title: Motor Control Exercises, Sling Exercises, and General Exercises for Patients With Chronic Low Back Pain: A Randomized Controlled Trial With 1-Year Follow-up

abstract: Background Exercise benefits patients with chronic nonspecific low back pain; however, the most effective type of exercise remains unknown. ::: ::: Objective This study compared outcomes after motor control exercises, sling exercises, and general exercises for low back pain. ::: ::: Design This was a randomized controlled trial with a 1-year follow-up. ::: ::: Setting The study was conducted in a primary care setting in Norway. ::: ::: Patients The participants were patients with chronic nonspecific low back pain (n=109). ::: ::: Interventions The interventions in this study were low-load motor control exercises, high-load sling exercises, or general exercises, all delivered by experienced physical therapists, once a week for 8 weeks. ::: ::: Measurements The primary outcome measure was pain reported on the Numeric Pain Rating Scale after treatment and at a 1-year follow-up. Secondary outcome measures were self-reported activity limitation (assessed with the Oswestry Disability Index), clinically examined function (assessed with the Fingertip-to-Floor Test), and fear-avoidance beliefs after intervention. ::: ::: Results The postintervention assessment showed no significant differences among groups with respect to pain (overall group difference) or any of the outcome measures. Mean (95% confidence interval) group differences for pain reduction after treatment and after 1 year were 0.3 (−0.7 to 1.3) and 0.4 (−0.7 to 1.4) for motor control exercises versus sling exercises, 0.7 (−0.6 to 2.0) and 0.3 (−0.8 to 1.4) for sling exercises versus general exercises, and 1.0 (−0.1 to 2.0) and 0.7 (−0.3 to 1.7) for motor control exercises versus general exercises. ::: ::: Limitations The nature of the interventions made blinding impossible. ::: ::: Conclusions This study gave no evidence that 8 treatments with individually instructed motor control exercises or sling exercises were superior to general exercises for chronic low back pain.


title: Digital LDO with analog-assisted dynamic reference correction for fast and accurate load regulation

abstract: Low-dropout voltage regulators (LDOs) have been extensively used on-chip to supply voltage for various circuit blocks. Digital LDOs (DLDO) have recently attracted circuit designers for their low voltage operating capability and load current scalability. Existing DLDO techniques suffer from either poor transient performance due to slow digital control loop or poor DC load regulation due to low loop gain. A dual-loop architecture to improve the DC load regulation and transient performance is proposed in this work. The proposed regulator uses a fast control loop for improved transient response and an analog assisted dynamic reference correction loop for an improved DC load regulation. The design achieved a DC load regulation of 0.005mV/mA and a settling time of 139ns while regulating loads up to 200mA. The proposed DLDO is designed in 28nm FD-SOI technology with a 0.027mm2 active area.


title: Systematic review of universal resilience interventions targeting child and adolescent mental health in the school setting: review protocol

abstract: BACKGROUND ::: The mental health of children and adolescents is a key area of health concern internationally. Previous empirical studies suggest that resilience may act as a protective mechanism towards the development of mental health problems. Resilience refers to the ability to employ a collection of protective factors to return to or maintain positive mental health following disadvantage or adversity. Schools represent a potential setting within which protective factors of all children and adolescents may be fostered through resilience-focussed interventions. Despite this potential, limited research has investigated the effectiveness of universal school-based resilience-focussed interventions on mental health outcomes in children and adolescents. The objective of the present review is to assess the effects of universal school-based resilience-focussed interventions, relative to a comparison group, on mental health outcomes in children and adolescents. ::: ::: ::: METHODS/DESIGN ::: Eligible studies will be randomised (including cluster-randomised) controlled trials of universal interventions explicitly described as resilience-focussed or comprising strategies to strengthen a minimum of three internal protective factors, targeting children aged 5 to 18 years, implemented within schools, and reporting a mental health outcome. Screening for studies will be conducted across six electronic databases: MEDLINE, PsycINFO, Educational Resources Information Center (ERIC), Excerpta Medica database (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Cochrane Central Register of Controlled Trials (CENTRAL). Two reviewers will retrieve eligible articles, assess risk of bias, and extract data. Where studies are sufficiently homogenous and reported outcomes are amenable for pooled synthesis, meta-analysis will be performed. Narrative description will be used to synthesise trial outcome data where data cannot be combined or heterogeneity exists. ::: ::: ::: DISCUSSION ::: This review will aid in building an evidence base for the effectiveness of universal school-based resilience-focussed interventions and in doing so provide an opportunity to better inform the development of interventions to potentially prevent mental health problems in child and adolescent populations. ::: ::: ::: SYSTEMATIC REVIEW REGISTRATION ::: PROSPERO CRD42015025908.


