discussed issue of policy for the maintenance and development of the public archive. Mass digitisation of the public record requires investment and secure maintenance if the archive is to be sustained. So what kind of regulatory structure needs to be established to facilitate this? Moreover, how far should private sector participation and expertise be utilised? On what basis should proprietary and user rights be granted where such collaborations take place? Finally, what message comes out of this as regards regulatory reform for libraries and archives?


title: Developing a NFC-equipped smart classroom: Effects on attitudes toward computer science

abstract: This study describes a smart classroom system that integrates near field communication (NFC) technology to automate attendance management, locate students, and provide real-time student feedback. The proposed system's attendance management feature can conserve time and reduce paper-work because students use their NFC-enabled smartphones or NFC smart cards to register their attendance automatically. Also, the system's student-positioning feature is particularly useful for teaching large classes because teachers typically experience difficulties in both recognizing students and familiarizing themselves with their students' learning progress. In addition, the real-time feedback function could be beneficial especially for Asian students because they are usually reluctant to express their opinions during class. This study also evaluates the proposed system's effect on students' attitude toward science education by applying relevant theories. Our case study of a computer science course shows that the attitudes of students toward computer science generally improved following the implementation of the proposed system. Because more than half of the measures from the aspects of learning computer science at school and importance of computer science are positive and significant, this indicates that students find computer science more interesting, expecting, exciting, beneficial, helpful, and appealing after the using the proposed system.


title: A study of factors that affect user intentions toward email service switching

abstract: Internet portals have long been using email services to attract new members and retain existing customers. Recent announcements by major Internet portals of increases in email storage capacity demonstrate this strategy. It is, however, uncertain how people react to such announcements. We wished to understand the mechanisms associated with users' intention to switch their current email service provider. Key variables pertaining to customer satisfaction with email service, attractive alternatives, and switching cost were examined to explain the dynamics of switching. An empirical study based on 1408 survey responses confirmed the importance of user satisfaction for service continuation. The variables attractive alternatives and continuity cost also demonstrated strong association with intention to switch. We found that the variable attractive alternatives moderated the relationship between customer satisfaction and intention to switch, but a moderating effect of switching cost was not found. Our investigation indicated that the findings of extant studies on the dynamics of service switching or re-purchase also apply to email services.


title: A snake robot joint mechanism with a contact force measurement system

abstract: A snake robot can traverse cluttered and irregular environments by using irregularities around its body as push-points to aid the propulsion. This is denoted obstacle-aided locomotion and requires the snake robot to have two features: 1) a smooth exterior surface combined with 2) a contact force sensing system. These two features are characteristic of biological snakes, but have received limited attention in snake robot designs so far. This paper presents a joint mechanism for a snake robot aimed at meeting both these requirements. The paper details the design and implementation of the joint mechanism and presents experimental results that validate the function of the contact force measurement system.


title: A focus on graphical model design and applications to document and image analysis ] [

abstract: In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those distributions are called "topics" because, when fit to data, they capture the salient themes that run through the collection. We describe both finite-dimensional parametric topic models and their Bayesian nonparametric counterparts, which are based on the hierarchical Dirichlet process (HDP). We discuss two extensions of topic models to time-series data-one that lets the topics slowly change over time and one that lets the assumed prevalence of the topics change. Finally, we illustrate the application of topic models to nontext data, summarizing some recent research results in image analysis.


title: An Empirical Examination of the Determinants of Successful Transition Management in Offshore Business Process Outsourcing

abstract: Business process outsourcing (BPO) is fast becoming a mainstream business phenomenon and in recent times offshore business process outsourcing has gained momentum, with a number of companies wanting to capitalize on the benefits of offshore services. Effectively transitioning the identified business processes to a cost effective geographic location and stabilizing them in a defined timeframe constitutes an important step for successful offshore BPO. Towards this, transition management assumes paramount importance. In this paper, we report the preliminary findings of our empirical study focused on examining the determinants of successful transition management for offshore BPO, and provide recommendations for both the outsourcing firm and the BPO vendor. We also present directions for further research relating to transition management in offshore BPO arrangements.


