title: Exponential Family Matrix Completion under Structural Constraints

abstract: We consider the matrix completion problem of recovering a structured matrix from noisy and partial measurements. Recent works have proposed tractable estimators with strong statistical guarantees for the case where the underlying matrix is low-rank, and the measurements consist of a subset, either of the exact individual entries, or of the entries perturbed by additive Gaussian noise, which is thus implicitly suited for thin-tailed continuous data. Arguably, common applications of matrix completion require estimators for (a) heterogeneous data-types, such as skewed-continuous, count, binary, etc., (b) for heterogeneous noise models (beyond Gaussian), which capture varied uncertainty in the measurements, and (c) heterogeneous structural constraints beyond low-rank, such as block-sparsity, or a superposition structure of low-rank plus elementwise sparseness, among others. In this paper, we provide a vastly unified framework for generalized matrix completion by considering a matrix completion setting wherein the matrix entries are sampled from any member of the rich family of exponential family distributions; and impose general structural constraints on the underlying matrix, as captured by a general regularizer R(.). We propose a simple convex regularized M-estimator for the generalized framework, and provide a unified and novel statistical analysis for this general class of estimators. We finally corroborate our theoretical results on simulated datasets.


title: An Overview of Virtual Simulation and Serious Gaming for Surgical Education and Training

abstract: The rising popularity of video games has seen a recent push towards the application of serious games to medical education and training. With their ability to engage players/learners for a specific purpose, serious games provide an opportunity to acquire cognitive and technical surgical skills outside the operating room thereby optimizing operating room exposure with live patients. However, before the application of serious games for surgical education and training becomes more widespread, there are a number of open questions and issues that must be addressed including the relationship between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this chapter we begin with a brief overview of alternative medical/surgical educational methods, followed by a discussion of serious games and their application to surgical education, fidelity, multi-modal cue interaction and their role within a virtual simulations/serious games. The chapter ends with a description of the serious games surgical cognitive education and training framework (SCETF) and concluding remarks.


title: Threats to Feminist Identity and Reactions to Gender Discrimination

abstract: The aim of this research was to examine conditions that modify feminists’ support for women as targets of gender discrimination. In an experimental study we tested a hypothesis that threatened feminist identity will lead to greater differentiation between feminists and conservative women as victims of discrimination and, in turn, a decrease in support for non-feminist victims. The study was conducted among 96 young Polish female professionals and graduate students from Gender Studies programs in Warsaw who self-identified as feminists (M age = 22.23). Participants were presented with a case of workplace gender discrimination. Threat to feminist identity and worldview of the discrimination victim (feminist vs. conservative) were varied between research conditions. Results indicate that identity threat caused feminists to show conditional reactions to discrimination. Under identity threat, feminists perceived the situation as less discriminatory when the target held conservative views on gender relations than when the target was presented as feminist. This effect was not observed under conditions of no threat. Moreover, feminists showed an increase in compassion for the victim when she was portrayed as a feminist compared to when she was portrayed as conservative. Implications for the feminist movement are discussed.


title: Invasive species in Southeast Asia: the knowledge so far

abstract: This review deals with alien species invasion in Southeast Asia, an important conservation and management concern in the region. I report on the current and potential future impacts of biological invasions on biodiversity in Southeast Asia. Current knowledge of the invasive species in Southeast Asia is mostly based on anecdotal observations. Nevertheless, I attempt to compile existing empirical evidence on the negative effects of the biological invaders found in the region. These impacts include displacement of native biota, modification of ecosystems, hybridization, environmental disturbance, and economic loss. Any effective counter-measure will need to involve a multi-national strategy, yet such measure is challenging due to a broad spectrum of political and economic development models among the Southeast Asian countries. An overview of the taxonomic structure of the invasive species in Southeast Asia shows that the invasive plant and fish are the most represented taxonomic groups in all countries. The current research effort in invasion ecology from Southeast Asia is not being up to international standard in comparison to other regions, and the absence of recent international journal articles on invasive plant species reveals the biases in biological invasion-related research. The lack of research capacity and financial support from governments, and the inability to disseminate scholarly data in international journals are the possible reasons for the dearth of research literature on biological invasions from the region. Finally, a forward-looking agenda for the region should include improving the quality and quantity of biological invasion research; adopting a tough approach to the illegal release of wildlife; and applying multi-national strategies that integrate data sharing, prioritization, public awareness, policy work, capacity building, conservation actions and surveillance.


Generating Synthetic Healthcare Records Using Convolutional Generative Adversarial Networks


title: Communication network architecture and design principles for smart grids

abstract: An integrated high performance, highly reliable, scalable, and secure communications network is critical for the successful deployment and operation of next-generation electricity generation, transmission, and distribution systems — known as “smart grids.” Much of the work done to date to define a smart grid communications architecture has focused on high-level service requirements with little attention to implementation challenges. This paper investigates in detail a smart grid communication network architecture that supports today's grid applications (such as supervisory control and data acquisition [SCADA], mobile workforce communication, and other voice and data communication) and new applications necessitated by the introduction of smart metering and home area networking, support of demand response applications, and incorporation of renewable energy sources in the grid. We present design principles for satisfying the diverse quality of service (QoS) and reliability requirements of smart grids.


title: On cognitive small cells in two-tier heterogeneous networks

abstract: In a two-tier heterogeneous network (HetNet) where small base stations (SBSs) coexist with macro base stations (MBSs), the SBSs may suffer significant performance degradation due to the inter- and intra-tier interferences. Introducing cognition into the SBSs through the spectrum sensing (e.g., carrier sensing) capability helps them detecting the interference sources and avoiding them via opportunistic access to orthogonal channels. In this paper, we use stochastic geometry to model and analyze the performance of two cases of cognitive SBSs in a multichannel environment, namely, the semi-cognitive case and the full-cognitive case. In the semi-cognitive case, the SBSs are only aware of the interference from the MBSs, hence, only inter-tier interference is minimized. On the other hand, in the full-cognitive case, the SBSs access the spectrum via a contention resolution process, hence, both the intra- and intertier interferences are minimized, but at the expense of reduced spectrum access opportunities. We quantify the performance gain in outage probability obtained by introducing cognition into the small cell tier for both the cases. We will focus on a special type of SBSs called the femto access points (FAPs) and also capture the effect of different admission control policies, namely, the open-access and closed-access policies. We show that a semi-cognitive SBS always outperforms a full-cognitive SBS and that there exists an optimal spectrum sensing threshold for the cognitive SBSs which can be obtained via the analytical framework presented in this paper.


title: Prototyping Software Product Lines analysis with Pharo

abstract: Software Product Lines (SPLs) are an emerging software engineering paradigm that aims to optimize software development costs and time to market trough systematic development of reusable of core assets. At the heart of SPL engineering is variability modelling. Feature models (FM) are a common way to model variability and reason about it. Examples of reasoning are for instance checking that at least one product is represented by a given FM (satisfiability) or finding the product that best fits a given set of requirements. In practice however, such operations are often complex and time consuming. In order to address these challenges, we introduce in the present article the notion of transitive dependency between features and show how it can be used as the basis for efficient analysis and automatic reasoning on feature models. We exploit this new concept to implement a first platform for prototyping and reasoning on large SPLs in Pharo. Finally we illustrate the efficiency of our proposal on the problem of features selection optimisation.


title: Data-Driven Sparse Structure Selection for Deep Neural Networks

abstract: Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy state-of-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter -- scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection.


title: Quantum Recommendation Systems

abstract: A recommendation system uses the past purchases or ratings of $n$ products by a group of $m$ users, in order to provide personalised recommendations to individual users. The information is modelled as an $m \times n$ preference matrix which is assumed to have a good $k$-rank approximation, for a small constant $k$. In this work, we present a quantum algorithm for recommendation systems that has running time $O(\text{poly}(k)\text{polylog}(mn))$. All classical algorithms for recommendation systems that work through reconstructing an approximation of the preference matrix run in time polynomial in the matrix dimension. Our algorithm provides good recommendations by sampling efficiently from an approximation of the preference matrix, without reconstructing the entire matrix. For this, we design an efficient quantum procedure to project a given vector onto the row space of a given matrix. This is the first algorithm for recommendation systems that runs in time polylogarithmic in the dimensions of the matrix and provides a real world application of quantum algorithms in machine learning.


