title: Person Re-Identification Over Camera Networks Using Multi-Task Distance Metric Learning

abstract: Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.


title: Capabilities of Dynamic Reconfiguration of Multiagent-Based Industrial Control Systems

abstract: This paper focuses on reconfiguration aspects of agent-based industrial control systems. Reconfiguration is the ability of the control system to modify its behavior to cope with unusual conditions and changes of the production environment like machine breakdowns, product innovations, rush orders, etc. This paper presents a multiagent system dedicated to material flow control-manufacturing agent simulation tool (MAST)-and discusses in detail how agents efficiently cope with the dynamic reconfiguration. Two scenarios are discussed: 1) the automatic reconfiguration of the transportation paths carried out by agents in case of failures and 2) the structural reconfiguration enabling online addition, removal, or reconnection of equipment or transportation paths on the factory floor. Two proof-of-concept applications of MAST in University of Cambridge's and Vienna University of Technology's testbeds are described.


title: ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation

abstract: Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are useful for many practical tasks in machine learning. Synaptic weights, as well as neuron activation functions within the deep network are typically stored with high-precision formats, e.g. 32 bit floating point. However, since storage capacity is limited and each memory access consumes power, both storage capacity and memory access are two crucial factors in these networks. Here we present a method and present the ADaPTION toolbox to extend the popular deep learning library Caffe to support training of deep CNNs with reduced numerical precision of weights and activations using fixed point notation. ADaPTION includes tools to measure the dynamic range of weights and activations. Using the ADaPTION tools, we quantized several CNNs including VGG16 down to 16-bit weights and activations with only 0.8% drop in Top-1 accuracy. The quantization, especially of the activations, leads to increase of up to 50% of sparsity especially in early and intermediate layers, which we exploit to skip multiplications with zero, thus performing faster and computationally cheaper inference.


title: A Dynamic Target Visual Positioning Method Based on ROI

abstract: The method of visual positioning can be mainly divided into fixed camera system and mobile camera system. In this paper, we propose a dynamic target positioning method based on ROI (regions of interest), which utilizes the deep learning method to detect targets and employs the fixed camera system to locate the targets. The ROI method proposed only process the region of target, which can reduce the time-consuming, and it can solve the problem that none or less feature points of the target is detected in 3D reconstruction. We make a dataset of the experimental car and use YOLOv2 to train the dataset, by which the training model of the experimental car is obtained; then the trained model is used to detect the experimental car in the video data which acquired by two USB cameras and get the ROI of the moving target. According to the triangulation method, only the ROI of the image data at the same time is reconstructed, and the average of the obtained coordinates as the position of the car at that moment. In the experiment, we use the positions obtained by optitrack system as the true values, and compare the positions got by the method of this paper (ROI method) with the true value. The experimental results show that the ROI method proposed can be used to locate the dynamic target with the positioning accuracy at the cm level.


title: Analysis and measurement of 3D torque and forces for permanent magnet motors with slotless windings

abstract: Slotless windings, skewed and rhombic, are widely used in industry. Beside the drive torque, possibly undesired transverse torques and forces are generated, which have not been analyzed previously. An analytical derivation of the torque and force components in all three directions is detailed in this paper for the skewed winding. It is shown that for some winding configurations alternating transverse torque components are generated, which may compromise stable operation in applications where for example magnetic or gas bearings are involved. Moreover the windings are analyzed with regard to a potential use as active magnetic radial bearings in high-speed applications. Finally, measurements are presented to verify the theoretical results.


title: Information privacy concerns, antecedents and privacy measure use in social networking sites: Evidence from Malaysia

abstract: The research aims at gaining insights into information privacy concerns, its antecedents and privacy measure use in social networking sites. The Social Cognitive, Protection Motivation theories and gender factor were used as a basis to develop and confirm a research model. Using a cross-sectional survey design and cluster sampling technique, four-hundred thirteen questionnaires were distributed to undergraduates at a public Malaysian university; three-hundred forty were included in analyses. Data was analyzed using structural equation modeling technique. Results suggest that in order of importance only perceived severity, self-efficacy, perceived vulnerability, and gender are antecedents of information privacy concerns with social networking sites; response efficacy and rewards were not significant antecedents contrary to many past findings in the literature that used Social Cognitive and Protection Motivation Theory as a theoretical basis. Information privacy concerns explain privacy measure use in social networking sites. The implications of these results and study limitations are discussed.


The isolation of plant protoplasts.


title: Uterine endothelial cell proliferation before and after embryo implantation in rats

abstract: Angiogenesis occurs rarely in normal adult tissues. The female reproductive tract, however, provides several exceptions, including the endometrium during early pregnancy. The aim of this study was to quantify endothelial cell proliferation (a component of angiogenesis) in the rat endometrium at about the time of implantation, using immunohistochemistry with a double staining technique. Proliferating cells were stained using an antibody against proliferating cell nuclear antigen (PCNA; clone PC10), and endothelial cells were stained with a lectin from Griffonia simplicifolia. Results showed that the endothelial cell proliferative index in the endometrium rose significantly from approximately 1% on the first 2 days of pregnancy to 13% on day 3; and continued to rise to 28% on day 5. After embryo implantation, the endometrial endothelial cell proliferative index rose further to 71% on day 7 at embryo sites only; but significantly decreased to basal values at intersites. The endothelial cell proliferative indices in the myometrium and mesometrial triangle remained at basal values during the first 5 days, but increased to approximately 22% at embryo sites only by day 7. We conclude that in the rat: (1) endometrial angiogenesis may be occurring before embryo implantation; (2) endometrial endothelial and stromal cell proliferation occurs concomitantly, except on day 3 when endothelial cell proliferation begins in advance of other stromal cell proliferation; and (3) there are two separate mechanisms controlling uterine endothelial cell proliferation during early pregnancy. The first mechanism is maternally controlled and is apparent throughout the entire endometrium from day 3; and the second mechanism is apparent after implantation in the vicinity of the embryo.


title: Face recognition after plastic surgery: a comprehensive study

abstract: It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery, which thus poses a new challenge to automatic face recognition. This paper first gives a comprehensive study on Face Recognition After Plastic Surgery (FRAPS), with careful analysis of the effects of plastic surgery on face appearance and its challenges to face recognition. Then, to address FRAPS problem, an ensemble of Gabor Patch classifiers via Rank-Order list Fusion (GPROF) is proposed, inspired by the assumption of the interior consistency of face components in terms of identity. On the face database of plastic surgery, GPROF achieves much higher face identification rate than the best known results in the literature. Furthermore, with our impressive results, we suggest that plastic surgery detection should be paid more attend to. To address this problem, a partial matching based plastic surgery detection algorithm is proposed, aiming to detect four distinct types of surgery, i.e., the eyelid surgery, nose surgery, forehead surgery and face lift surgery. Our experimental results demonstrate that plastic surgery detection is a nontrivial task, and thus deserves more research efforts.


title: Personalization of Supermarket Product Recommendations

abstract: We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process. ::: ::: The recommender is currently being used in a pilot program with several hundred customers. Analysis of results to date have shown a 1.8% boost in program revenue as a result of purchases made directly from the list of recommended products. A substantial fraction of the accepted recommendations are from product classes new to the customer, indicating a degree of willingness to expand beyond present purchase patterns in response to reasonable suggestions.


title: Potential-based difference rewards for multiagent reinforcement learning

abstract: Difference rewards and potential-based reward shaping can both significantly improve the joint policy learnt by multiple reinforcement learning agents acting simultaneously in the same environment. Difference rewards capture an agent's contribution to the system's performance. Potential-based reward shaping has been proven to not alter the Nash equilibria of the system but requires domain-specific knowledge. This paper introduces two novel reward functions that combine these methods to leverage the benefits of both. ::: ::: Using the difference reward's Counterfactual as Potential (CaP) allows the application of potential-based reward shaping to a wide range of multiagent systems without the need for domain specific knowledge whilst still maintaining the theoretical guarantee of consistent Nash equilibria. ::: ::: Alternatively, Difference Rewards incorporating Potential-Based Reward Shaping (DRiP) uses potential-based reward shaping to further shape difference rewards. By exploiting prior knowledge of a problem domain, this paper demonstrates agents using this approach can converge either up to 23.8 times faster than or to joint policies up to 196% better than agents using difference rewards alone.