title: Optimal thresholding of classifiers to maximize F1 measure

abstract: This paper provides new insight into maximizing F1 measures in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, the F1 measure is widely used to evaluate the success of a binary classifier when one class is rare. Micro average, macro average, and per instance average F1 measures are used in multilabel classification. For any classifier that produces a real-valued output, we derive the relationship between the best achievable F1 value and the decision-making threshold that achieves this optimum. As a special case, if the classifier outputs are well-calibrated conditional probabilities, then the optimal threshold is half the optimal F1 value. As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive. When the actual prevalence of positive examples is low, this behavior can be undesirable. As a case study, we discuss the results, which can be surprising, of maximizing F1 when predicting 26,853 labels for Medline documents.


Understanding Medical Named Entity Extraction in Clinical Notes


title: Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists

abstract: For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview , an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview 's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.


title: An acoustic segment modeling approach to query-by-example spoken term detection

abstract: The framework of posteriorgram-based template matching has been shown to be successful for query-by-example spoken term detection (STD). This framework employs a tokenizer to convert query examples and test utterances into frame-level posteriorgrams, and applies dynamic time warping to match the query posteriorgrams with test posteriorgrams to locate possible occurrences of the query term. It is not trivial to design a reliable tokenizer due to heterogeneous test conditions and the limitation of training resources. This paper presents a study of using acoustic segment models (ASMs) as the tokenizer. ASMs can be obtained following an unsupervised iterative procedure without any training transcriptions. The STD performance of the ASM tokenizer is evaluated on Fisher Corpus with comparison to three alternative tokenizers. Experimental results show that the ASM tokenizer outperforms a conventional GMM tokenizer and a language-mismatched phoneme recognizer. In addition, the performance is significantly improved by applying unsupervised speaker normalization techniques.


title: Stability of Networked Control Systems

abstract: First, we review some previous work on networked control systems (NCSs) and offer some improvements. Then, we summarize the fundamental issues in NCSs and examine them with different underlying network-scheduling protocols. We present NCS models with network-induced delay and analyze their stability using stability regions and a hybrid systems technique. Following that, we discuss methods to compensate network-induced delay and present experimental results over a physical network. Then, we model NCSs with packet dropout and multiple-packet transmission as asynchronous dynamical systems and analyze their stability. Finally, we present our conclusions.


title: Hand gesture recognition using a neural network shape fitting technique

abstract: A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success.


Query Intent Detection using Convolutional Neural Networks


title: Bringing Virtualization to the x86 Architecture with the Original VMware Workstation

abstract: This article describes the historical context, technical challenges, and main implementation techniques used by VMware Workstation to bring virtualization to the x86 architecture in 1999. Although virtual machine monitors (VMMs) had been around for decades, they were traditionally designed as part of monolithic, single-vendor architectures with explicit support for virtualization. In contrast, the x86 architecture lacked virtualization support, and the industry around it had disaggregated into an ecosystem, with different vendors controlling the computers, CPUs, peripherals, operating systems, and applications, none of them asking for virtualization. We chose to build our solution independently of these vendors. As a result, VMware Workstation had to deal with new challenges associated with (i) the lack of virtualization support in the x86 architecture, (ii) the daunting complexity of the architecture itself, (iii) the need to support a broad combination of peripherals, and (iv) the need to offer a simple user experience within existing environments. These new challenges led us to a novel combination of well-known virtualization techniques, techniques from other domains, and new techniques. VMware Workstation combined a hosted architecture with a VMM. The hosted architecture enabled a simple user experience and offered broad hardware compatibility. Rather than exposing I/O diversity to the virtual machines, VMware Workstation also relied on software emulation of I/O devices. The VMM combined a trap-and-emulate direct execution engine with a system-level dynamic binary translator to efficiently virtualize the x86 architecture and support most commodity operating systems. By relying on x86 hardware segmentation as a protection mechanism, the binary translator could execute translated code at near hardware speeds. The binary translator also relied on partial evaluation and adaptive retranslation to reduce the overall overheads of virtualization. Written with the benefit of hindsight, this article shares the key lessons we learned from building the original system and from its later evolution.