title: A Zero-Voltage-Switching Contour-Based Outphasing Power Amplifier

abstract: A parallel class-E modified Chireix outphasing power amplifier (PA) that can maintain high efficiency across a wide output power range is presented. The architecture presented essentially implements a zero voltage switching (ZVS) contour-based PA using a modified Chireix outphasing structure. It utilizes the inherent load modulation present in the outphasing scheme and combines it with duty cycle modulation to satisfy ZVS conditions over a wide range of output powers. Thus, a ZVS contour-based PA is realized, and the amplifier maintains high efficiency for about 9-dB back-off of output power. Beyond the 9-dB range, simple outphasing is performed to extend the dynamic range up to about 30 dB. The proposed PA, implemented using discrete components on an FR4 printed circuit board, achieves a dynamic range of 30 dB with a peak power of 29 dBm and a peak drain efficiency of 65% at about 6-dB back-off from the peak output power at 100 MHz from a 3-V supply.


title: A Dynamic Programming Algorithm for Robust Runway Scheduling

abstract: An algorithm for generating schedules of airport runway operations that are robust to perturbations caused by system uncertainty is presented. The algorithm computes a tradeoff curve between runway throughput and the probability that random deviations of aircraft from the schedule violate system constraints and require intervention from air traffic controllers. The algorithm accommodates various operational constraints imposed by the terminal-area system such as minimum separation requirements between successive aircraft, earliest and latest times for each aircraft, precedence constraints among aircraft and the limited flexibility in deviating from the first-come-first-served (FCFS) order afforded to air traffic controllers (a concept known as Constrained Position Shifting). When the maximum allowable number of position shifts from the FCFS order is bounded by a constant, the complexity of the algorithm is O(n(L/isin)3), where n is the number of aircraft, L is largest difference between the latest and earliest arrival time over all aircraft, and e is the desired output accuracy.


title: Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest

abstract: This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.


title: DiFacto: Distributed Factorization Machines

abstract: Factorization Machines offer good performance and useful embeddings of data. However, they are costly to scale to large amounts of data and large numbers of features. In this paper we describe DiFacto, which uses a refined Factorization Machine model with sparse memory adaptive constraints and frequency adaptive regularization. We show how to distribute DiFacto over multiple machines using the Parameter Server framework by computing distributed subgradients on minibatches asynchronously. We analyze its convergence and demonstrate its efficiency in computational advertising datasets with billions examples and features.


title: B-CoC: A Blockchain-based Chain of Custody for Evidences Management in Digital Forensics

abstract: One of the main issues in digital forensics is the management of evidences. From the time of evidence collection until the time of their exploitation in a legal court, evidences may be accessed by multiple parties involved in the investigation that take temporary their ownership. This process, called Chain of Custody (CoC), must ensure that evidences are not altered during the investigation, despite multiple entities owned them, in order to be admissible in a legal court. Currently digital evidences CoC is managed entirely manually with entities involved in the chain required to fill in documents accompanying the evidence. In this paper, we propose a Blockchain-based Chain of Custody (B-CoC) to dematerialize the CoC process guaranteeing auditable integrity of the collected evidences and traceability of owners. We developed a prototype of B-CoC based on Ethereum and we evaluated its performance.