title: Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism

abstract: Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD "seed" genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology.


title: Exploring Current Viewing Context for TV Contents Recommendation

abstract: Due to the diversity of alternative programs to watch and the change of viewers' contexts, real-time prediction of viewers' preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers' preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer's current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models.


title: Real-time generation of populated virtual cities

abstract: This paper presents a new approach for real-time generation of 3D virtual cities. The main goal is to provide of a generic framework which support semi-automatic creation, manage-ment, and visualization of urban complex environments for virtual human simulation, called virtual urban life (VUL). It intends to minimize efforts of designers in the modeling of complex and huge environments. A versatile multi-level data model has been developed to support data management and visualization in an efficient way. Moreover, a polygon partitioning algorithm addresses the city allotment problem in an automatic way, according to input parameters and constraints. In addition, we discuss some results of virtual populated city simulations developed with proposed frame-work.


title: Managed Query Processing within the SAP HANA Database Platform

abstract: The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance.


title: SIMULATION AND COMPARISON OF SPWM AND SVPWM CONTROL FOR THREE PHASE INVERTER

abstract: A voltage source inverter is commonly used to supply a three-phase induction motor with variable frequency and variable voltage for variable speed applications. A suitable pulse width modulation (PWM) technique is employed to obtain the required output voltage in the line side of the inverter. The different methods for PWM generation can be broadly classified into Triangle comparison based PWM (TCPWM) and Space Vector based PWM (SVPWM). In TCPWM methods such as sine-triangle PWM, three phase reference modulating signals are compared against a common triangular carrier to generate the PWM signals for the three phases. In SVPWM methods, a revolving reference voltage vector is provided as voltage reference instead of three phase modulating waves. The magnitude and frequency of the fundamental component in the line side are controlled by the magnitude and frequency, respectively, of the reference vector. The highest possible peak phase fundamental is very less in sine triangle PWM when compared with space vector PWM. Space Vector Modulation (SVM) Technique has become the important PWM technique for three phase Voltage Source Inverters for the control of AC Induction, Brushless DC, Switched Reluctance and Permanent Magnet Synchronous Motors. The study of space vector modulation technique reveals that space vector modulation technique utilizes DC bus voltage more efficiently and generates less harmonic distortion when compared with Sinusoidal PWM (SPWM) technique. In this paper first a model for Space vector PWM is made and simulated using MATLAB/SIMULINK software and its performance is compared with Sinusoidal PWM. The simulation study reveals that Space vector PWM utilizes dc bus voltage more effectively and generates less THD when compared with sine PWM.


An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm


title: The Role of Search Engine Optimization in Search Marketing

abstract: This paper examines the impact of search engine optimization SEO on the competition between advertisers for organic and sponsored search results. The results show that a positive level of search engine optimization may improve the search engine's ranking quality and thus the satisfaction of its visitors. In the absence of sponsored links, the organic ranking is improved by SEO if and only if the quality provided by a website is sufficiently positively correlated with its valuation for consumers. In the presence of sponsored links, the results are accentuated and hold regardless of the correlation. When sponsored links serve as a second chance to acquire clicks from the search engine, low-quality websites have a reduced incentive to invest in SEO, giving an advantage to their high-quality counterparts. As a result of the high expected quality on the organic side, consumers begin their search with an organic click. Although SEO can improve consumer welfare and the payoff of high-quality sites, we find that the search engine's revenues are typically lower when advertisers spend more on SEO and thus less on sponsored links. Modeling the impact of the minimum bid set by the search engine reveals an inverse U-shaped relationship between the minimum bid and search engine profits, suggesting an optimal minimum bid that is decreasing in the level of SEO activity.


title: A Novel split radix fast fourier transform design for an adaptive and scalable implementation

abstract: Fast Fourier Transform (FFT) is a rudimentary operation in signal processing. Modern high speed signal processing application technologies, such as 4G LTE, 5G, Internet of Things (IoT), etc., necessitate a high throughput implementation of FFT. Moreover the FFT size required in aforementioned technologies tends to vary with the operation mode. It is highly desirable to have a FFT implementation that not only meets the high throughput demand but is also scalable to cater selectable N-point FFT. In this paper, our contribution is two-fold; we first propose a novel split-radix 4/8 FFT algorithm which is comparable in efficiency with radix-8 algorithm but is considerably less complex in design for large size FFTs. Secondly, we developed an automation tool that gives high level implementation details of an adaptive N-point split-radix FFT processor.


title: A Simple Planar Polarization Reconfigurable Monopole Antenna for GNSS/PCS

abstract: The design of a simple planar polarization reconfigurable monopole antenna for the Global Navigation Satellite System (GNSS) and Personal Communications System (PCS) is presented. The antenna consists of two meandered monopoles, a feeding network using the Wilkinson power divider, two switchable 90 $^{0}$ -phase shifters implemented using $\lambda/4$ -microstrip lines and a defected ground structure (DGS). The meandered monopoles resonating at about 1.55 GHz are placed perpendicular to each other. The input signal is divided into two signals with equal amplitude and phase by the power divider and fed to the meandered monopoles via the phase shifters. The two signals arriving at the two monopoles have a phase difference of 90 $^{0}$ , $-90^{0}$ or 0 $^{0}$ , depending on the phase shifters controlled using six PIN-diode switches, hence generating a right/left-handed circularly polarized (CP) or linearly polarized (LP) signal. We propose a novel biasing technique to control the six PIN diodes using five voltages. Measurement results show that the antenna in CP has an impedance bandwidth $({\rm S}11 of 1.06-1.64 GHz and an axial-ratio bandwidth $({\rm axial-ratio} of 1.43-1.84 GHz, and in LP has an impedance bandwidth of 1.63-1.88 GHz. Simulated and measured results on S11, AR, radiation pattern, and gains show good agreements.


title: A theory of visual attention

abstract: A unified theory of visual recognition and attentional selection is developed by integrating the biased-choice model for single-stimulus recognition (Luce, 1963; Shepard, 1957) with a choice model for selection from multielement displays (Bundesen, Pedersen, & Larsen, 1984) in a race model framework. Mathematically, the theory is tractable, and it specifies the computations necessary for selection. The theory is applied to extant findings from a broad range of experimental paradigms. The findings include effects of object integrality in selective report, number and spatial position of targets in divided-attention paradigms, selection criterion and number of distracters in focused-attention paradigms, delay of selection cue in partial report, and consistent practice in search. On the whole, the quantitative fits are encouraging.


Language. An Introduction to the Study of Speech


title: A Reconfigurable 5-to-14 bit SAR ADC for Battery-Powered Medical Instrumentation

abstract: In battery-powered medical instrumentation, the resolution and signal bandwidth of analog-to-digital converters (ADCs) have to be adapted to the needs of the application to avoid power wastage. This paper presents a reconfigurable successive approximation register (SAR) ADC implemented in 130 nm CMOS that resolves 5–14 bit with a maximum achievable effective number of bits (ENOB) of 13.5 using non-subtractive dither. In the proposed ADC design, the power consumption can be traded for accuracy to improve the energy efficiency and extend its application range, while reducing system integration complexity. A figure-of-merit (FoM) of 59 fJ/conversion is achieved at 1.2 V supply and the converter occupies an area of 0.42 ${\rm mm}^{2}$ . Measurement results of the ADC integrated in a multi-channel analog front-end (AFE) circuit show the suitability of the ADC for portable medical monitoring devices.


title: Visual descriptors for scene categorization: experimental evaluation

abstract: Humans are endowed with the ability to grasp the overall meaning or the gist of a complex visual scene at a glance. We need only a fraction of a second to decide if a scene is indoors, outdoors, on a busy street, or on a clear beach. In recent years, computational gist recognition or scene categorization has been actively pursued, given its numerous applications in image and video search, surveillance, and assistive navigation. Many visual descriptors have been developed to address the challenges in scene categorization, including the large number of semantic categories and the tremendous variations caused by imaging conditions. This paper provides a critical review of visual descriptors used for scene categorization, from both methodological and experimental perspectives. We present an empirical study conducted on four benchmark data sets assessing the classification accuracy and class separability of state-of-the-art visual descriptors.