Extracting Definitions of Mathematical Expressions in Scientific Papers


title: On-Body Smartphone Localization with an Accelerometer

abstract: A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device’s functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.


title: Corpus Development for Affective Video Indexing

abstract: Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of corpus development guidelines based on state-of-the-art practice intended to support researchers in this field. Affective descriptions can be used for video search and browsing systems offering users affective perspectives. The paper is motivated by the observation that affective video indexing has yet to fully profit from the standard corpora (data sets) that have benefited conventional forms of video indexing. Affective video indexing faces unique challenges, since viewer-reported affective reactions are difficult to assess. Moreover affect assessment efforts must be carefully designed in order to both cover the types of affective responses that video content evokes in viewers and also capture the stable and consistent aspects of these responses. We first present background information on affect and multimedia and related work on affective multimedia indexing, including existing corpora. Three dimensions emerge as critical for affective video corpora, and form the basis for our proposed guidelines: the context of viewer response, personal variation among viewers, and the effectiveness and efficiency of corpus creation. Finally, we present examples of three recent corpora and discuss how these corpora make progressive steps towards fulfilling the guidelines.


Anomaly detection in large graphs


title: A 4-Phase 30–70 MHz Switching Frequency Buck Converter Using a Time-Based Compensator

abstract: A high switching frequency multi-phase buck converter architecture using a time-based compensator is presented. Efficiency degradation due to mismatch between the phases is mitigated by generating precisely matched duty-cycles by combining a time-based multi-phase generator (MPG) with a time-based PID compensator (T-PID). The proposed approach obviates the need for a complex current sensing and calibration circuitry needed to implement active current sharing in an analog controller. It also eliminates the need for a high resolution analog-to-digital converter and digital pulse width modulator needed for implementing passive current sharing in a digital controller. Fabricated in a 65 nm CMOS process, the prototype multi-phase buck converter occupies an active area of 0.32 mm $^{2}$ , of which the controller occupies only 0.04 mm $^{2}$ . The converter operates over a wide range of switching frequencies (30–70 MHz) and regulates output to any desired voltage in the range of 0.6 V to 1.5 V from 1.8 V input voltage. With a 400 mA step in the load current, the settling time is less than 0.6 $\mu$ s and the measured duty-cycle mismatch is less than 0.48%. Better than 87% peak efficiency is achieved while consuming a quiescent current of only 3 $\mu$ A/MHz.


title: Batch Mode Active Learning for Object Detection Based on Maximum Mean Discrepancy

abstract: Various active learning methods have been proposed for image classification problems, while very little work addresses object detection. Measuring the informativeness of an image based on its object windows is a key problem in active learning for object detection. In this paper, an image selection method to select the most representative images is proposed based on measuring their object window distributions by Maximum Mean Discrepancy (MMD). Then an active learning method for object detection is introduced based on MMD-based image selection. Experimental results show that MMD-based image selection can improve object detection performance compared to random image selection. The proposed active learning method based on MMD image selection also outperforms a classical active learning method and passive learning method.


title: Thumb Motor Performance Varies by Movement Orientation, Direction, and Device Size During Single-Handed Mobile Phone Use

abstract: Objective: The aim of this study was to determine if thumb motor performance metrics varied by movement orientation, direction, and device size during single-handed use of a mobile phone device.Background: With the increased use of mobile phones, understanding how design factors affect and improve performance can provide better design guidelines.Method: A repeated measures laboratory experiment of 20 right-handed participants measured the thumb tip’s 3-D position relative to a phone during reciprocal tapping tasks across four phone designs and four thumb tip movement orientations. Each movement orientation included two movement directions: an “outward” direction consisting in CMC (carpometacarpal) joint flexion or abduction movements and an “inward” direction consisting in CMC joint extension or adduction movements. Calculated metrics of the thumb’s motor performance were Fitts’ effective width and index of performance.Results: Index of performance varied significantly across phones, with performance bein...


title: Sequence to Sequence Learning with Neural Networks

abstract: Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.