title: A Bilingual Scene-to-Speech Mobile Based Application

abstract: Scene-to-Speech (STS) is the process of recognizing visual objects in a picture or a video to say aloud a descriptive text that represents the scene. The recent advancement in convolution neural network (CNN), a deep learning feed-forward artificial neural network, enables us to recognize objects on mobile handled devices in real-time. Several applications have been developed to recognize objects in scenes and speak loud their relevant descriptions. However, the Arabic language is not fully supported. In this paper, we propose a bilingual mobile based application that captures video scenes and processes their content to recognize objects in real-time. The mobile application will then speak loud, in English or Arabic language, the description of the captured scene. The mobile application can be extended to further support eLearning technologies and edutainment games. People with visual impairments (VI), such as people with low vision and totally blind people, can benefit from the application to know about their surroundings. We conducted an elementary study about the usage of the mobile application with people with VI and they expressed their interest to use it in their daily lives.


title: Multiscale Deep Alternative Neural Network for Large-Scale Video Classification

abstract: With the rapid increase in the amount of multimedia data, video classification has become a demanding and challenging research topic. Compared with image classification, video classification requires mapping a video that contains hundreds of frames to semantic tags, which poses many challenges to the direct use of advanced models originally designed for image-oriented tasks. On the other hand, continuous frames in a video also give us more visual clues that we can leverage to achieve better classification. One of the most important clues is the context in the spatiotemporal domain. In this paper, we introduce the multiscale deep alternative neural network (DANN), a novel architecture combining the strengths of both convolutional neural network and recurrent neural networks to achieve a deep network that can collect rich context hierarchies for video classification. In particular, the DANN is stacked with alternative layers, each of which consists of a volumetric convolutional layer followed by a recurrent layer. The former acts as a local feature learner, whereas the latter is used to collect contexts. Compared with popular deep feed-forward neural networks, the DANN learns local features and their contexts from the very beginning. This setting enables preserving context evolutions, which we show to be essential for improving the accuracy of video classification. To release the full potential of the DANN, we develop a deeper version with stochastic-layer skip-connections and construct a multiscale DANN to incorporate contexts at different scales. We show how to apply the multiscale DANN for video classification with carefully designed configurations in terms of both input–output settings and training–testing methods. The DANN is shown to be robust to not only human-centric videos, but also natural videos. As there are few large-scale natural disaster video datasets, we construct a new large-scale one and make it publicly available. Experiments on four datasets show the effectiveness of our method for both human actions and natural events.


title: Mutual Fund Performance: Luck or Skill?