title: A resistive TCAM accelerator for data-intensive computing

abstract: Power dissipation and off-chip bandwidth restrictions are critical challenges that limit microprocessor performance. Ternary content addressable memories (TCAM) hold the potential to address both problems in the context of a wide range of data-intensive workloads that benefit from associative search capability. Power dissipation is reduced by eliminating instruction processing and data movement overheads present in a purely RAM based system. Bandwidth demand is lowered by processing data directly on the TCAM chip, thereby decreasing off-chip traffic. Unfortunately, CMOS-based TCAM implementations are severely power- and area-limited, which restricts the capacity of commercial products to a few megabytes, and confines their use to niche networking applications. This paper explores a novel resistive TCAM cell and array architecture that has the potential to scale TCAM capacity from megabytes to gigabytes. High-density resistive TCAM chips are organized into a DDR3-compatible DIMM, and are accessed through a software library with zero modifications to the processor or the motherboard. On applications that do not benefit from associative search, the TCAM DIMM is configured to provide ordinary RAM functionality. By tightly integrating TCAM with conventional virtual memory, and by allowing a large fraction of the physical address space to be made content-addressable on demand, the proposed memory system improves average performance by 4 x and average energy consumption by 10 x on a set of evaluated data-intensive applications.


title: Review on Design and Control Aspects of Robotic Shoulder Rehabilitation Orthoses

abstract: Robotic rehabilitation devices are more frequently used for the physical therapy of people with upper limb weakness, which is the most common type of stroke-induced disability. Rehabilitation robots can provide customized, prolonged, intensive, and repetitive training sessions for patients with neurological impairments. In most cases, the robotic exoskeletons have to be aligned with the human joints and provide natural arm movements. This is a challenging task to achieve for one of the most biomechanically complex joints of human body, i.e., the shoulder. Therefore, specific considerations have been made in the development of various existing robotic shoulder rehabilitation orthoses. Different types of actuation, degrees of freedom (DOFs), and control strategies have been utilized for the development of these shoulder rehabilitation orthoses. This paper presents a comprehensive review of these shoulder rehabilitation orthoses. Recent advancements in the mechanism design, their advantages and disadvantages, overview of hardware, actuation system, and power transmission are discussed in detail with the emphasis on the assisted DOFs for shoulder motion. A brief overview of control techniques and clinical studies conducted with the developed robotic shoulder orthoses is also presented. Finally, current challenges and directions of future development for robotic shoulder rehabilitation orthoses are provided at the end of this paper.


title: Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching

abstract: Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names. While many approximated name matching techniques have been proposed, most are generic string-matching algorithms. Unlike other types of proper names, personal names are highly cultural. Many ethnicities have their own unique naming systems and identifiable characteristics. In this paper we explore such relationships between ethnicities and personal names to improve the name matching performance. First, we propose a name-ethnicity classifier based on the multinomial logistic regression. Our model can effectively identify nameethnicity from personal names in Wikipedia, which we use to define name-ethnicity, to within 85% accuracy. Next, we propose a novel alignment-based name matching algorithm, based on Smith-Waterman algorithm and logistic regression. Different name matching models are then trained for different name-ethnicity groups. Our preliminary experimental result on DBLP's disambiguated author dataset yields a performance of 99% precision and 89% recall. Surprisingly, textual features carry more weight than phonetic ones in nameethnicity classification.


title: Nexus: An Extensible File Format for Systematic Information

abstract: NEXUS is a file format designed to contain systematic data for use by computer programs. The goals of the format are to allow future expansion, to include diverse kinds of information, to be independent of particular computer operating systems, and to be easily processed by a program. To this end, the format is modular, with a file consisting of separate blocks, each containing one particular kind of information, and consisting of standardized commands. Public blocks (those containing information utilized by several programs) house information about taxa, morphological and molecular characters, distances, genetic codes, assumptions, sets, trees, etc.; private blocks contain information of relevance to single programs. A detailed description of commands in public blocks is given. Guidelines are provided for reading and writing NEXUS files and for extending the format.


title: Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

abstract: In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid n...


title: Double suicidal gunshot wounds to the heart

abstract: A case of double gunshot wounds to the heart is reported, where police investigations determined the manner of death to be suicide. In addition, the autopsy findings supported this conclusion. The localization, appearance, and mutual relations of the inflicted wounds, as well as the direction of the bullet trajectories, helped to determine the most probable sequence of events. The victim had been able to act after the first shot and inflict the second shot as the conduction system of the heart was at least partially preserved. Even when the circumstances indicate the most probable manner of death, multiple gunshot wounds represent a challenge for forensic pathologists; a victim’s ability to act after the first shot must be determined with respect to the degree of initial incapacitation.


title: Artificial Intelligence And Human Decision Making

abstract: Abstract Decision is obviously related to reasoning. One of the possible definitions of artificial intelligence (AI) refers to cognitive processes and especially to reasoning. Before making any decision, people also reason, it is therefore natural to explore the links between AI and decision making. This paper distinguishes between two aspects of decision making: diagnosis and look-ahead. It is shown that, on the one hand, AI has many relationships with diagnosis (expert systems, case-based reasoning, fuzzy set and rough set theories). On the other hand, AI has not paid enough attention to look-ahead reasoning, whose main components are uncertainty and preferences. These aspects of AI and decision making are reviewed in the paper.


title: Fast Metric Tracking by Detection System: Radar Blob and Camera Fusion

abstract: This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.


title: Control theoretic approach to tracking radar: First step towards cognition

abstract: In Haykin (2006) [8], the idea of Cognitive Radar was described for the first time. Four essential points were emphasized in that seminal paper: Bayesian filtering in the receiver, dynamic programming in the transmitter, memory, and global feedback to facilitate computational intelligence. This paper provides a first step towards designing a cognitive radar for tracking applications by presenting a fore-active tracking radar; a radar that utilizes its previous measurements and actions to optimize its transmitted waveform (Haykin, 2011 [11]). In our design, the emphasis is being placed on the cubature Kalman filter to approximate the Bayesian filter in the receiver, approximate dynamic programming for transmit-waveform selection in the transmitter, and global feedback embodying the transmitter, the radar environment, and the receiver all under one overall feedback loop. Simulation results, based on the tracking of an object falling in space, are presented, which substantiate practical validity of the superior performance of a fore-active tracking radar over a traditional active radar with fixed waveform.


title: Internet addiction in adolescents: Prevalence and risk factors

abstract: As new media are becoming daily fare, Internet addiction appears as a potential problem in adolescents. From the reported negative consequences, it appears that Internet addiction can have a variety of detrimental outcomes for young people that may require professional intervention. Researchers have now identified a number of activities and personality traits associated with Internet addiction. This study aimed to synthesise previous findings by (i) assessing the prevalence of potential Internet addiction in a large sample of adolescents, and (ii) investigating the interactions between personality traits and the usage of particular Internet applications as risk factors for Internet addiction. A total of 3105 adolescents in the Netherlands filled out a self-report questionnaire including the Compulsive Internet Use Scale and the Quick Big Five Scale. Results indicate that 3.7% of the sample were classified as potentially being addicted to the Internet. The use of online gaming and social applications (online social networking sites and Twitter) increased the risk for Internet addiction, whereas extraversion and conscientiousness appeared as protective factors in high frequency online gamers. The findings support the inclusion of ‘Internet addiction’ in the DSM-V. Vulnerability and resilience appear as significant aspects that require consideration in further studies.


title: Modelling the interaction of electromagnetic fields (10 MHz-10 GHz) with the human body: methods and applications.

abstract: Numerical modelling of the interaction between electromagnetic fields (EMFs) and the dielectrically inhomogeneous human body provides a unique way of assessing the resulting spatial distributions of internal electric fields, currents and rate of energy deposition. Knowledge of these parameters is of importance in understanding such interactions and is a prerequisite when assessing EMF exposure or when assessing or optimizing therapeutic or diagnostic medical applications that employ EMFs. In this review, computational methods that provide this information through full time-dependent solutions of Maxwell's equations are summarized briefly. This is followed by an overview of safety- and medical-related applications where modelling has contributed significantly to development and understanding of the techniques involved. In particular, applications in the areas of mobile communications, magnetic resonance imaging, hyperthermal therapy and microwave radiometry are highlighted. Finally, examples of modelling the potentially new medical applications of recent technologies such as ultra-wideband microwaves are discussed.