A Performance Evaluation of Hypervisor, Unikernel, and Container Network I/O Virtualization


title: Hierarchical Attentional Hybrid Neural Networks for Document Classification

abstract: Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not take into account the contexting importance of words and sentences. In this paper, we propose a new approach based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms for document classification tasks. We use of convolution layers varying window sizes to extract more meaningful, generalizable and abstract features by the hierarchical representation. The proposed method in improves the results of the current attention-based approaches for document classification.


title: Dynamic maps for long-term operation of mobile service robots

abstract: This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stability plasticity dilemma (the tradeoff between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple time scales simultaneously (five in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the time scale. Robust statistics are used to interpret the samples. It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes. The method was evaluated in a five-week experiment in a real dynamic environment. Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.


title: The effect of self-distancing on adaptive versus maladaptive self-reflection in children

abstract: Although children and adolescents vary in their chronic tendencies to adaptively versus maladaptively reflect over negative feelings, the psychological mechanisms underlying these different types of self-reflection among youngsters are unknown. We addressed this issue in the present research by examining the role that self-distancing plays in distinguishing adaptive versus maladaptive selfreflection among an ethnically and socioeconomically diverse sample of fifth-grade public schoolchildren. Children were randomly assigned to analyze their feelings surrounding a recent angerrelated interpersonal experience from either a self-immersed or self-distanced perspective. They then rated their negative affect and described in writing the stream of thoughts they experienced when they analyzed their feelings. Children’s stream-of-thought essays were content analyzed for the presence of recounting statements, reconstruing statements, and blame attributions. Path analyses indicated that children who analyzed their feelings from a self-distanced perspective focused significantly less on recounting the “hot,” emotionally arousing features of their memory (i.e., what happened to me?) and relatively more on reconstruing their experience. This shift in thought content—less recounting and more reconstruing—led children in the self-distanced group to blame the other person involved in their recalled experience significantly less, which in turn led them to display significantly lower levels of emotional reactivity. These findings help delineate the psychological mechanisms that distinguish adaptive versus maladaptive forms of self-reflection over anger experiences in children. Their basic findings and clinical implications are discussed.


title: Army of Botnets

abstract: The trend toward smaller botnets may be more dangerous than large botnets, in terms of large-scale attacks like distributed denials of service. We examine the possibility of “super-botnets,” networks of independent botnets that can be coordinated for attacks of unprecedented scale. For an adversary, super-botnets would also be extremely versatile and resistant to countermeasures. As such, superbotnets must be examined by the research community, so that defenses against this threat can be developed proactively. Our simulation results shed light on the feasibility and structure of super-botnets and some properties of their command-and-control mechanism. New forms of attack that super-botnets can launch are explored, and possible defenses against the threat of super-botnets are suggested.


title: Stealing Machine Learning Models via Prediction APIs

abstract: Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis. ::: The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., "steal") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.


title: Multi-scale morphological analysis for retinal vessel detection in wide-field fluorescein angiography

abstract: We describe a new method for accurate retinal vessel detection in wide-field fluorescein angiography (FA), which is a challenging problem because of the variations in vasculature between different orientations and large and small vessels, and the changes in the vasculature appearance as the injection of the dye perfuses the retina. Decomposing the original FA image into multiple resolutions, the vessels at each scale are segmented independently by first correcting for inhomogeneous illumination, then applying morphological operations to extract rectilinear structure and finally applying adaptive binarization. Specifically, a modified top-hat filter is applied using linear structuring elements with 9 directions. The maximum value of the resulting response images at each pixel location is then used for adaptive binarization. Final vessel segments are identified by fusing vessel segments at each scale. Quantitative results on VAMPIRE dataset, which includes high resolution wide-field FA images and hand-labeled ground truth vessel segments, demonstrate that the proposed method provides a significant improvement on vessel detection (approximately 10% higher recall, with same precision) than the method originally published with VAMPIRE dataset.