abstract: I. INTRODUCTION Fundamental finance principles, along with common sense, dictate that investors take account of both risk and return, yielding some measure of positive risk-adjusted returns that also account for all transaction costs and/or management fees. In other words, investors seek positive alphas ([alpha]'s), i.e., risk and cost-adjusted positive returns. Many investors turn to professionals in search of such gains. According to the Investment Company Fact Book (2011), almost 30% of world's wealth is invested in mutual funds: $25 trillion of the $85 trillion total world-wide investible wealth. About 44% of individual wealth in the U.S., $12 trillion, is invested in mutual funds. Mutual funds charge management fees that appear to reduce total return to investors. Many mutual funds either do not beat the market or fail to outperform their chosen benchmarks. Indeed, while Fama and French (2010) note "the aggregate portfolio of actively managed U.S. equity mutual funds is close to the market portfolio, but the high costs of active management show up intact as lower returns to investors," they document existence of genuine inferior and superior performance at the negative and positive extremes. Results herein mirror those of Fama and French (2010), though a probability approach leads to the results herein versus the bootstrap method used by them. Experiments involving many successive trials with multiple subjects, demonstrate a positive probability that a subject will win several times in a row merely due to luck, not skill. For example, while the probability that a person flips a coin and ends up with 10 heads in a row is less than one in a thousand (1/1032 = 0.098%), such strings of luck do occur, and when they do, it is not skill on the part of the person flipping the coin. Some of the thousands of investment funds may end up the "winners" for prolonged periods of time. Naturally, investors need to know whether streaks of superior performance are due to skill. The analysis herein demonstrates that such streaks exist within the family of mutual funds and are due to skill, not luck. The main contention of this paper relies on detecting the existence of skill with the generalized binomial distribution, using an established analogy with winning in sports. Money managers are usually well-educated and even skilled, as are professional athletes. Competition alone in sports dictates that not all professional teams can win; though as noted in more detail below, the most highly skilled teams appear to win more often. In contrast, a good deal of prior evidence indicates that a majority of actively managed mutual funds fail to outperform passively managed index funds. According to Berk and Green (2004), the apparent lack of investment skill has "led researchers to raise questions about the rationality of investors who place money with active managers despite their apparent inability to outperform passive strategies and who appear to devote considerable resources to evaluating past performance of managers." This paper contributes to this important debate about luck vs. skill. Using a novel statistical approach based on the Generalized Binomial Distribution (Drezner and Farnum, 1993), this paper investigates whether some mutual funds provide positive [alpha]'s over time as a result of skill. The approach demonstrates, in contrast to a large degree of the extant evidence, that skilled fund managers do exist--persistence in superior performance cannot be attributed to luck. This paper's results suggest important practical implications. The lack of convincing evidence that mutual funds provide superior performance dramatically influences the investing habits of individuals. The lack of such evidence along with such straightforward factors as the ease of trading ETFs and lower management costs for ETFs have most likely led to the growth of ETFs and a relative decline in mutual funds in recent years. …


title: Optical Communications for High-Altitude Platforms

abstract: This paper contains a review of technologies, theoretical studies, and experimental field trials for optical communications from and to high-altitude platforms (HAPs). We discuss the pointing, acquisition, and tracking of laser terminals and describe how laser beams with low divergence can be used to transmit data at multi-Gigabits per second. Investigating the influence of the atmosphere, background light, and flight qualification requirements on system design, we explain why the data rates in free-space optical communications are still significantly below those possible in today's terrestrial fiber-based systems. Techniques like forward-error correction, adaptive optics, and diversity reception are discussed. Such measures help to increase the data rate or link distance while keeping the bit error ratio and outage probability of the optical HAP communication system low.


title: Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

abstract: Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.


title: Applying reinforcement learning to Tetris

abstract: This paper investigates the possible application of reinforcement learning to Tetris. The author investigates the background of Tetris, and qualifies it in a mathematical context. The author discusses reinforcement learning, and considers historically successful applications of it. Finally the author discusses considerations surrounding implementation.


title: A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network

abstract: Recently many researches have shown that word embeddings are able to represent information from word related contexts or its nearest neighborhood words, and thus are applied in many NLP tasks successfully. In this paper, we propose convolutional neural network model to extent word embeddings to mention/antecedent representation. These representations are obtained through convoluting neighboring word embeddings and other contextual information for coreference resolution. We evaluate our system on the English portion of the CoNLL 2012 Shared Task dataset and show that the proposed system achieves a competitive performance compared with the state-of-the-art approaches. We also show that our proposed model especially improves the coreference resolution of long spans significantly.


Job Scheduling in Multiprogrammed Parallel Systems


title: Robotic origami folding

abstract: Origami, the art of paper sculpture, is a fresh challenge for the field of robotic manipulation, and provides a concrete example of the many difficulties and general manipulation problems faced in robotics. This paper describes our initial exploration, and highlights key problems in the manipulation, modeling, and design of foldable structures. Results include the design of the first origami-folding robot, a complete fold-sequence planner for a simple class of origami, and analysis of the kinematics of more complicated folds, including the common paper shopping bag.