title: Google Street View: Capturing the World at Street Level

abstract: Street View serves millions of Google users daily with panoramic imagery captured in hundreds of cities in 20 countries across four continents. A team of Google researchers describes the technical challenges involved in capturing, processing, and serving street-level imagery on a global scale.


title: Deep Relative Attributes

abstract: Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features. We adopt an appropriate ranking loss to train the whole network in an end-to-end fashion. Our proposed method outperforms the baseline and state-of-the-art methods in relative attribute prediction on various coarse and fine-grained datasets. Our qualitative results along with the visualization of the saliency maps show that the network is able to learn effective features for each specific attribute. Source code of the proposed method is available at this https URL


title: Hierarchical Prosody Conversion Using Regression-Based Clustering for Emotional Speech Synthesis

abstract: This paper presents an approach to hierarchical prosody conversion for emotional speech synthesis. The pitch contour of the source speech is decomposed into a hierarchical prosodic structure consisting of sentence, prosodic word, and subsyllable levels. The pitch contour in the higher level is encoded by the discrete Legendre polynomial coefficients. The residual, the difference between the source pitch contour and the pitch contour decoded from the discrete Legendre polynomial coefficients, is then used for pitch modeling at the lower level. For prosody conversion, Gaussian mixture models (GMMs) are used for sentence- and prosodic word-level conversion. At subsyllable level, the pitch feature vectors are clustered via a proposed regression-based clustering method to generate the prosody conversion functions for selection. Linguistic and symbolic prosody features of the source speech are adopted to select the most suitable function using the classification and regression tree for prosody conversion. Three small-sized emotional parallel speech databases with happy, angry, and sad emotions, respectively, were designed and collected for training and evaluation. Objective and subjective evaluations were conducted and the comparison results to the GMM-based method for prosody conversion achieved an improved performance using the hierarchical prosodic structure and the proposed regression-based clustering method.


title: Design and Application of a VOC-Monitoring System Based on a ZigBee Wireless Sensor Network

abstract: Monitoring volatile organic compound (VOC) pollution levels in indoor environments is of great importance for the health and comfort of individuals, especially considering that people currently spend >80% of their time indoors. The primary aim of this paper is to design a low-power ZigBee sensor network and internode data reception control framework to use in the real-time acquisition and communication of data concerning air pollutant levels from VOCs. The network consists of end device sensors with photoionization detectors, routers that propagate the network over long distances, and a coordinator that communicates with a computer. The design is based on the ATmega16 microcontroller and the Atmel RF230 ZigBee module, which are used to effectively process communication data with low power consumption. Priority is given to power consumption and sensing efficiency, which are achieved by incorporating various smart tasking and power management protocols. The measured data are displayed on a computer monitor through a graphical user interface. The preliminary experimental results demonstrate that the wireless sensor network system can monitor VOC concentrations with a high level of accuracy and is thus suitable for automated environmental monitoring. Both good indoor air quality and energy conservation can be achieved by integrating the VOC monitoring system proposed in this paper with the residential integrated ventilation controller.


title: 3D path planning with continuous bounded curvature and pitch angle profiles using 7th order curves

abstract: Path planning is a fundamental task for any kind of autonomous mobile robot. In this paper, we present a motion planning technique on the three-dimensional space considering vehicles with spatial curvature and pitch (climb or dive) angle constraints. Concerning real fixed-wing Unmanned Aerial Vehicles or underwater Remotely Operated Vehicles, we face the problem of calculating paths with continuous acceleration profiles, preventing the robot from abrupt turning or climbing/diving rates during its navigation. We also focus on the generation of paths with reduced length, allowing energy savings and minimizing the time for the task fulfillments. The proposed methodology provides, in a fast and iterative way, near-optimal paths in some scenarios, but with the advantage of continuous curvature and pitch rate profiles, a fundamental issue regarding the dynamics of real-world robots. The methodology was validated through numerous trials, under different scenarios in a simulated environment, and the results compared to the ones obtained by state-of-the-art techniques, providing a thorough evaluation.


title: Physical therapy intervention in the neonatal intensive care unit.

abstract: This article presents the elements of the Intervention section of the Infant Care Path for Physical Therapy in the Neonatal Intensive Care Unit (NICU). The types of physical therapy interventions presented in this path are evidence-based and the suggested timing of these interventions is primarily based on practice knowledge from expert therapists, with supporting evidence cited. Physical therapy intervention in the NICU is infant-driven and focuses on providing family-centered care. In this context, interventions to facilitate a calm behavioral state and motor organization in the infant, address positioning and handling of the infant, and provide movement therapy are presented.


Breaking Microsoft ’ s CAPTCHA


title: Carsim: A System To Visualize Written Road Accident Reports As Animated 3D Scenes

abstract: This paper describes a system to create animated 3D scenes of car accidents from reports written in Swedish. The system has been developed using news reports of varying size and complexity. The text-to-scene conversion process consists of two stages. An information extraction module creates a structured representation of the accident and a visual simulator generates and animates the scene. ::: ::: We first describe the overall structure of the text-to-scene conversion and the structure of the representation. We then explain the information extraction and visualization modules. We show snapshots of the car animation output and we conclude with the results we obtained.


title: Rule-based composition of intelligent mechatronic components in manufacturing systems using prolog

abstract: This paper proposes a rule-based approach for automatic configuration of mechatronic components in a novel agent-based manufacturing automation architecture known as MIRA, implemented using Prolog. Through this method, MIRAs are enriched with semantic knowledge representation and, based on that, perform some reasoning and decision making (both at the design stage and even during the operation) to achieve the desired goals. This approach is illustrated in a simple case study in which composition of a reconfigurable pick-and-place robot with various linear cylinders is achieved through rule-based reasoning.


title: Powered Lower Limb Orthoses for Gait Rehabilitation

abstract: Bodyweight supported treadmill training has become a prominent gait rehabilitation method in leading rehabilitation centers. This type of locomotor training has many functional benefits but the labor costs are considerable. To reduce therapist effort, several groups have developed large robotic devices for assisting treadmill stepping. A complementary approach that has not been adequately explored is to use powered lower limb orthoses for locomotor training. Recent advances in robotic technology have made lightweight powered orthoses feasible and practical. An advantage to using powered orthoses as rehabilitation aids is they allow practice starting, turning, stopping, and avoiding obstacles during overground walking.


title: Building citizen trust through e-government

abstract: The trust of citizens in their governments has gradually eroded. One response by several North American governments has been to introduce e-government, or Web-mediated citizen-to-government interaction. This paper tests the extent to which online initiatives have succeeded in increasing trust and external political efficacy in voters. An Internet-based survey of 182 Canadian voters shows that using the Internet to transact with government has a significantly positive impact on trust and external political efficacy. Interestingly, though the quality of the interaction is important, it is secondary to internal political efficacy in determining trust levels, and not significant in determining levels of external political efficacy (or perceived government responsiveness). For policy-makers, this suggests e-government efforts might be better-aimed at citizens with high pre-extant levels of trust, rather than in developing better Web sites. For researchers, this paper introduces political efficacy as an important determinant of trust as it pertains to e-government.


title: How low does ethical leadership flow? Test of a trickle-down model

abstract: This research examines the relationships between top management and supervisory ethical leadership and group-level outcomes (e.g., deviance, OCB) and suggests that ethical leadership flows from one organizational level to the next. Drawing on social learning theory [Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.; Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall.] and social exchange theory [Blau, p. (1964). Exchange and power in social life. New York: John Wiley.], the results support our theoretical model using a sample of 904 employees and 195 managers in 195 departments. We find a direct negative relationship between both top management and supervisory ethical leadership and group-level deviance, and a positive relationship with group-level OCB. Finally, consistent with the proposed trickle-down model, the effects of top management ethical leadership on group-level deviance and OCB are mediated by supervisory ethical leadership.