title: Feature Map Quality Score Estimation Through Regression

abstract: Understanding the visual quality of a feature map plays a significant role in many active vision applications. Previous works mostly rely on object-level features, such as compactness, to estimate the quality score of a feature map. However, the compactness is leveraged on feature maps produced by salient object detection techniques where the maps tend to be compact. As a result, the compactness feature fails when the feature maps are blurry (e.g., fixation maps). In this paper, we regard the process of estimating the quality score of feature maps, specifically fixation maps, as a regression problem. After extracting several local, global, geometric, and positional characteristic features from a feature map, a model is learned using a random forest regressor to estimate the quality score of any unseen feature map. Our model is specifically tailored to estimate the quality of three types of maps: bottom-up, target, and contextual feature maps. These maps are produced for a large benchmark fixation data set of more than 900 challenging outdoor images. We demonstrate that our approach provides an accurate estimate of the quality of the abovementioned feature maps compared to the groundtruth data. In addition, we show that our proposed approach is useful in feature map integration for predicting human fixation. Instead of naively integrating all three feature maps when predicting human fixation, our proposed approach dynamically selects the best feature map with the highest estimated quality score on an individual image basis, thereby improving the fixation prediction accuracy.


title: jFAST: a java finite automata simulator

abstract: Visualization and interactivity are valuable active learning techniques that can improve mastery of difficult concepts. In this paper we describe jFAST, an easy-to-use graphical software tool for teachers and students, with an emphasis on introductory level finite state machine topics. The jFAST software enables visual design, exploration and simulation of a variety of finite state machines, with a primary goal of enhancing teaching effectiveness in this subject, particularly for less advanced computer science students. The architecture and functionality of jFAST are explained, and results of preliminary evaluation are provided.


title: Popcorn: the personal knowledge base

abstract: People often use powerful tools to manage the documents they encounter, but very rarely to store the mental knowledge they glean from those documents. Popcorn is a personal knowledge base: an experimental interface and database designed to store and retrieve a user's accumulated personal knowledge. It aims to let the user represent information in a way that corresponds more naturally to their mental conceptions than simply text would, in part by making heavy use of transclusion: sharing items among multiple contexts. This paper describes the design rationale for the system, contrasting it with related efforts, and presents the results of deploying it to a group of volunteers who used it in real-world settings. The results, while revealing some limitations in the tool, and some challenges in coping with knowledge reorganization, suggest that the analysis underlying the design is useful, and that Popcorn is a powerful and effective tool for a variety of intellectual work.


title: The PS-Battles Dataset - an Image Collection for Image Manipulation Detection

abstract: The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PS-Battles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.


title: Data, Design and Civics: An Exploratory Study of Civic Tech

abstract: Civic technology, or civic tech, encompasses a rich body of work, inside and outside HCI, around how we shape technology for, and in turn how technology shapes, how we govern, organize, serve, and identify matters of concern for communities. This study builds on previous work by investigating how civic leaders in a large US city conceptualize civic tech, in particular, how they approach the intersection of data, design and civics. We encountered a range of overlapping voices, from providers, to connectors, to volunteers of civic services and resources. Through this account, we identified different conceptions and expectation of data, design and civics, as well as several shared issues around pressing problems and strategic aspirations. Reflecting on this set of issues produced guiding questions, in particular about the current and possible roles for design, to advance civic tech.


title: The pleasure and displeasure people feel when they exercise at different intensities: decennial update and progress towards a tripartite rationale for exercise intensity prescription.

abstract: The public health problem of physical inactivity has proven resistant to research efforts aimed at elucidating its causes and interventions designed to alter its course. Thus, in most industrialized countries, the majority of the population is physically inactive or inadequately active. Most theoretical models of exercise behaviour assume that the decision to engage in exercise is based on cognitive factors (e.g. weighing pros and cons, appraising personal capabilities, evaluating sources of support). Another, still-under-appreciated, possibility is that these decisions are influenced by affective variables, such as whether previous exercise experiences were associated with pleasure or displeasure. This review examines 33 articles published from 1999 to 2009 on the relationship between exercise intensity and affective responses. Unlike 31 studies that were published until 1998 and were examined in a 1999 review, these more recent studies have provided evidence of a relation between the intensity of exercise and affective responses. Pleasure is reduced mainly above the ventilatory or lactate threshold or the onset of blood lactate accumulation. There are pleasant changes at sub-threshold intensities for most individuals, large inter-individual variability close to the ventilatory or lactate threshold and homogeneously negative changes at supra-threshold intensities. When the intensity is self-selected, rather than imposed, it appears to foster greater tolerance to higher intensity levels. The evidence of a dose-response relation between exercise intensity and affect sets the stage for a reconsideration of the rationale behind current guidelines for exercise intensity prescription. Besides effectiveness and safety, it is becoming increasingly clear that the guidelines should take into account whether a certain level of exercise intensity would be likely to cause increases or decreases in pleasure.


title: Business success, Angola-style : postcolonial politics and the rise and rise of Sonangol

abstract: This paper investigates a paradoxical case of business success in one of the world's worst-governed states, Angola. Founded in 1976 as the essential tool of the Angolan end of the oil business, Sonangol, the national oil company, was from the very start protected from the dominant (both predatory and centrally planned) logic of Angola's political economy. Throughout its first years, the pragmatic senior management of Sonangol accumulated technical and managerial experience, often in partnership with Western oil and consulting firms. By the time the ruling party dropped Marxism in the early 199os, Sonangol was the key domestic actor in the economy, an island of competence thriving in tandem with the implosion of most other Angolan state institutions. However, the growing sophistication of Sonangol (now employing thousands of people, active in four continents, and controlling a vast parallel budget of offshore accounts and myriad assets) has not led to the benign developmental outcomes one would expect from the successful 'capacity building' of the last thirty years. Instead, Sonangol has primarily been at the service of the presidency and its rentier ambitions. Amongst other themes, the paper seeks to highlight the extent to which a nominal 'failed state' can be successful amidst widespread human destitution, provided that basic tools for elite empowerment (in this case, Sonangol and the means of coercion) exist to ensure the viability of incumbents.


title: Sediment quality criteria in use around the world

abstract: There have been numerous sediment quality guidelines (SQGs) developed during the past 20 years to assist regulators in dealing with contaminated sediments. Unfortunately, most of these have been developed in North America. Traditionally, sediment contamination was determined by assessing the bulk chemical concentrations of individual compounds and often comparing them with background or reference values. Since the 1980s, SQGs have attempted to incorporate biological effects in their derivation approach. These approaches can be categorized as empirical, frequency-based approaches to establish the relationship between sediment contamination and toxic response, and theoretically based approaches that attempt to account for differences in bioavailability through equilibrium partitioning (EqP) (i.e., using organic carbon or acid volatile sulfides). Some of these guidelines have been adopted by various regulatory agencies in several countries and are being used as cleanup goals in remediation activities and to identify priority polluted sites. The original SQGs, which compared bulk chemical concentrations to a reference or to background, provided little insight into the ecosystem impact of sediment contaminants. Therefore, SQGs for individual chemicals were developed that relied on field sediment chemistry paired with field or laboratory-based biological effects data. Although some SQGs have been found to be relatively good predictors of significant site contamination, they also have several limitations. False positive and false negative predictions are frequently in the 20% to 30% range for many chemicals and higher for others. The guidelines are chemical specific and do not establish causality where chemical mixtures occur. Equilibrium-based guidelines do not consider sediment ingestion as an exposure route. The guidelines do not consider spatial and temporal variability, and they may not apply in dynamic or larger-grained sediments. Finally, sediment chemistry and bioavailability are easily altered by sampling and subsequent manipulation processes, and therefore, measured SQGs may not reflect in situ conditions. All the assessment tools provide useful information, but some (such as SQGs, laboratory toxicity and bioaccumulation, and benthic indices) are prone to misinterpretation without the availability of specific in situ exposure and effects data. SQGs should be used only in a “screening” manner or in a “weight-of-evidence” approach. Aquatic ecosystems (including sediments) must be assessed in a “holistic” manner in which multiple components are assessed (e.g., habitat, hydrodynamics, resident biota, toxicity, and physicochemistry, including SQGs) by using integrated approaches.