title: Implementing atomic actions on decentralized data

abstract: Synchronization of accesses to shared data and recovering the state of such data in the case of failures are really two aspects of the same problem--implementing atomic actions on a related set of data items. In this paper a mechanism that solves both problems simultaneously in a way that is compatible with requirements of decentralized systems is described. In particular, the correct construction and execution of a new atomic action can be accomplished without knowledge of all other atomic actions in the system that might execute concurrently. Further, the mechanisms degrade gracefully if parts of the system fail: only those atomic actions that require resources in failed parts of the system are prevented from executing, and there is no single coordinator that can fail and bring down the whole system.


title: Enhancing academic performance and social and emotional competence with the RULER feeling words curriculum

abstract: Abstract A pre- and post-test quasi-experimental design was used to test the impact of a 30-week, theoretically-based social and emotional learning (SEL) curriculum, The RULER Feeling Words Curriculum (“RULER”), on the academic performance and social and emotional competence of 5th and 6th grade students ( N = 273) in fifteen classrooms in three schools. Academic performance was assessed by report card grades. Social and emotional competence was assessed with teacher reports of student behavior. Students in classrooms integrating RULER had higher year-end grades and higher teacher ratings of social and emotional competence (e.g., leadership, social skills, and study skills) compared to students in the comparison group. This study provides preliminary empirical evidence that SEL programs like RULER improve important student outcomes.


title: Performance Analyses of 38 kWe Turbo‐Machine Unit for Space Reactor Power Systems

abstract: This paper developed a design and investigated the performance of 38 kWe turbo‐machine unit for space nuclear reactor power systems with Closed Brayton Cycle (CBC) energy conversion. The compressor and turbine of this unit are scaled versions of the NASA's BRU developed in the sixties and seventies. The performance results of turbo‐machine unit are calculated for rotational speed up to 45 krpm, variable reactor thermal power and system pressure, and fixed turbine and compressor inlet temperatures of 1144 K and 400 K. The analyses used a detailed turbo‐machine model developed at the University of New Mexico that accounts for the various energy losses in the compressor and turbine and the effect of compressibility of the He‐Xe (40 mole/g) working fluid with increased flow rate. The model also accounts for the changes in the physical and transport properties of the working fluid with temperature and pressure. Results show that a unit efficiency of 24.5% is achievable at rotation speed of 45 krpm and system p...


title: DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal

abstract: Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.


title: Design of Intercell Transformers for High-Power Multicell Interleaved Flyback Converter

abstract: This paper presents the developments and the results of an analytic method elaborated to design isolated intercell transformers. These particular magnetic components are intended for multicell interleaved flyback converters recently proposed and well-suited to low-voltage, high-power applications. A specific 2-D model to evaluate the winding losses is included in the design process. The results, obtained for standard core shapes, allow defining the ranges of the main parameters, number of cells, and switching frequency, required for future realizations.


title: Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

abstract: Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values. We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.


title: Quantum Algorithm for Spectral Measurement with Lower Gate Count

abstract: We present two techniques that can greatly reduce the number of gates required to realize an energy measurement, with application to ground state preparation in quantum simulations. The first technique realizes that to prepare the ground state of some Hamiltonian, it is not necessary to implement the time-evolution operator: any unitary operator which is a function of the Hamiltonian will do. We propose one such unitary operator which can be implemented exactly, circumventing any Taylor or Trotter approximation errors. The second technique is tailored to lattice models, and is targeted at reducing the use of generic single-qubit rotations, which are very expensive to produce by standard fault tolerant techniques. In particular, the number of generic single-qubit rotations used by our method scales with the number of parameters in the Hamiltonian, which contrasts with a growth proportional to the lattice size required by other techniques.


title: Generalized Autoregressive Conditional Heteroskedasticity

abstract: The present paper proposes a generalization of the canonical AutoRegressive Conditional Heteroskedasticity (ARCH) model by extending the conditional variance equation toward past conditional variances. The stationarity conditions and autocorrelation structure of the Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model are derived. Using an empirical example of uncertainty of the inflation rate the paper demonstrates that the GARCH model provides a better fit and a more plausible learning mechanism than the ARCH model.