title: A time delay neural network architecture for efficient modeling of long temporal contexts.

abstract: Recurrent neural network architectures have been shown to efficiently model long term temporal dependencies between acoustic events. However the training time of recurrent networks is higher than feedforward networks due to the sequential nature of the learning algorithm. In this paper we propose a time delay neural network architecture which models long term temporal dependencies with training times comparable to standard feed-forward DNNs. The network uses sub-sampling to reduce computation during training. On the Switchboard task we show a relative improvement of 6% over the baseline DNN model. We present results on several LVCSR tasks with training data ranging from 3 to 1800 hours to show the effectiveness of the TDNN architecture in learning wider temporal dependencies in both small and large data scenarios.


title: Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

abstract: Abstract Hand pose estimation from single depth images is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural networks, accurate hand pose estimation is still a challenging problem. In this paper we propose a novel approach named as pose guided structured region ensemble network (Pose-REN) to boost the performance of hand pose estimation. Under the guidance of an initially estimated pose, the proposed method extracts regions from the feature maps of convolutional neural network and generates more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by tree-structured fully connections to regress the refined hand pose. The final hand pose is obtained by an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.


title: An artificial neural network approach for wafer dicing saw quality prediction

abstract: Abstract The wafer dicing saw is a critical process of the semiconductor IC assembly manufacturing, where chipping and cracking can damage internal circuits and result in device failure. This article applies back propagation neural networks to learn and analyze data transmitted from the machine sensors for predictive purposes. Experimental results show that neural network processing provides prediction accuracy of 75%. The results can be monitored during real-time machine operation. If predictive values exceed a certain specification, an alarm can be triggered to prevent product loss, thus increasing production efficiency.


A Survey and Comparative Study of Filter and Wrapper Feature Selection Techniques


title: Exemplar- based Sparse Representation and Sparse Discrimination for Noise Robust Speaker Identification

abstract: Probabilistic modeling is the most successful approach widely used in speaker recognition either for modeling the speakers in GMM-UBM structure or by serving as a prior in secondarylevel feature extraction to form i-vectors. In this paper, we introduce exemplar-based sparse representation and sparse discrimination for closed-set speaker identification in a noisy living room from very short speech segments each of 2 seconds length on average. Large spectro-temporal contexts in melfrequency band energy domain are used to build dictionary of all speakers and decomposing the observed noisy speech, the sparse activations are extracted as features for modeling stage. Sparse discriminant analysis is employed to learn sparse discriminative directions for classification stage. Experiments on the recently developed computational hearing in multi source environments (CHiME) corpus demonstrate excellent performance of the proposed approach specially in low-SNR. The speaker identification results are also reported for baseline textindependent GMM-UBM and text-dependent HMM.


title: ERP components on reaction errors and their functional significance: a tutorial

abstract: Abstract Some years ago we described a negative (Ne) and a later positive (Pe) deflection in the event-related brain potentials (ERPs) of incorrect choice reactions [Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L., 1990. In: Brunia, C.H.M., Gaillard, A.W.K., Kok, A. (Eds.), Psychophysiological Brain Research. Tilburg Univesity Press, Tilburg, pp. 192–195. Falkenstein, M., Hohnsbein, J., Hoormann, J., 1991. Electroencephalography and Clinical Neurophysiology, 78, 447–455]. Originally we assumed the Ne to represent a correlate of error detection in the sense of a mismatch signal when representations of the actual response and the required response are compared. This hypothesis was supported by the results of a variety of experiments from our own laboratory and that of Coles [Gehring, W.J., Goss, B., Coles, M.G.H., Meyer, D.E., Donchin, E., 1993. Psychological Science 4, 385–390. Bernstein, P.S., Scheffers, M.K., Coles, M.G.H., 1995. Journal of Experimental Psychology: Human Perception and Performance 21, 1312–1322. Scheffers, M.K., Coles, M.G.H., Bernstein, P., Gehring, W.J., Donchin, E., 1996. Psychophysiology 33, 42–54]. However, new data from our laboratory and that of Vidal et al. [Vidal, F., Hasbroucq, T., Bonnet, M., 1999. Biological Psychology, 2000] revealed a small negativity similar to the Ne also after correct responses. Since the above mentioned comparison process is also required after correct responses it is conceivable that the Ne reflects this comparison process itself rather than its outcome. As to the Pe, our results suggest that this is a further error-specific component, which is independent of the Ne, and hence associated with a later aspect of error processing or post-error processing. Our new results with different age groups argue against the hypotheses that the Pe reflects conscious error processing or the post-error adjustment of response strategies. Further research is necessary to specify the functional significance of the Pe.


title: Balance of neurotransmitter amino acids and integrative activity of the brain after local ischemic damage to the frontal cortex in rats: effects of glycine and piracetam

abstract: Bilateral local compression ischemia in the frontal rat brain cortex modifies the levels of neurotransmitter amino acids at the stage of most pronounced disorders in the brain integrative activity. In this model, neuroprotective properties of the drugs piracetam and glycine are evaluated. Glycine accelerates GABA turnover at the sites of ischemia as well as in the parietal cortex and hippocampus and, similar to piracetam, restores almost completely the conditioned response of passive avoidance impaired by ischemia. Glycine also reduces locomotor activity of both ischemic and sham-operated rats. It is suggested that normalizing effect of glycine on the functional balance of neurotransmitter amino acids immediately after ischemic damage to the cortex promotes restitution of disturbed functions of the central nervous system.


title: Consistency of the EKF-SLAM Algorithm

abstract: This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKF-SLAM). We show that the algorithm produces very optimistic estimates once the "true" uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without ground-truth, although a very inconsistent filter may exhibit observable symptoms, such as disproportionately large jumps in the vehicle pose update. Conventional solutions - adding stabilising noise, using an iterated EKF or unscented filter, etc., - do not improve the situation. However, if "small" heading uncertainty is maintained, EKF-SLAM exhibits consistent behaviour over an extended time-period. Although the uncertainty estimate slowly becomes optimistic, inconsistency can be mitigated indefinitely by applying tactics such as batch updates or stabilising noise. The manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for large-scale maps


title: Supervised Representation Learning with Double Encoding-Layer Autoencoder for Transfer Learning

abstract: Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed to learn more robust or higher-level features for transfer learning. In this article, we adapt the autoencoder technique to transfer learning and propose a supervised representation learning method based on double encoding-layer autoencoder. The proposed framework consists of two encoding layers: one for embedding and the other one for label encoding. In the embedding layer, the distribution distance of the embedded instances between the source and target domains is minimized in terms of KL-Divergence. In the label encoding layer, label information of the source domain is encoded using a softmax regression model. Moreover, to empirically explore why the proposed framework can work well for transfer learning, we propose a new effective measure based on autoencoder to compute the distribution distance between different domains. Experimental results show that the proposed new measure can better reflect the degree of transfer difficulty and has stronger correlation with the performance from supervised learning algorithms (e.g., Logistic Regression), compared with previous ones, such as KL-Divergence and Maximum Mean Discrepancy. Therefore, in our model, we have incorporated two distribution distance measures to minimize the difference between source and target domains in the embedding representations. Extensive experiments conducted on three real-world image datasets and one text data demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.