title: Challenges to digital forensics: A survey of researchers & practitioners attitudes and opinions

abstract: Digital forensics have become increasingly important as an approach to investigate cyber- and computer-assisted crime. Whilst many tools exist and much research is being undertaken, many questions exist regarding the future of the domain. Indeed, prior literature has widely published the challenges that exist within the domain, from the increasing volume of data (e.g. SANs, hard drive capacities, databases) to the varying technology platforms and systems that exist (e.g. tablets, mobile phones, embedded systems, cloud computing). However, little effort has focused upon understanding the reality of these challenges. The paper presents research that seeks to identify, quantify and prioritise these challenges so that future efforts can be concentrated on the issues that actually affect the domain. The study undertook a survey of researchers and practitioners (both law enforcement and organisational) to examine the real-challenges from the perceived challenges and to understand what effect the future will have upon the digital forensic domain. A total of 42 participants undertook the study with 55% having 3 or more years of of experience. 45% were academic researchers, 16% law enforcement and 31% had a forensic role within an organisation. Overwhelmingly, 93% of participants felt that the number and complexity of investigations would increase in the future. Apart from the plethora of findings elaborated in the paper, the principal future challenge priorities included cloud computing, anti-forensics and encryption. Respondents also identified, improving communication between researchers and practitioners and the need to develop approaches to identify and extract “significant data” through techniques such as criminal profiling as essential. Interestingly, participants did not feel that the growth in privacy enhancing technologies nor legislation was a significant inhibitor to the future of digital forensics.


Neuropsychological Interpretation of Objective Psychological Tests


title: 1-Bit Matrix Completion

abstract: In this paper we develop a theory of matrix completion for the extreme case of noisy 1-bit observations. Instead of observing a subset of the real-valued entries of a matrix M, we obtain a small number of binary (1-bit) measurements generated according to a probability distribution determined by the real-valued entries of M. The central question we ask is whether or not it is possible to obtain an accurate estimate of M from this data. In general this would seem impossible, but we show that the maximum likelihood estimate under a suitable constraint returns an accurate estimate of M when ||M||_{\infty}<= \alpha, and rank(M)<= r. If the log-likelihood is a concave function (e.g., the logistic or probit observation models), then we can obtain this maximum likelihood estimate by optimizing a convex program. In addition, we also show that if instead of recovering M we simply wish to obtain an estimate of the distribution generating the 1-bit measurements, then we can eliminate the requirement that ||M||_{\infty}<= \alpha. For both cases, we provide lower bounds showing that these estimates are near-optimal. We conclude with a suite of experiments that both verify the implications of our theorems as well as illustrate some of the practical applications of 1-bit matrix completion. In particular, we compare our program to standard matrix completion methods on movie rating data in which users submit ratings from 1 to 5. In order to use our program, we quantize this data to a single bit, but we allow the standard matrix completion program to have access to the original ratings (from 1 to 5). Surprisingly, the approach based on binary data performs significantly better.


title: Collective classification for packed executable identification

abstract: Malware is any software designed to harm computers. Commercial anti-virus are based on signature scanning, which is a technique effective only when the malicious executables have been previously analysed and identified. Malware writers employ several techniques in order to hide their actual behaviour. Executable packing consists in encrypting or hiding the real payload of the executable. Generic unpacking techniques do not depend on the packer used, as they execute the binary within an isolated environment (namely `sandbox') to gather the real code of the packed executable. However, this approach is slow and, therefore, a filter step is required to determine when an executable has been packed. To this end, supervised machine learning approaches trained with static features from the executables have been proposed. Notwithstanding, supervised learning methods need the identification and labelling of a high number of packed and not packed executables. In this paper, we propose a new method for packed executable detection that adopts a collective learning approach to reduce the labelling requirements of completely supervised approaches. We performed an empirical validation demonstrating that the system maintains a high accuracy rate while the labelling efforts are lower than when using supervised learning.


title: Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

abstract: Contextual information plays an important role in solving vision problems such as image segmentation. However, extracting contextual information and using it in an effective way remains a difficult problem. To address this challenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for image segmentation. At each level of the hierarchy, a classifier is trained based on down sampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM, therefore, a fast and accurate classifier is required to make the training tractable. The classifier also needs to be robust against over fitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic disjunctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logistic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respectively. We demonstrate that LDNN outperforms state-of-the-art classifiers and can be used in the CHM to improve object segmentation performance.


The Morphological Approach to Segmentation: The Watershed Transformation


title: Unsupervised Learning by Predicting Noise

abstract: Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and Pascal VOC.


title: Understanding Mixup Training Methods

abstract: Mixup is a neural network training method that generates new samples by linear interpolation of multiple samples and their labels. The mixup training method has better generalization ability than the traditional empirical risk minimization method (ERM). But there is a lack of a more intuitive understanding of why mixup will perform better. In this paper, several different sample mixing methods are used to test how neural networks learn and infer from mixed samples to illustrate how mixups work as a data augmentation method and how it regularizes neural networks. Then, a method of weighting noise perturbation was designed to visualize the loss functions of mixup and ERM training methods to analyze the properties of their high-dimensional decision surfaces. Finally, by analyzing the mixture of samples and their labels, a spatial mixup approach was proposed that achieved the state-of-the-art performance on the CIFAR and ImageNet data sets. This method also enables the generative adversarial nets to have more stable training process and more diverse sample generation ability.


title: Co-clustering by block value decomposition

abstract: Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data analysis is to find the hidden block structure of the data matrix. In this paper, we present a new co-clustering framework, block value decomposition(BVD), for dyadic data, which factorizes the dyadic data matrix into three components, the row-coefficient matrix R, the block value matrix B, and the column-coefficient matrix C. Under this framework, we focus on a special yet very popular case -- non-negative dyadic data, and propose a specific novel co-clustering algorithm that iteratively computes the three decomposition matrices based on the multiplicative updating rules. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the specific algorithms for co-clustering, and in particular, for discovering the hidden block structure in the dyadic data.


title: Parallel Edge Splatting for Scalable Dynamic Graph Visualization

abstract: We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-byside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words.


title: N.E.: A comparative evaluation of interactive segmentation algorithms

abstract: In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2min for each object. To facilitate the experiments, a ''scribble-driven'' segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms.


title: Social network sites: definition, history, and scholarship

abstract: Social network sites (SNSs) are increasingly attracting the attention of academic and industry researchers intrigued by their affordances and reach. This special theme section of the Journal of Computer-Mediated Communication brings together scholarship on these emergent phenomena. In this introductory article, we describe features of SNSs and propose a comprehensive definition. We then present one perspective on the history of such sites, discussing key changes and developments. After briefly summarizing existing scholarship concerning SNSs, we discuss the articles in this special section and conclude with considerations for future research.


title: Efficient Program Tracing

abstract: A program trace lists the addresses of instructions executed and data referenced during a program's execution. Earlier approaches to collecting program traces, including abstract execution and optimal control tracing, are reviewed. Two tracing systems based on these techniques are presented. Results collected when using the later systems on several programs show significant reductions in the cost of collecting traces. Reduction in trace file sizes are also significant. >