title: A generalized Wiener attack on RSA

abstract: We present an extension of Wiener’s attack on small RSA secret decryption exponents [10]. Wiener showed that every RSA public key tuple (N,e) with \(e \in {\mathbb{Z}}_{\phi(N)}^*\) that satisfies ed − 1 = 0 mod φ(N) for some \(d<\frac 1 3 N^{\frac 1 4}\) yields the factorization of N=pq. Our new method finds p and q in polynomial time for every (N,e) satisfying ex + y = 0 mod φ(N) with ::: ::: $$ x < \frac 1 3 N^{\frac 1 4} \quad \textrm{and} \quad |y| = {\cal O}(N^{- \frac 3 4}ex). $$ ::: ::: In other words, the generalization works for all secret keys d= – xy − 1, where x, y are suitably small. We show that the number of these weak keys is at least \(N^{\frac 3 4-\epsilon}\) and that the number increases with decreasing prime difference p-q. As an application of our new attack, we present the cryptanalysis of an RSA-type scheme presented by Yen, Kim, Lim and Moon [11,12]. Our results point out again the warning for crypto-designers to be careful when using the RSA key generation process with special parameters.


title: Sambot: A self-assembly modular robot for swarm robot

abstract: This paper presents a novel self-assembly modular robot (Sambot) that also shares characteristics with self-reconfigurable and self-assembly and swarm robots. Each Sambot can move autonomously and connect with the others. Multiple Sambot can be self-assembled to form a robotic structure, which can be reconfigured into different configurable robots and can locomote. A novel mechanical design is described to realize function of autonomous motion and docking. Introducing embedded mechatronics integrated technology, whole actuators, sensors, microprocessors, power and communication unit are embedded in the module. The Sambot is compact and flexble, the overall size is 80×80×102mm. The preliminary self-assembly and self-reconfiguration of Sambot is discussed, and several possible configurations consisting of multiple Sambot are designed in simulation environment. At last, the experiment of self-assembly and self-reconfiguration and locomotion of multiple Sambot has been implemented.


title: Gaussian Sampling over the Integers: Efficient, Generic, Constant-Time

abstract: Sampling integers with Gaussian distribution is a fundamental problem that arises in almost every application of lattice cryptography, and it can be both time consuming and challenging to implement. Most previous work has focused on the optimization and implementation of integer Gaussian sampling in the context of specific applications, with fixed sets of parameters. We present new algorithms for discrete Gaussian sampling that are both generic (application independent), efficient, and more easily implemented in constant time without incurring a substantial slow-down, making them more resilient to side-channel (e.g., timing) attacks. As an additional contribution, we present new analytical techniques that can be used to simplify the precision/security evaluation of floating point cryptographic algorithms, and an experimental comparison of our algorithms with previous algorithms from the literature.


title: ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

abstract: A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.


title: Fuzzy vectorial-based similarity detection of music plagiarism

abstract: Plagiarism, i.e., copying the work of others and trying to pass it off as one own, is a debated topic in different fields. In particular, in music field, the plagiarism is a controversial and debated phenomenon that has to do with the huge amount of money that music is able to generate. However, the existing mechanisms for plagiarism detection mainly apply superficial and brute-force string matching techniques. Such well-known metrics, widely used to discover similarities in text documents, cannot work well in discovering similarities in music compositions. Despite the wide-spread belief that few notes in common between two songs is enough to decide whether a plagiarism exists, the analysis of similarities is a very complex process. In this work, we provide novel perspectives in the field of automatic music plagiarism detection, and specifically, we propose an approach based on a fuzzy vectorial-based similarity. Given a suspicious melody, our approach envisions three steps: (1) its transformation in a vectorial representation, (2) retrieving of a list of similar melodies, (3) analysis and comparison with this subset of associated similar scores by using a fuzzy degree of similarity, that varies in a range between 0 for melodies that are fully musically different, and 1 for identical melodies. To assess the effectiveness of our system we performed tests on a large dataset of ascertained plagiarisms. Results show that it is able to reach an accuracy of 93%.


title: Autonomous car following: A learning-based approach

abstract: We propose a learning-based method for the longitudinal control of an autonomous vehicle on the highway. We use a driver model to generate acceleration inputs which are used as a reference by a model predictive controller. The driver model is trained using real driving data, so that it can reproduce the driver's behavior. We show the system's ability to reproduce different driving styles from different drivers. By solving a constrained optimization problem, the model predictive controller ensures that the control inputs applied to the vehicle satisfy some safety criteria. This is demonstrated on a vehicle by artificially creating potentially dangerous situations with virtual obstacles.