Real-Time Object Detection and Recognition System Using OpenCV via SURF Algorithm in Emgu CV for Robotic Handling in Libraries


title: OpenID 2.0: a platform for user-centric identity management

abstract: With the advancement in user-centric and URI-based identity systems over the past two years, it has become clear that a single specification will not be the solution to all problems. Rather, like the other layers of the Internet, developing small, interoperable specifications that are independently implementable and useful will ultimately lead to market adoption of these technologies. This is the intent of the OpenID framework. OpenID Authentication 1.0 began as a lightweight HTTP-based URL authentication protocol. OpenID Authentication 2.0 it is now turning into an open community-driven platform that allows and encourages innovation. It supports both URLs and XRIs as user identifiers, uses Yadis XRDS documents for identity service discovery, adds stronger security, and supports both public and private identifiers. With continuing convergence under this broad umbrella, the OpenID framework is emerging as a viable solution for Internet-scale user-centric identity infrastructure.


title: Mobile health monitoring system based on activity recognition using accelerometer

abstract: We propose a new method to recognize a user’s activities of daily living with accelerometers and RFID sensor. Two wireless accelerometers are used for classification of five human body states using decision tree, and detection of RFID-tagged objects with hand movements provides additional instrumental activity information. Besides, we apply our activity recognition module to the health monitoring system. We derive linear regressions for each activity by finding the correlations between the attached accelerometers and the expended calories calculated from gas exchange analyzer under different activities. Finally, we can predict the expended calories more efficiently with only accelerometer sensor depend on the recognized activity. We implement our proposed health monitoring module on smart phones for better practical use.


title: A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity

abstract: The tremendous growth in the amount of attention and users, on social networking sites (SNSs), has led to information overload and that adds to the difficulty of making accurate recommendations of new friends to the users of SNSs. This article incorporates collaborative filtering (CF), the most successful and widely used filtering technique, in social networks to facilitate users in exploring new friends having similar interests while being connected with old ones as well. Here, first we design an implicit rating model, for estimating a user’s affinity toward his friends, which uncover the strength of relationship, utilizing both attribute similarity and user interaction intensity. We then propose a CF-based framework that offers list of friends to the user by leveraging on the preference of like-minded users, with a given small set of people that user has already labeled as friends. Despite the immense success of CF, accuracy and sparsity are still major challenges, especially in social networking domain with a staggering growth having enormous number of users. To address these inherent challenges, first we have explored the idea of adaptive similarity computation between users by employing evolutionary algorithms to learn individual preferences toward particular set of attributes that results in considerable improvement in recommendation accuracy as compared to the situation where all the attributes are given equal importance. Second, we incorporate effective missing data prediction algorithm as a solution to data sparsity thereby further enhancing accuracy. Experimental results are presented to illustrate the effectiveness of the proposed friends recommendation schemes.


title: Developmental robotics: a survey

abstract: Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.


title: Programming in a Robotics Context in the Kindergarten Classroom: The Impact on Sequencing Skills.

abstract: this paper examines the impact of programming of robots on sequencing ability in early childhood and the relationship be tween sequencing skills, class size, and teachers’ comfort lev el and experience with technology. fifty-eight children par ticipated in the study; 54 children were included in data anal ysis. this study was conducted in two different school envi ronments, where class size and teachers’ experiences with the technology use varied – one teacher had used the technology in the prior year; the other teacher had not. school environ ments were further subdivided into control and experimental groups. Children in the experimental group were exposed to the t angibleK program for a period of 20 hours, taught by their classroom teacher. Children participated in computer programming activities using a developmentally appropri ate tangible programming language, specifically designed to program a robot’s behavior. all 54 participants’ sequencing skills were assessed before and after the intervention using a picture story sequencing task and analyzed using a repeated measures, 2x2x2 design ANOVA. A significant interaction was found between group assignment and test results. no significant interactions were found for school assignment. results are discussed taking into account class size, teacher experience, and teacher comfort level with technology.


title: Listening in: practices surrounding iTunes music sharing

abstract: This paper presents a descriptive account of the social practices surrounding the iTunes music sharing of 13 participants in one organizational setting. Specifically, we characterize adoption, critical mass, and privacy; impression management and access control; the musical impressions of others that are created as a result of music sharing; the ways in which participants attempted to make sense of the dynamic system; and implications of the overlaid technical, musical, and corporate topologies. We interleave design implications throughout our results and relate those results to broader themes in a music sharing design space.


title: Smartphone-Based Point-of-Care Urinalysis Under Variable Illumination

abstract: Urine tests are performed by using an off-the-shelf reference sheet to compare the color of test strips. However, the tabular representation is difficult to use and more prone to visual errors, especially when the reference color-swatches to be compared are spatially apart. Thus, making it is difficult to distinguish between the subtle differences of shades on the reagent pads. This manuscript represents a new arrangement of reference arrays for urine test strips (urinalysis). Reference color swatches are grouped in a doughnut chart, surrounding each reagent pad on the strip. The urine test can be evaluated using naked eye by referring to the strip with no additional sheet necessary. Along with this new strip, an algorithm for smartphone based application is also proposed as an alternative to deliver diagnostic results. The proposed colorimetric detection method evaluates the captured image of the strip, under various color spaces and evaluates ten different tests for urine. Thus, the proposed system can deliver results on the spot using both naked eye and smartphone. The proposed scheme delivered accurate results under various environmental illumination conditions without any calibration requirements, exhibiting performances suitable for real-life applications and an ease for a common user.


title: FlatFish - a compact subsea-resident inspection AUV

abstract: Currently the inspection of industrial underwater structures is performed by remotely operated vehicles or by divers. Since the number of underwater structures keeps growing, e.g. due to new oil and gas fields or offshore wind farms, the need for a constantly available method to inspect these structures arises. The FlatFish project is an initiative of BG Group Brazil and the Brazilian Institute of Robotics in Salvador and aims at developing a subsea-resident AUV which can inspect the infrastructure at an oil and gas site on demand. FlatFish is a compact AUV, designed to acquire a high-resolution, textured 3D model of an underwater structure within an oil and gas asset. FlatFish is very agile and can hover during inspection, it uses a novel kind of navigation system to safely reach its goal within the field and is designed to stay submerged for extended periods of time. Within this paper the system design of FlatFish is presented.


title: Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss

abstract: Despite the recent success of convolutional neural networks for computer vision applications, unconstrained face recognition remains a challenge. In this work, we make two contributions to the field. Firstly, we consider the problem of face recognition with partial occlusions and show how current approaches might suffer significant performance degradation when dealing with this kind of face images. We propose a simple method to find out which parts of the human face are more important to achieve a high recognition rate, and use that information during training to force a convolutional neural network to learn discriminative features from all the face regions more equally, including those that typical approaches tend to pay less attention to. We test the accuracy of the proposed method when dealing with real-life occlusions using the AR face database. Secondly, we propose a novel loss function called batch triplet loss that improves the performance of the triplet loss by adding an extra term to the loss function to cause minimisation of the standard deviation of both positive and negative scores. We show consistent improvement in the Labeled Faces in the Wild (LFW) benchmark by applying both proposed adjustments to the convolutional neural network training.


title: Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data

abstract: In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains ::: bilingual texts and corresponding images is required to train a MNMT system with image features. However, ::: such a corpus is not available for low resource language pairs. To address this, ::: we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based ::: on an existing English-image parallel ::: corpus. We used these datasets to ::: build our image description translation system by adopting state-of-theart MNMT models. Our results show ::: that it is possible to train a MNMT ::: system for low-resource language pairs ::: through the use of synthetic data and ::: that such a system can benefit from image features.


title: Learning Linear Transformations for Fast Arbitrary Style Transfer

abstract: Given a random pair of images, an arbitrary style transfer method extracts the feel from the reference image to synthesize an output based on the look of the other content image. Recent arbitrary style transfer methods transfer second order statistics from reference image onto content image via a multiplication between content image features and a transformation matrix, which is computed from features with a pre-determined algorithm. These algorithms either require computationally expensive operations, or fail to model the feature covariance and produce artifacts in synthesized images. Generalized from these methods, in this work, we derive the form of transformation matrix theoretically and present an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network. Our algorithm is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process. We demonstrate the effectiveness of our approach on four tasks: artistic style transfer, video and photo-realistic style transfer as well as domain adaptation, including comparisons with the state-of-the-art methods.


title: Neural Collective Entity Linking

abstract: Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.


title: Probabilistic 3D multilabel real-time mapping for multi-object manipulation

abstract: Probabilistic 3D map has been applied to object segmentation with multiple camera viewpoints, however, conventional methods lack of real-time efficiency and functionality of multilabel object mapping. In this paper, we propose a method to generate three-dimensional map with multilabel occupancy in real-time. Extending our previous work [1] in which only target label occupancy is mapped, we achieve multilabel object segmentation in a single looking around action. We evaluate our method by testing segmentation accuracy with 39 different objects, and applying it to a manipulation task of multiple objects in the experiments. Our mapping-based method outperforms the conventional projection-based method by 40–96% relative (12.6 mean IU 3d ), and robot successfuly recognizes (86.9%) and manipulates multiple objects (60.7%) in an environment with heavy occlusions.