title: Conjoined twins — the Cape Town experience

abstract: This paper records our experience in the management of 25 sets of conjoined twins seen over a 32 year period (1964 - 1996). The twins were classified into 14 complete and symmetrical sets and 11 incomplete or heteropagus. The 14 symmetrical sets included 9 thoracopagus, 2 ischiopagus, 1 craniopagus and 1 omphalopagus twins. In the incomplete heteropagus group there was 1 ischiopagus, one twin being anencepahlic, 2 dipygus, 5 parasitic, 2 fetus-in-fetu and I cranial and caudal. The management is detailed case by case. Overall 10 of 14 symmetrical sets underwent attempts at separation with 16 surviving the procedure, but there were 3 late deaths. In the incomplete group 10 of 11 were operated on with 9 survivors. The importance of a multi-disciplinary approach, the extensive investigations required pre-operatively to define areas of organ and bony conjunction, congenital anomalies of each twin and surgical teamwork is emphasized. Specific problems encountered were identified. In thoracopagus twins the hearts were of paramount importance as conjuction was usually fatal, being associated with major congenital defects. The greater the extent of thoracic cage fusion the greater the chance of associated severe anomaly. Skin expansion to assist coverage of the defects after separation was of great assistance, as was the use of collagen coated vicryl. Evaluation of the liver and pancreatico-biliary systems with isotope excretion scanning was crucial to pre-operative planning. Where there was fusion of the duodenum a single pancreatico-biliary system could be expected and prior strategies for separation and Roux-en-Y enteric drainage of both pancreatic and biliary secretion should be planned. Gastro-oesophageal reflux led to considerable morbidity in both twins of a thoraco-omphalopagus set. In ischiopagus and dipygus conjoined twins bilateral posterior iliac osteotomies were an essential component to anatomic reconstruction of the pelvic ring and wound closure. Also in this group, due to the frequency and extent of shared genital, urinary and ano-rectal structures, long-term morbidity was expected and a component of this might be due to spinal cord tethering, or as in one of our cases, a progressive hydrosyringomyelia. Timing of separation was ideally set at between 5 and 9 months with 6 to 8 weeks of prior tissue expansion but earlier operation was frequently required because of cardio-respiratory problems or organ failure in one twin. In most cases the goal of obtaining separate, independent and intact individuals was achieved.


Deep Models for Arabic Dialect Identification on Benchmarked Data.


title: Towards Generalization and Simplicity in Continuous Control

abstract: This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks. The performance of these trained policies are competitive with state of the art results, obtained with more elaborate parameterizations such as fully connected neural networks. Furthermore, existing training and testing scenarios are shown to be very limited and prone to over-fitting, thus giving rise to only trajectory-centric policies. Training with a diverse initial state distribution is shown to produce more global policies with better generalization. This allows for interactive control scenarios where the system recovers from large on-line perturbations; as shown in the supplementary video.


title: An updated meta-analysis of the ego depletion effect

abstract: The ego depletion effect is one of the most famous phenomena in social psychology. A recent meta-analysis showed that after accounting for small-studies effects by using a newly developed method called PET-PEESE, the ego depletion effect was indistinguishable from zero. However, it is too early to draw such rushing conclusion because of the inappropriate usage of PET-PEESE. The current paper reported a stricter and updated meta-analysis of ego depletion by carefully inspecting problems in the previous meta-analysis, including new studies not covered by it, and testing the effectiveness of each depleting task. The results suggest that attention video should be an ineffective depleting task, whereas emotion video should be the most effective one. Future studies are needed to confirm the effectiveness of each depletion task revealed by the current meta-analysis.


iPCA: An Interactive System for PCA-based Visual Analytics


title: Design of a High-Efficiency 40-kV, 150-A, 3-kHz Solid-State Pulsed Power Modulator

abstract: This paper deals with the detailed design of a pulsed power modulator using insulated gate bipolar transistor (IGBT) switches for industrial applications. Output specifications of the proposed modulator are as follows: variable output pulse voltage 1~40 kV; pulse width 0.5~5 μs ; maximum pulse repetition rates 3 kHz, and average output power of 13 kW. The proposed pulsed power modulator consists of a high-voltage capacitor charger based on a high-efficiency resonant inverter and pulse generator part including a series of connected 24 pieces power cells. To verify the proposed design, PSpice modeling was performed. Finally, experimental results proved the reliability and robustness of the proposed solid-state pulsed power modulator.


title: The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

abstract: This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.


title: Textline detection in degraded historical document images

abstract: This paper presents a textline detection method for degraded historical documents. Our method follows a conventional two-step procedure that the binarization is first performed and then the textlines are extracted from the binary image. In order to address the challenges in historical documents such as document degradation, structure noise, and skews, we develop new methods for the binarization and textline extraction. First, we improve the performance of binarization by detecting the non-text regions and processing only text regions. We also improve the textline detection method by extracting main textblock and compensating the skew angle and writing style. Experimental results show that the proposed method yields the state-of-the-art performance for several datasets.


title: Evaluation of the Problem Behavior Frequency Scale–Teacher Report Form for Assessing Behavior in a Sample of Urban Adolescents

abstract: This study evaluated the structure and validity of the Problem Behavior Frequency Scale-Teacher Report Form (PBFS-TR) for assessing students' frequency of specific forms of aggression and victimization, and positive behavior. Analyses were conducted on two waves of data from 727 students from two urban middle schools (Sample 1) who were rated by their teachers on the PBFS-TR and the Social Skills Improvement System (SSIS), and on data collected from 1,740 students from three urban middle schools (Sample 2) for whom data on both the teacher and student report version of the PBFS were obtained. Confirmatory factor analyses supported first-order factors representing 3 forms of aggression (physical, verbal, and relational), 3 forms of victimization (physical, verbal and relational), and 2 forms of positive behavior (prosocial behavior and effective nonviolent behavior), and higher-order factors representing aggression, victimization, and positive behavior. Strong measurement invariance was established over gender, grade, intervention condition, and time. Support for convergent validity was found based on correlations between corresponding scales on the PBFS-TR and teacher ratings on the SSIS in Sample 1. Significant correlations were also found between teacher ratings on the PBFS-TR and student ratings of their behavior on the Problem Behavior Frequency Scale-Adolescent Report (PBFS-AR) and a measure of nonviolent behavioral intentions in Sample 2. Overall the findings provided support for the PBFS-TR and suggested that teachers can provide useful data on students' aggressive and prosocial behavior and victimization experiences within the school setting. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


title: The Influence of Ergonomic Factors and Perceptual-Motor Abilities on Handwriting Performance

abstract: DlfficullY with handwriting is one of the mosl frequent reasons that children in the public schools are re ferred to occupational Iherapy Current research on the injluence of er;gonomic faclors, such as pencil grip and pressure, and perceptual-motorfactors tradition al~v believed to affect handwriting, is reviewed Fac tors such as visual perception show little relationship to handwriting, whereas tactiLe-kinestbetic, visual motor, and motor planning appear to be more cLoselv related to handwriting By better understanding the ergonomic and'perceptuctL-motorfactors that contrib ute to and injluence handwriting, therapists will be better able to design rationallv based intervention programs,


title: Laryngospasm from the anesthesiologist's viewpoint.

abstract: Abstract ::: Laryngeal spasm is a problem constantly confronting the anesthetist. It can be serious and may produce fatal cerebral or cardiac complications. Etiologic agents include primary vagal hypertonicity, anoxemia, and painful stimulation of whatever source. ::: ::: Laryngeal spasm must be differentiated from simple obstruction by the tongue or foreign bodies, epiglottic impaction, laryngeal edema, tracheal spasm and collapse, and bronchial spasm. ::: ::: Proper checking of the patient before anesthesia and adequate premedication with atropine or scopolamine are preventive measures of great value. Once spasm has developed the etiologic agent should be removed if possible. Other measures include intravenous administration of atropine or curare, tracheal intubation, and tracheotomy.


Solar cells on curtains


title: Wide-Beam SIW-Slot Antenna for Wide-Angle Scanning Phased Array

abstract: In order to enhance the scanning range of planar phased arrays, a planar substrate integrated waveguide slot (SIWslot) antenna with wide beamwidth is proposed in this letter. The proposed antenna is fabricated on a single-layer substrate, which is fully covered with a metal ground on the back. The SIW works like a dielectric-filled rectangular waveguide working in the TE 10 mode. There are four inclined slots etched on the top metal layer following the rules of rectangular waveguide slot antenna. The electric fields in the slots work as equivalent magnetic currents. As opposed to normal microstrip antennas, the equivalent magnetic currents from the slots over a larger metal ground can radiate with a wide beamwidth. Its operating bandwidth is from 5.4 to 6.45 GHz with a relative bandwidth of 17.7%. Meanwhile, the 3-dB beamwidth in the xz-plane is between 130° and 148° in the whole operating band. Furthermore, the SIW-slot element is employed in a 1 × 8 planar phased array. The measured results show that the main lobe of phased array can obtain a wide-angle scanning from -71° to 73° in the whole operating band.


title: Are all objects equal? Deep spatio-temporal importance prediction in driving videos

abstract: Understanding intent and relevance of surrounding agents from video is an essential task for many applications in robotics and computer vision. The modeling and evaluation of contextual, spatio-temporal situation awareness is particularly important in the domain of intelligent vehicles, where a robot is required to smoothly navigate in a complex environment while also interacting with humans. In this paper, we address these issues by studying the task of on-road object importance ranking from video. First, human-centric object importance annotations are employed in order to analyze the relevance of a variety of multi-modal cues for the importance prediction task. A deep convolutional neural network model is used for capturing video-based contextual spatial and temporal cues of scene type, driving task, and object properties related to intent. Second, the proposed importance annotations are used for producing novel analysis of error types in image-based object detectors. Specifically, we demonstrate how cost-sensitive training, informed by the object importance annotations, results in improved detection performance on objects of higher importance. This insight is essential for an application where navigation mistakes are safety-critical, and the quality of automation and human-robot interaction is key. HighlightsWe study a notion of object relevance, as measured in a spatio-temporal context of driving a vehicle.Various spatio-temporal object and scene cues are analyzed for the task of object importance classification.Human-centric metrics are employed for evaluating object detection and studying data bias.Importance-guided training of object detectors is proposed, showing significant improvement over an importance-agnostic baseline.


title: Maintaining Integrity: How Nurses Navigate Boundaries in Pediatric Palliative Care

abstract: PURPOSE ::: To explore how nurses manage personal and professional boundaries in caring for seriously ill children and their families. ::: ::: ::: DESIGN AND METHODS ::: Using a constructivist grounded theory approach, a convenience sample of 18 registered nurses from four practice sites was interviewed using a semi-structured interview guide. ::: ::: ::: RESULTS ::: Nurses across the sites engaged in a process of maintaining integrity whereby they integrated two competing, yet essential, aspects of their nursing role - behaving professionally and connecting personally. When skillful in both aspects, nurses were satisfied that they provided high-quality, family-centered care to children and families within a clearly defined therapeutic relationship. At times, tension existed between these two aspects and nurses attempted to mitigate the tension. Unsuccessful mitigation attempts led to compromised integrity characterized by specific behavioral and emotional indicators. Successfully mitigating the tension with strategies that prioritized their own needs and healing, nurses eventually restored integrity. Maintaining integrity involved a continuous effort to preserve completeness of both oneself and one's nursing practice. ::: ::: ::: CONCLUSIONS ::: Study findings provide a theoretical conceptualization to describe the process nurses use in navigating boundaries and contribute to an understanding for how this specialized area of care impacts health care providers. ::: ::: ::: PRACTICE IMPLICATIONS ::: Work environments can better address the challenges of navigating boundaries through offering resources and support for nurses' emotional responses to caring for seriously ill children. Future research can further refine and expand the theoretical conceptualization of maintaining integrity presented in this paper and its potential applicability to other nursing specialties.


title: Service Quality (Servqual) and its Effect on Customer Satisfaction in Retailing

abstract: Service Quality within retail units is pivotal for satisfying customers, retaining them and creating loyalty amongst customers. This research uses SERVQUAL to analyze the gap between perceptions and expectations of the customer, concerning with the service at retail units in the South Indian state of Andhra Pradesh. Customer Satisfaction level is assessed for the services offered at select retail units in the city of Hyderabad. Five dimensions in service quality (servqual), tangibility, reliability, responsiveness, empathy, and assurance (Parasuraman, Zeithaml, &Berry, 1985) have been considered for this empirical research. General purpose of this research to know some factors that impact customer satisfaction. The purpose are (1) to describe applied of service quality (servqual) dimension in retail Business (2) to know service quality (servqual) dimensions that make customers satisfied, and (3) to know service quality (servqual) dimensions that are dominant in influencing customer satisfaction. The research methodology was carried out in a survey crosssectional applied to 369 respondents. The data obtained was analyzed by using reliability method, correlation and regression. Result of research showed that services offered by retail units have positive impact and are significant in building customer satisfaction. Findings of this empirical research reiterate the point of view that Service Quality dimensions are crucial for customer satisfaction in retailing – a burgeoning sector with high growth potential and opportunities in fast growing economies like India’s.


title: A comparative study between LSTM and ARIMA for sales forecasting in retail

abstract: Food waste is a major environmental issue. Expired products are thrown away, implying that too much food is ordered compared to what is sold and that a more accurate prediction model is required wi ...


title: The UMass mobile manipulator uMan: An experimental platform for autonomous mobile manipulation

abstract: Research in Autonomous Mobile Manipulation critically depends on the availability of adequate experimental platforms. In this paper, we describe an ongoing effort at the University of Massachusetts Amherst to construct a hardware platform with redundant kinematic degrees of freedom, a comprehensive sensor suite, and significant end-effector capabilities for manipulation. In our research, we pursue an end-effector centric view of autonomous mobile manipulation. In support of this view, we are developing a comprehensive software suite to provide a high level of competency in robot control and perception. This software suite is based on a multi-objective, tasklevel motion control framework. We use this control framework to integrate a variety of motion capabilities, including taskbased force or position control of the end-effector, collision-free global motion for the entire mobile manipulator, and mapping and navigation for the mobile base. We also discuss our efforts in developing perception capabilities targeted to problems in autonomous mobile manipulation. Preliminary experiments on our UMass Mobile Manipulator (UMan) are presented.


title: Shader Lamps : Animating Real Objects With Image-Based Illumination

abstract: We describe a new paradigm for three-dimensional computer graphics, using projectors to graphically animate physical objects in the real world. The idea is to replace a physical object— with its inherent color, texture, and material properties—with a neutral object and projected imagery, reproducing the original (or alternative) appearance directly on the object. Because the approach is to effectively "lift" the visual properties of the object into the projector, we call the projectors shader lamps. We address the central issue of complete and continuous illumination of non-trivial physical objects using multiple projectors and present a set of new techniques that makes the process of illumination practical. We demonstrate the viability of these techniques through a variety of table-top applications, and describe preliminary results to reproduce life-sized virtual spaces.


title: Budget pacing for targeted online advertisements at LinkedIn

abstract: Targeted online advertising is a prime source of revenue for many Internet companies. It is a common industry practice to use a generalized second price auction mechanism to rank advertisements at every opportunity of an impression. This greedy algorithm is suboptimal for both advertisers and publishers when advertisers have a finite budget. In a greedy mechanism high performing advertisers tend to drop out of the auction marketplace fast and that adversely affects both the advertiser experience and the publisher revenue. We describe a method for improving such ad serving systems by including a budget pacing component that serves ads by being aware of global supply patterns. Such a system is beneficial for both advertisers and publishers. We demonstrate the benefits of this component using experiments we conducted on advertising at LinkedIn.


title: Comparing the performance of OS-level virtualization tools in SoC-based systems: The case of I/O-bound applications

abstract: Organizations need process data in real time. Cloud Computing is a common choice but it presents high latency on data transfer. As an alternative to reduces the latency, Edge computing based on System-on-a-Chip systems (SoC-based systems) can retain part of the data processing, allowing resource sharing among multiple requests through the use of OS-level virtualization. In this work, we analyze the performance of two popular OS-level virtualization solutions - Linux Containers (LXC) and Docker - for I/O-bound activities running in SoC systems, in order to understand how resource sharing affects the processing performance.


title: Foundations of Homomorphic Secret Sharing

abstract: Homomorphic secret sharing (HSS) is the secret sharing analogue of homomorphic encryption. An HSS scheme supports a local evaluation of functions on shares of one or more secret inputs, such that the resulting shares of the output are short. Some applications require the stronger notion of additive HSS, where the shares of the output add up to the output over some finite Abelian group. While some strong positive results for HSS are known under specific cryptographic assumptions, many natural questions remain open. ::: ::: We initiate a systematic study of HSS, making the following contributions. ::: ::: - A definitional framework. We present a general framework for defining HSS schemes that unifies and extends several previous notions from the literature, and cast known results within this framework. ::: - Limitations. We establish limitations on information-theoretic multi-input HSS with short output shares via a relation with communication complexity. We also show that additive HSS for non-trivial functions, even the AND of two input bits, implies non-interactive key exchange, and is therefore unlikely to be implied by public-key encryption or even oblivious transfer. ::: - Applications. We present two types of applications of HSS. First, we construct 2-round protocols for secure multiparty computation from a simple constant-size instance of HSS. As a corollary, we obtain 2-round protocols with attractive asymptotic efficiency features under the Decision Diffie Hellman (DDH) assumption. Second, we use HSS to obtain nearly optimal worst-case to average-case reductions in P. This in turn has applications to fine-grained average-case hardness and verifiable computation.