title: The management of tooth wear with crowns and indirect restorations

abstract: This manuscript summarises the reasons behind choosing indirect restorations in the treatment of tooth wear. The purpose of this article is to discuss the use of crowns as a restorative treatment option for tooth wear. There are also challenges with the use of composites as they can repeatedly fail and in these situations the indications for crowns for treatment of tooth wear is worthy of consideration. This article is part of a themed issue discussing the management of tooth wear.


title: Generative Models for Pose Transfer

abstract: We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding frames and generalizing outside the demonstrated action set. Our most salient contribution is determining a pipeline (pose detection, face detection, k-NN based pairing) that is effective at perform-ing the desired task. We also detail several iterative improvements and failure modes.


title: Compact Duplexing for a 680-GHz Radar Using a Waveguide Orthomode Transducer

abstract: A compact 680-GHz waveguide orthomode transducer (OMT) and circular horn combination has been designed, tested, and characterized in a radar transceiver's duplexer. The duplexing capability is implemented by a hybrid waveguide quasi optical solution, combining a linear polarization OMT and an external grating polarizer. Isolation between the OMT's orthogonal ports' flanges was measured with a vector network analyzer to exceed 33 dB over a >10% bandwidth between 630 and 710 GHz. Calibrated Y-factor measurements using a mixer attached to the OMT ports reveal losses through the transmit and receive paths that sum to an average of 4.7 dB of two-way loss over 660-690 GHz. This is consistent with radar sensitivity measurements comparing the new OMT/horn with a quasi-optical wire grid beam splitter. Moreover, the radar performance assessment validates the OMT as a suitable compact substitute of the wire grid for the JPL's short-range 680-GHz imaging radar.


title: A Family of Rank Similarity Measures Based on Maximized Effectiveness Difference

abstract: Rank similarity measures provide a method for quantifying differences between search engine results without the need for relevance judgments. For example, the providers of a search service might use such measures to estimate the impact of a proposed algorithmic change across a large number of queries—perhaps millions—identifying those queries where the impact is greatest. In this paper, we propose and validate a family of rank similarity measures, each derived from an associated effectiveness measure. Each member of the family is based on the maximization of effectiveness difference under this associated measure. Computing this maximized effectiveness difference (MED) requires the solution of an optimization problem that varies in difficulty, depending on the associated measure. We present solutions for several standard effectiveness measures, including nDCG, AP, and ERR. Through an experimental validation, we show that MED reveals meaningful differences between retrieval runs. Mathematically, MED is a metric, regardless of the associated measure. Prior work has established a number of other desiderata for rank similarity in the context of search, and we demonstrate that MED satisfies these requirements. Unlike previous proposals, MED allows us to directly translate assumptions about user behavior from any established effectiveness measure to create a corresponding rank similarity measure. In addition, MED cleanly accommodates partial relevance judgments, and if complete relevance information is available, it reduces to a simple difference between effectiveness values.


title: MyMediaLite: a free recommender system library

abstract: MyMediaLite is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars) and item prediction from positive-only implicit feedback (e.g. from clicks or purchase actions). The library offers state-of-the-art algorithms for those two tasks. Programs that expose most of the library's functionality, plus a GUI demo, are included in the package. Efficient data structures and a common API are used by the implemented algorithms, and may be used to implement further algorithms. The API also contains methods for real-time updates and loading/storing of already trained recommender models. MyMediaLite is free/open source software, distributed under the terms of the GNU General Public License (GPL). Its methods have been used in four different industrial field trials of the MyMedia project, including one trial involving over 50,000 households.


title: Challenges of computational verification in social multimedia

abstract: Fake or misleading multimedia content and its distribution through social networks such as Twitter constitutes an increasingly important and challenging problem, especially in the context of emergencies and critical situations. In this paper, the aim is to explore the challenges involved in applying a computational verification framework to automatically classify tweets with unreliable media content as fake or real. We created a data corpus of tweets around big events focusing on the ones linking to images (fake or real) of which the reliability could be verified by independent online sources. Extracting content and user features for each tweet, we explored the fake prediction accuracy performance using each set of features separately and in combination. We considered three approaches for evaluating the performance of the classifier, ranging from the use of standard cross-validation, to independent groups of tweets and to cross-event training. The obtained results included a 81% for tweet features and 75% for user ones in the case of cross-validation. When using different events for training and testing, the accuracy is much lower (up to %58) demonstrating that the generalization of the predictor is a very challenging issue.


title: Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images

abstract: The analysis and the count of blood cell in microscope image can provide useful information concerning the health of the patients. In particular, morphological analysis of white cell deformations can effectively detect important diseases such as the acute lymphoblastic leukemia. Blood images obtained by microscopes coupled with a digital camera are simple to obtain and can be more simply transmitted to clinical centers than liquid blood samples. Automatic measurement systems for white cells in blood microscope image can greatly help blood experts that typically inspect blood films manually. Unfortunately, the analysis made by human experts is not rapid and it presents a not standardized accuracy due to the operator's capabilities and tiredness. The presented paper shows how that it is effectively possible to accurately measure the white cells properties in order to allow, at a second stage, the leukemia identification. In particular, the paper presents how to suitably enhance the microscope image by removing the undesired microscope background and a new segmentation strategy to robustly identify white cells permitting to better extract their features for subsequent automatic diagnosis of diseases


title: Three Topological Results on the Twistor Discriminant Locus in the 4-Sphere

abstract: We exploit techniques from classical (real and complex) algebraic geometry for the study of the standard twistor fibration \({\pi : \mathbb{CP}^3 \rightarrow S^4}\). We prove three results about the topology of the twistor discriminant locus of an algebraic surface in \({\mathbb{CP}^3}\). First of all we prove that, with the exception of two special cases, the real dimension of the twistor discriminant locus of an algebraic surface is always equal to 2. Secondly we describe the possible intersections of a general surface with the family of twistor lines: we find that only 4 configurations are possible and for each of them we compute the dimension. Lastly we give a decomposition of the twistor discriminant locus of a given cone in terms of its singular locus and its dual variety.


title: Pervasive business intelligence as a competitive advantage

abstract: Today the strategic significance of information is fundamental to any organization. With the intensification of competition between companies in open markets and often saturated, companies must learn to know themselves and to the market through the collection and analysis of quality information. The strategic information is seen as a key resource for success in the business, which is provided by Business Intelligence systems. A successful business strategy requires an awareness of the surrounding (internal and external) environment of organizations, including customers, competitors, industry structure and competitive forces. Managing the future means not only is able to anticipate what will happen outside the organization, but also be able to represent the events through their own actions timely. To make it possible, Pervasive Business Intelligence arises as a natural evolution of business intelligence applications in organizations, allowing to companies achieve and maintain a sustainable competitive advantage.


title: Apollo: scalable and coordinated scheduling for cloud-scale computing

abstract: Efficiently scheduling data-parallel computation jobs over cloud-scale computing clusters is critical for job performance, system throughput, and resource utilization. It is becoming even more challenging with growing cluster sizes and more complex workloads with diverse characteristics. This paper presents Apollo, a highly scalable and coordinated scheduling framework, which has been deployed on production clusters at Microsoft to schedule thousands of computations with millions of tasks efficiently and effectively on tens of thousands of machines daily. The framework performs scheduling decisions in a distributed manner, utilizing global cluster information via a loosely coordinated mechanism. Each scheduling decision considers future resource availability and optimizes various performance and system factors together in a single unified model. Apollo is robust, with means to cope with unexpected system dynamics, and can take advantage of idle system resources gracefully while supplying guaranteed resources when needed.


title: Excluded Grid Theorem: Improved and Simplified

abstract: We study the Excluded Grid Theorem of Robertson and Seymour. This is a fundamental result in graph theory, that states that there is some function f:Z+→ Z+, such that for any integer g> 0, any graph of treewidth at least f(g), contains the (g x g)-grid as a minor. Until recently, the best known upper bounds on f were super-exponential in g. A recent work of Chekuri and Chuzhoy provided the first polynomial bound, by showing that treewidth f(g)=O(g98 poly log g) is sufficient to ensure the existence of the (g x g)-grid minor in any graph. In this paper we provide a much simpler proof of the Excluded Grid Theorem, achieving a bound of $f(g)=O(g^{36} poly log g)$. Our proof is self-contained, except for using prior work to reduce the maximum vertex degree of the input graph to a constant.