title: Spaceflight induced changes in the human proteome.

abstract: ABSTRACTIntroduction: Spaceflight is one of the most extreme conditions encountered by humans: Individuals are exposed to radiation, microgravity, hypodynamia, and will experience isolation. A better understanding of the molecular processes induced by these factors may allow us to develop personalized countermeasures to minimize risks to astronauts.Areas covered: This review is a summary of literature searches from PubMed, NASA, Roskosmos and the authors’ research experiences and opinions. The review covers the available proteomic data on the effects of spaceflight factors on the human body, including both real space missions and ground-based model experiments.Expert commentary: Overall, the authors believe that the present background, methodology and equipment improvements will enhance spaceflight safety and support accumulation of new knowledge on how organisms adapt to extreme conditions.


title: Convolutional Geometric Matrix Completion

abstract: Geometric matrix completion~(GMC) has been proposed for recommendation by integrating the relationship~(link) graphs among users/items into matrix completion~(MC) . Traditional \mbox{GMC} methods typically adopt graph regularization to impose smoothness priors for \mbox{MC}. Recently, geometric deep learning on graphs~(\mbox{GDLG}) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods. To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN}. RMGCNN combines graph convolutional network~(GCN) and recurrent neural network~(RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method. In this paper, we propose a new \mbox{GMC} method, called \underline{c}onvolutional \underline{g}eometric \underline{m}atrix \underline{c}ompletion~(CGMC), for recommendation with graphs among users/items. CGMC is a pure GCN-based method with a newly designed graph convolutional network. Experimental results on real datasets show that CGMC can outperform other state-of-the-art methods including RMGCNN.


title: Mobile business models: organizational and financial design issues that matter

abstract: Designing viable mobile services and business models that capture value for all the organizations involved is a challenge. There are many design issues that can be taken into account, and it is often unclear what their ultimate effect is on the performance of a business model. This paper offers a framework for relating critical design issues to success factors and tests the causal relationship between these core concepts in the organizational and financial domain of mobile business models, based on an international survey among 120 practitioners and experts in the mobile Internet services domain, most of them from EU countries. According to our findings, addressing organizational design issues (i.e. partner selection, governance and relation management) leads to an acceptable division of roles among actors, while addressing financial design issues (i.e., pricing, division of investments and costs among partners) results in risk levels that are perceived to be acceptable. The level of profitability that is perceived to be acceptable is influenced indirectly by these design issues, because the relationships are mediated through the risk level that the actors involved perceive to be acceptable and through the way the roles are divided among the actors.


title: Unbalanced neuronal circuits in addiction

abstract: Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it.


title: A Performance Isolation Analysis of Disk-Intensive Workloads on Container-Based Clouds

abstract: The popularity of Cloud computing due to the increasing number of customers has led Cloud providers to adopt resource-sharing solutions to meet growing demand for infrastructure resources. As the adoption of resource-sharing/consolidation in Cloud computing became arguably a well-established solution, the ability the underlying virtualization systems of preventing performance interferences from customers must also be understood. Virtualization systems based on containers, such as LXC, are the basis of the next-generation of Cloud computing and have become the most popular solution under PaaS/IaaS Cloud platforms with the rise of Docker -- an open platform for developers and sysadmins to build, ship, and run distributed applications. Such platforms have enticed many attentions globally, since they leverage container-based virtualization systems to offer high scalability while low performance overheads, the performance might be solely aggravated if the customers' workloads are consolidated onto the same hardware and the isolation layer does not properly isolate the shared resources. Performance isolation is an inherent concern of such systems due to the nature as they are conceived and is still an unexplored and open research topic, the consequences might influence in the adoption under shared Cloud computing platforms where Quality-of-Service is a crucial factor that cannot be disregarded. In this paper we analyze the performance interference suffered by disk-intensive workloads within very noisy-perturbed containers (different hardware components stressed). Our results show workload combinations whose performance degradation goes up to 38%, but in contrast we expose a workload-balanced scenario wherein the performance does not suffer any interference.


title: Nanomechanics of vascular endothelium

abstract: The mechanical characteristics of endothelial cells reveal four distinct compartments, namely glycocalyx, cell cortex, cytoplasm and nucleus. There is accumulating evidence that endothelial nanomechanics of these individual compartments control vascular physiology. Depending on protein composition, filament formation and interaction with cross-linker proteins, these four compartments determine endothelial stiffness. Structural organization and mechanical properties directly influence physiological processes such as endothelial barrier function, nitric oxide release and gene expression. This review will focus on endothelial nanomechanics and its impact on vascular function.


title: Sequential anomaly detection based on temporal-difference learning: Principles, models and case studies

abstract: Anomaly detection is an important problem that has been popularly researched within diverse research areas and application domains. One of the open problems in anomaly detection is the modeling and prediction of complex sequential data, which consist of a series of temporally related behavior patterns. In this paper, a novel sequential anomaly detection method based on temporal-difference (TD) learning is proposed, where the anomaly detection problem of multi-stage cyber attacks is considered as an application case. A Markov reward process model is presented for the anomaly detection and alarming process of sequential data and it is verified that when the reward function is properly defined, the anomaly probabilities of sequential behaviors are equivalent to the value functions of the Markov reward process. Therefore, TD learning algorithms in the reinforcement learning literature can be used to efficiently construct anomaly detection models of complex sequential behaviors by estimating the value functions of the Markov reward process. Compared with other machine learning methods for anomaly detection, the proposed approach has the advantage of simplified labeling process using delayed evaluative signals and the prediction accuracy can be improved even if labeled training data are limited. Based on the experimental results on intrusion detection of host computers using system call data, it was shown that the proposed anomaly detection method can achieve higher or at least comparable detection accuracies than other approaches including SVMs, and HMMs.


title: ISCA Archive MODELING DRIVERS ' SPEECH UNDER STRESS

abstract: We explore the use of features derived from multiresolution analysis of speech and the Teager energy operator for classification of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create user-dependent discriminants of four stress categories. In addition we address the problem of choosing a suitable temporal scale for representing categorical differences in the data. This leads to two modeling approaches. In the first approach, the dynamics of the feature set within the utterance are assumed to be important for the classification task. These features are then classified using dynamic Bayesian network models as well as a model consisting of a mixture of hidden Markov models (M-HMM). In the second approach, we define an utterance-level feature set by taking the mean value of the features across the utterance. This feature set is then modeled with a support vector machine and a multilayer perceptron classifier. We compare the performance on the sparser and full dynamic representations against a chance-level performance of 25% and obtain the best performance with the speaker-dependent mixture model (96.4% on the training set, and 61.2% on a separate testing set). We also investigate how these models perform on the speaker-independent task. Although the performance of the speaker-independent models degrades with, respect to the models trained on individual speakers, the mixture model still outperforms the competing models and achieves significantly better than random recognition (80.4% on the training set, and 51.2% on a separate testing set).


title: Review of Canine Transmissible Venereal Sarcoma

abstract: Canine transmissible venereal sarcoma (CTVS) is usually a sexually transmitted neoplasm of the external genitalia of dogs. This tumour is unique in oncology because it was the ¢rst tumour to be transmitted experimentally, this being achieved by the Russian veterinarian Nowinsky in 1876. This stimulated a lively interest among scientists and became a new starting point for the study of oncology. CTVS has been studied and reported extensively since the early days of nineteenth century, but a recent, widely circulated, review of this condition has not been published. This review is designed to unify the epidemiological, pathological, immunological and therapeutic concepts related to CTVS.