title: Warfarin or acenocoumarol: which is better in the management of oral anticoagulants?

abstract: Warfarin is employed more frequently than acenocoumarol because of its longer half-life (36 h), theoretically providing more stable anticoagulation, and avoiding factor VII fluctuations that potentially occur during acenocoumarol treatment (half-life 10 h). The aim of our study was to compare acenocoumarol with warfarin in the same group of 103 patients who started oral anticoagulation with acenocoumarol and then changed to warfarin. In these patients we compared the previous period of six months on acenocoumarol treatment (July-December 1996) with a new six-month period on warfarin (July-December 1997). We wished to know whether warfarin could improve the quality and the stability of oral anticoagulation of our patients and whether there was a difference between the two drugs in the weekly mean dose per patient. Moreover in order to detect the possible daily fluctuation of factor VII, we evaluated a further group of 54 patients. A subgroup of these patients was treated with warfarin while another received acenocoumarol. In the first group of patients, 1,158 and 1,064 PTs were carried out with acenocoumarol and warfarin, respectively. The percentage of PTs in the therapeutic range was 59% with acenocoumarol and 62% with warfarin (p=0.4). The mean number of visits per patient was 12 and 11, and the mean number of visits in the therapeutic range was 7 and 7, respectively. The last check in file method did not show any difference between the two drugs. Overdose states were 51 (4.4%) with acenocoumarol and 30 (2.8%) with warfarin (p=0.4). A good correlation (r=0.92) was found between the acenocoumarol and the warfarin weekly mean dose. The mean warfarin/acenocoumarol weekly dose ratio was 2.08 (range: 1.25-3.30; CI 95%: 1.99-2.16). In the second group of patients, factor VII levels with both drugs were higher 24 h after administration than 16 h after, showing that their daily fluctuation was independent of the drug's half-life, since factor VII levels in patients with a low vitamin K intake were not increased. Our results showed that warfarin did not appear to be better than acenocoumarol in the performance of an Anticoagulation Clinic in terms of PTs within the therapeutic range per patient. It seems that the behaviour of factor VII was affected by the intake of vitamin K rather than by the short half-life of acenocoumarol.


title: Representational similarity encoding for fMRI: Pattern-based synthesis to predict brain activity using stimulus-model-similarities.

abstract: Patterns of neural activity are systematically elicited as the brain experiences categorical stimuli and a major challenge is to understand what these patterns represent. Two influential approaches, hitherto treated as separate analyses, have targeted this problem by using model-representations of stimuli to interpret the corresponding neural activity patterns. Stimulus-model-based-encoding synthesizes neural activity patterns by first training weights to map between stimulus-model features and voxels. This allows novel model-stimuli to be mapped into voxel space, and hence the strength of the model to be assessed by comparing predicted against observed neural activity. Representational Similarity Analysis (RSA) assesses models by testing how well the grand structure of pattern-similarities measured between all pairs of model-stimuli aligns with the same structure computed from neural activity patterns. RSA does not require model fitting, but also does not allow synthesis of neural activity patterns, thereby limiting its applicability. We introduce a new approach, representational similarity-encoding, that builds on the strengths of RSA and robustly enables stimulus-model-based neural encoding without model fitting. The approach therefore sidesteps problems associated with overfitting that notoriously confront any approach requiring parameter estimation (and is consequently low cost computationally), and importantly enables encoding analyses to be incorporated within the wider Representational Similarity Analysis framework. We illustrate this new approach by using it to synthesize and decode fMRI patterns representing the meanings of words, and discuss its potential biological relevance to encoding in semantic memory. Our new similarity-based encoding approach unites the two previously disparate methods of encoding models and RSA, capturing the strengths of both, and enabling similarity-based synthesis of predicted fMRI patterns.


title: Social Exclusion and Selective Memory: How the Need to belong Influences Memory for Social Events

abstract: The need to belong has been forwarded as a pervasive human motive, influencing a range of cognitive, emotional, and behavioral responses. The current research explored the influence of belongingness needs on the selective retention of social information. Just as physical hunger results in selective memory for food-relevant stimuli, it was hypothesized that social hunger, aroused when belongingness needs were unmet, would result in selective memory for socially relevant stimuli. In two studies, the authors used a simulated computer chat room to present brief acceptance or rejection experiences to participants. Participants then read a diary containing both social and individual events. In both, rejection experiences resulted in selective memory for the explicitly social events of the diary. The implications of these results for the existence and consequences of a basic need to belong are discussed.


title: Boost PFC with 100 Hz switching frequency providing output voltage stabilization and compliance with EMC standards

abstract: Consumer and household applications require cheap AC/DC power supplies complying with EMC standards. Passive solutions are bulky and do not provide output voltage stabilization. Active solutions based on power factor correctors (PFCs) with high-frequency switching provide compactness and regulation capability, but are generally expensive due to the need for fast-recovery diodes and complex EMI filters. This paper describes a boost PFC in which the switch is turned on and off only twice per line period. This results in limited di/dt and dv/dt and switch losses, allows use of slow-recovery diodes and avoids the need for heavy EMI filters. In addition, the output voltage can be stabilized in a broad load range by a simple control. The switching unit, including control and protection, can also be integrated in a simple smart-power IC for large-volume applications.


title: A Generalized Linear Threshold Model for Multiple Cascades

abstract: This paper presents a generalized version of the linear threshold model for simulating multiple cascades on a network while allowing nodes to switch between them. The proposed model is shown to be a rapidly mixing Markov chain and the corresponding steady state distribution is used to estimate highly likely states of the cascades' spread in the network. Results on a variety of real world networks demonstrate the high quality of the estimated solution.


title: Continuation Methods and Curriculum Learning for Learning to Rank

abstract: In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank. The basic idea is to design the training process as a learning path across increasingly complex training instances and objective functions. We propose to instantiate continuation methods in Learning to Rank by changing the IR measure to optimize during training, and we present two different curriculum learning strategies to identify easy training examples. Experimental results show that simple continuation methods are more promising than curriculum learning ones since they allow for slightly improving the performance of state-of-the-art λ-MART models and provide a faster convergence speed.


title: Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem

abstract: In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries. Since the nature language queries users input are diverse and multi-faceted, general encoder-decoder models cannot effectively handle low-frequency words and out-of-vocabulary words. We propose a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the source text and extracted query terms. For evaluation and training, we also proposed a semi-automatic method to construct a large-scale dataset about natural language query-keyword query pairs. Experimental results on this dataset demonstrated that our model could achieve better performance than the previous state-of-the-art methods.


Strategy Generation and Evaluation for Meta-Game Playing


title: From genetic privacy to open consent

abstract: Recent advances in high-throughput genomic technologies are showing concrete results in the form of an increasing number of genome-wide association studies and in the publication of comprehensive individual genome-phenome data sets. As a consequence of this flood of information the established concepts of research ethics are stretched to their limits, and issues of privacy, confidentiality and consent for research are being re-examined. Here, we show the feasibility of the co-development of scientific innovation and ethics, using the open-consent framework that was implemented in the Personal Genome Project as an example.


title: Opinion Mining in Twitter How to Make Use of Sarcasm to Enhance Sentiment Analysis

abstract: Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.


title: IoT based smart home with real time E-metering using E-controller

abstract: Technology stemming from web of things is home automation. World's main focus is to make a smart home to take advantage in providing comfort for human life. Web technology is a thing which is growing all the time. Embedded systems with Internet on Things (IoT) is becoming important and necessary part in the current IT industry and exhibiting potential market. Power consumption and efficiency with a user's comfort level is most important issue during this stage while performing various operations. Controller is suitable for power consumption in system and efficiency experiments with size factor. E-controller is represented by combining embedded technology with IoT. Real time information data monitoring of energy consumption is main objective of system. The paper introduces smart home system based on IoT with the help of Web application.


title: Packaging of optoelectronic and RF components with shared elements for dual-mode wireless communications

abstract: The development of a new hybrid packaging method is reported, where the front end radio frequency (RF) element, namely a modified quasi-Yagi antenna, is integrated with an optical transmitter and an optical receiver on single PCB for dual-mode, i.e. RF/free space optical communication. The VCSEL and the pin diode are placed on the antenna directors to share the common metal pads on the Duroid (RT 6010) board. The modules have demonstrated dual-mode wireless communication capability, i.e. RF and free space optics. Though the RF channel induces noise to the optical transmitter/receiver due to electromagnetic coupling between closely-spaced RF and optical circuits, a data rate of 2.5 Gbit/s is demonstrated for the optical channel.


title: Hands for Dexterous Manipulation and Robust Grasping: A Difficult Road Towards Simplicity

abstract: In this paper, an attempt at summarizing the evolution and the state of the art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author's view are the leading ideas and emerging trends is privileged with respect to exhaustiveness of citations. The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology,and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a -minimalistic" attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the new sometimes difficult, problems that are brought about by building and controlling simpler, more practical devices.


title: Aggregate Implications of Corporate Debt Choices

abstract: This article studies the transmission of financial shocks in a model where corporate credit is intermediated via both banks and bond markets. In choosing between bank and bond financing, firms trade-off the greater flexibility of banks in case of financial distress against the lower marginal costs of large bond issuances. I find that, in response to a contraction in bank credit supply, aggregate bond issuance in the corporate sector increases, but not enough to avoid a decline in aggregate borrowing and investment. Keeping leverage constant while retiring bank debt would expose firms to a higher risk of financial distress; they offset this by reducing total borrowing. A calibration of the model to the Great Recession indicates that this precautionary mechanism can account for one-third of the total decline in investment by firms with access to bond markets.


title: Design of power hybrid structures for a N-port power combiner

abstract: This paper deals with a design of power hybrid structures for an N-port power combiner in a primary radar. The primary radar operates on the S-band. This article focuses on the design of high power components for the transmitter part of the primary radar, especially with the power combiner. The individual parts of the power combiner are designed in a CST Microwave Studio.


title: Optimal essential matrix estimation via inlier-set maximization

abstract: In this paper, we extend the globally optimal “rotation space search” method [11] to essential matrix estimation in the presence of feature mismatches or outliers. The problem is formulated as inlier-set cardinality maximization, and solved via branch-and-bound global optimization which searches the entire essential manifold formed by all essential matrices. Our main contributions include an explicit, geometrically meaningful essential manifold parametrization using a 5D direct product space of a solid 2D disk and a solid 3D ball, as well as efficient closed-form bounding functions. Experiments on both synthetic data and real images have confirmed the efficacy of our method. The method is mostly suitable for applications where robustness and accuracy are paramount. It can also be used as a benchmark for method evaluation.


title: AI Characters and Directors for Interactive Computer Games

abstract: We are creating an environment for investigating the role of advanced AI in interactive, story-based computer games. This environment is based on the Unreal Tournament (UT) game engine and the Soar AI engine. Unreal provides a 3D virtual environment, while Soar provides a flexible architecture for developing complex AI characters. This paper describes our progress to date, starting with our game, Haunt 2, which is designed so that complex AI characters will be critical to the success (or failure) of the game. It addresses design issues with constructing a plot for an interactive storytelling environment, creating synthetic characters for that environment, and using a story director agent to tell the story with those characters.


title: A Beacon Interval Shifting Scheme for Interference Mitigation in Body Area Networks

abstract: This paper investigates the issue of interference avoidance in body area networks (BANs). IEEE 802.15 Task Group 6 presented several schemes to reduce such interference, but these schemes are still not proper solutions for BANs. We present a novel distributed TDMA-based beacon interval shifting scheme that reduces interference in the BANs. A design goal of the scheme is to avoid the wakeup period of each BAN coinciding with other networks by employing carrier sensing before a beacon transmission. We analyze the beacon interval shifting scheme and investigate the proper back-off length when the channel is busy. We compare the performance of the proposed scheme with the schemes presented in IEEE 802.15 Task Group 6 using an OMNeT++ simulation. The simulation results show that the proposed scheme has a lower packet loss, energy consumption, and delivery-latency than the schemes of IEEE 802.15 Task Group 6.


The 4 GHz Bandwidth Millimeter-Wave Radar


title: Rapid understanding of scientific paper collections: integrating statistics, text analysis, and visualization

abstract: Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three-phase usability study guided our revisions to ASE and led us to improve the testing methods. © 2012 Wiley Periodicals, Inc.


title: Lipreading using spatiotemporal histogram of oriented gradients

abstract: We propose a visual speech parametrization based on histogram of oriented gradients (HOG) for the task of lipreading from frontal face videos. Inspired by the success of spatiotemporal local binary patterns, the features are designed to capture dynamic information contained in the input video sequence by combining HOG descriptors extracted from three orthogonal planes that span x, y and t axes. We integrate our features into a system based on hidden Markov model (HMM) and show that by utilizing robust and properly tuned parametrization this traditional scheme can outperform recent sophisticated embedding approaches to lipreading. We perform experiments on three different datasets, two of which are publicly available. In order to conduct an unbiased feature comparison, the process of model learning including hyperparameter tuning is as automatized as possible. To this end, we rely heavily on cross validation.


title: Multicultural Issues in Autism

abstract: The professional literature provides ample evidence that individuals with autism exhibit a myriad of unusual social, communication, and behavioral patterns of interactions that present challenges to their families and service providers. However, there is a dearth of quality works on multicultural issues regarding autistic spectrum disorders. In this article, we explore issues surrounding autism and multiculturalism, with the intent not to provide answers but to raise questions for further examination. We focus our discussions on two primary issues: autism within cultural groups and multicultural family adaptation based on the framework of pluralistic societies in which some cultural groups are a minority within the dominant culture. We found differences in prevalence rates across races for autism and little information regarding how multicultural families adapt to raising a child with autism. Further, students with multicultural backgrounds and autism are challenged on at least four dimensions: communication, social skills, behavioral repertoires, and culture. Future research in these areas is clearly warranted.


Ethics in the Design of Automated Vehicles: The AVEthics project


title: DOLLÁR, et al.: THE FASTEST PEDESTRIAN DETECTOR IN THE WEST 1 The Fastest Pedestrian Detector in the West

abstract: We demonstrate a multiscale pedestrian detector operating in near real time ( 6 fps on 640x480 images) with state-of-the-art detection performance. The computational bottleneck of many modern detectors is the construction of an image pyramid, typically sampled at 8-16 scales per octave, and associated feature computations at each scale. We propose a technique to avoid constructing such a finely sampled image pyramid without sacrificing performance: our key insight is that for a broad family of features, including gradient histograms, the feature responses computed at a single scale can be used to approximate feature responses at nearby scales. The approximation is accurate within an entire scale octave. This allows us to decouple the sampling of the image pyramid from the sampling of detection scales. Overall, our approximation yields a speedup of 10-100 times over competing methods with only a minor loss in detection accuracy of about 1-2% on the Caltech Pedestrian dataset across a wide range of evaluation settings. The results are confirmed on three additional datasets (INRIA, ETH, and TUD-Brussels) where our method always scores within a few percent of the state-of-the-art while being 1-2 orders of magnitude faster. The approach is general and should be widely applicable.


A Ship Target Location and Mask Generation Algorithms Base on Mask RCNN


title: Centrally Banked Cryptocurrencies

abstract: Current cryptocurrencies, starting with Bitcoin, build a decentralized blockchain-based transaction ledger, main- tained through proofs-of-work that also serve to generate a monetary supply. Such decentralization has benefits, such as independence from national political control, but also significant limitations in terms of computational costs and scalability. We introduce RSCoin, a cryptocurrency framework in which central banks maintain complete control over the monetary supply, but rely on a distributed set of authorities, or mintettes, to prevent double-spending. While monetary policy is centralized, RSCoin still provides strong transparency and auditability guarantees. We demonstrate, both theoretically and experimentally, the benefits of a modest degree of centralization, such as the elimination of wasteful hashing and a scalable system for avoiding double- spending attacks.