title: TinyOS-New Trends, Comparative Views, and Supported Sensing Applications: A Review

abstract: The wireless sensor network (WSN) is an interesting area for modern day research groups. Tiny sensor nodes are deployed in a diversity of environments but with limited resources. Scarce resources compel researchers to employ an operating system that requires limited memory and minimum power. Tiny operating system (TinyOS) is a widely used operating system for sensor nodes, which provides concurrency and flexibility while adhering to the constraints of scarce resources. Comparatively, TinyOS is considered to be the most robust, innovative, energy-efficient, and widely used operating system in sensor networks. This paper looks at the state-of-the-art TinyOS and the different dimensions of its design paradigm, programming model, execution model, scheduling algorithms, concurrency, memory management, hardware support platforms, and other features. The addition of different features in TinyOS makes it the operating system of choice for WSNs. Sensing nodes with TinyOS seem to show more flexibility in supporting diverse types of sensing applications.


title: Benefits and barriers as a critical success factor in the implementation of IT governance: Literature review

abstract: This study aims to study the literature on the benefits and barriers as a critical success factor in the implementation of IT Governance in an organization or company. Methods used with several approaches to find some basic concepts related to benefits and constraints, as well as some concepts about IT Governance implementation. Through several steps with reference, PRISMA checklist method will be known matters relating to the benefits and barriers to the implementation of IT Governance in an organization or company. A total of 85 articles have been collected, found 6 is a duplicate article, so it must be removed from the list. 79 articles filtered with reference to title and abstract, found only 70 articles accordingly. From the last 70 articles re-examined to see the feasibility of each paper. The final result of the examination, 58 was used as the main study literature. Benefits and barriers in the implementation of IT Governance should be known and understood by all parties because it has an important role and influence in improving the performance of an organization so that business goals can be successful. The final result of this literature study found 7 elements related to the benefits and 7 elements associated with the barriers.


title: Unmasking the Abnormal Events in Video

abstract: We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.


title: Clinical measurement of craniovertebral angle by electronic head posture instrument: a test of reliability and validity.

abstract: The study was a cross-sectional reliability study with the objective of assessing the reliability and validity of the Electronic Head Posture Instrument (EHPI) in measuring the craniovertebral (CV) angle for subjects with or without neck pain. Twenty-six subjects (mean age=36.88, SD+/-9.95) with chronic neck pain and 27 subjects (mean age=31.85, SD+/-7.63) without neck pain were recruited. The CV angle was measured by the EHPI which consists of an electronic angle finder, a transparent plastic base and a camera stand. Two therapists were recruited to assess the intra- and inter-rater reliability of the EHPI in two separate sessions of measurement. The difference in CV angle between the two groups was determined. The CV angle of the patient group (mean 43.94, SD+/-3.61) was significantly smaller (p<0.001) than that of the normal group (mean 50.58, SD+/-2.09). Intra-rater (intra-class correlation coefficient (ICC) ranged from 0.86 to 0.94) and inter-rater (ICC ranged from 0.85 to 0.91) reliability of the EHPI in measuring CV angle for both groups of subjects were high. In conclusion the EHPI was found to be reliable and valid in measuring the CV angle for subjects with or without neck pain.


title: Time-Lock Puzzles from Randomized Encodings

abstract: Time-lock puzzles are a mechanism for sending messages "to the future". A sender can quickly generate a puzzle with a solution s that remains hidden until a moderately large amount of time t has elapsed. The solution s should be hidden from any adversary that runs in time significantly less than t, including resourceful parallel adversaries with polynomially many processors. While the notion of time-lock puzzles has been around for 22 years, there has only been a single candidate proposed. Fifteen years ago, Rivest, Shamir and Wagner suggested a beautiful candidate time-lock puzzle based on the assumption that exponentiation modulo an RSA integer is an "inherently sequential" computation. We show that various flavors of randomized encodings give rise to time-lock puzzles of varying strengths, whose security can be shown assuming the mere existence of non-parallelizing languages, which are languages that require circuits of depth at least t to decide, in the worst-case. The existence of such languages is necessary for the existence of time-lock puzzles. We instantiate the construction with different randomized encodings from the literature, where increasingly better efficiency is obtained based on increasingly stronger cryptographic assumptions, ranging from one-way functions to indistinguishability obfuscation. We also observe that time-lock puzzles imply one-way functions, and thus the reliance on some cryptographic assumption is necessary. Finally, generalizing the above, we construct other types of puzzles such as proofs of work from randomized encodings and a suitable worst-case hardness assumption (that is necessary for such puzzles to exist).


title: Anova for Unbalanced Data: An Overview

abstract: Ecological studies typically involve comparison of biological responses among a variety of environmental conditions. When the response variables have continuous distributions and the conditions are discrete, whether inherently or by design, then it is appropriate to analyze the data using analysis of variance (ANOVA). When data conform to a complete, balanced design (equal numbers of observations in each experimental treatment), it is straightforward to conduct an ANOVA, particularly with the aid of the numerous statistical computing packages that are available. Interpretation of an ANOVA of balanced data is also unambiguous. Unfortunately, for a variety of reasons, it is rare that a practicing ecologist embarks on an analysis of data that are completely balanced. Regardless of its cause, lack of balance necessitates care in the analysis and interpretation. In this paper, our aims is to provide an overview of the consequences of lack of balance and to give some guidelines to analyzing unbalanced data for models involving fixed effects. Our treatment is necessarily cursory and will not substitute for training available from a sequence of courses in mathematical statistics and linear models. It is intended to introduce the reader to the main issues and to the extensive statistical literature that deals with them.


title: An Empirical Evaluation Of Key Factors Contributing To Internet Abuse In The Workplace

abstract: Purpose – This study seeks to synthesize theories from communication, psychology and criminology to examine the factors that influence the two most popular topics in industry – internet abuse and addiction at the workplace.Design/methodology/approach – The survey results of 351 responses were analyzed to test the proposed hypotheses and research model using structural equation modeling. Data were collected in Southern Science Park in Taiwan.Findings – It was found that personality factors such as locus of control and self‐esteem significantly influence employees' internet addictions; and internet addiction significantly impacts employees' internet abuse at the workplace.Practical implications – Employers should pay special attention to employees' personalities because they play important roles in internet addiction and internet abuse. Also a good internet policy will be useful especially to a panoptic working environment, which is becoming popular.Originality/value – This study provides a comprehensive th...


title: A rationale for the use of high-powered magnification or microscopes in general dentistry.

abstract: This article argues that high-powered magnification (4x-6x or more) provides substantially more visual information for diagnosing and treating dental pathology compared to the use of unaided vision or entry-level 2.5x magnification. In all phases of general dentistry, the increased visual detail provided by high magnification reduces ambiguity in diagnosis and treatment decision-making, increases control in treatment implementation, allows a dentist to produce more ergonomic restorations that are less prone to recurrent decay, and arguably improves clinical outcomes compared to work performed with unaided vision. High magnification enhances a dentist's ability to diagnose caries and cracks in teeth, distinguish between different colors intraorally, detect the interfaces between different surfaces and materials, detect microscopic interferences in fixed and removable metal frameworks, adjust occlusal prematurities, and polish restorations. This article explains specific general dental applications for high-powered magnification in restorative dentistry, fixed and removable prosthodontics, endodontics, pediatric dentistry, periodontics, and oral surgery.


title: LOGAIDER: A Tool for Mining Potential Correlations of HPC Log Events

abstract: Today's large-scale supercomputers are producing a huge amount of log data. Exploring various potential correlations of fatal events is crucial for understanding their causality and improving the working efficiency for system administrators. To this end, we developed a toolkit, named LogAider, that can reveal three types of potential correlations: across-field, spatial, and temporal. Across-field correlation refers to the statistical correlation across fields within a log or across multiple logs based on probabilistic analysis. For analyzing the spatial correlation of events, we developed a generic, easy-to-use visualizer that can view any events queried by users on a system machine graph. LogAider can also mine spatial correlations by an optimized K-meaning clustering algorithm over a Torus network topology. It is also able to disclose the temporal correlations (or error propagations) over a certain period inside a log or across multiple logs, based on an effective similarity analysis strategy. We assessed LogAider using the one-year reliability-availability-serviceability (RAS) log of Mira system (one of the world's most powerful supercomputers), as well as its job log. We find that LogAider very helpful for revealing the potential correlations of fatal system events and job events, with an accurate mining of across-field correlation with both precision and recall of 99.9-100%, as well as precise detection of temporal-correlation with a high similarity (up to 95%) to the ground-truth.