title: Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices

abstract: In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches. In this work, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. Our system achieves these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tag's mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tag's moving trajectories and its locations, relaxing the assumption of knowing tag's track function in advance. We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with a millimeter accuracy to a median of 5mm and 7.29mm using linear and circular track respectively. In our year- long large scale baggage sortation systems deployed in two airports, our results from real deployments show that Tagoram can achieve a centimeter-level accuracy to a median of 6.35cm in these real deployments.


title: Less Hashing, Same Performance: Building a Better Bloom Filter

abstract: A standard technique from the hashing literature is to use two hash functions h 1 (x) and h 2 (x) to simulate additional hash functions of the form g i (x) = h 1 (x) + ih 2 (x). We demonstrate that this technique can be usefully applied to Bloom filters and related data Structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.


title: Credit Risk Models III: Reconciliation Reduced - Structural Models

abstract: In recent years, some papers hav tried to bridge the gap between the two main approaches in credit risk modelling: structural and reduced form models. Based on incomplete information versions of standard structural models, they are able to obtain reduced form models in which the intensity of default is not given exogenously but determined endogenously within the model and it is a function of the firm's characteristics and the level of informtion that investors posses. They key element to link both approaches lies in the model's information assumptions. Using a specification of a structural model where investors do not have complete information about the dynamics of the processes which trigger the firm's default, these models derive a cumulative rate of default consistent with a reduced form model. This paper pretends to be an introduction to this literature, providing some of basic insights of the modelling structure and the main conclusion and results.


title: A Missing Information Loss function for implicit feedback datasets

abstract: Latent factor models for Recommender Systems with implicit feedback typically treat unobserved user-item interactions (i.e. missing information) as negative feedback. This is frequently done either through negative sampling (point--wise loss) or with a ranking loss function (pair-- or list--wise estimation). Since a zero preference recommendation is a valid solution for most common objective functions, regarding unknown values as actual zeros results in users having a zero preference recommendation for most of the available items. In this paper we propose a novel objective function, the \emph{Missing Information Loss} (MIL), that explicitly forbids treating unobserved user-item interactions as positive or negative feedback. We apply this loss to both traditional Matrix Factorization and user--based Denoising Autoencoder, and compare it with other established objective functions such as cross-entropy (both point- and pair-wise) or the recently proposed multinomial log-likelihood. MIL achieves competitive performance in ranking-aware metrics when applied to three datasets. Furthermore, we show that such a relevance in the recommendation is obtained while displaying popular items less frequently (up to a $20 \%$ decrease with respect to the best competing method). This debiasing from the recommendation of popular items favours the appearance of infrequent items (up to a $50 \%$ increase of long-tail recommendations), a valuable feature for Recommender Systems with a large catalogue of products.


title: Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process

abstract: Additive manufacturing (AM) enables flexible part geometry and functionality, and reduces product development life cycle by direct layer-wise fabrication from CAD files. In the last decade, great achievements are made on AM materials, machines, processes, etc. However, the quality of the AM parts is still questionable for industrial specifications. On the one hand, AM part quality variables can be either quantitative, such as dimensional accuracy, or qualitative, such as binary indicators for voids, missing features, or surface roughness. On the other hand, both offline process setting variables and functional in situ process variables can be measured and modeled with both quantitative and qualitative (QQ) quality response variables. In this paper, the QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models. The modeling of these in situ process variables provides the basis for real-time monitoring and control for AM processes. Simulation studies and experimental data from a fused deposition modeling process are performed to demonstrate the effectiveness of the proposed method. Note to Practitioners —Additive manufacturing (AM) processes have attracted much attention and showed many advantages over the traditional subtractive manufacturing processes. However, the product quality issues make AM intractable for high-quality parts in industrial applications. This paper aims to address the quality issues by modeling both quantitative quality variables, such as dimensional accuracy, and qualitative quality variables, such as the binary (go/no-go) indicator for surface conditions. Both offline process setting variables and in situ process variables are used in the model as predictors. Such a model is important for systematically quality evaluation of AM parts, and provides the basis for future process monitoring and control. The merits of the proposed method are demonstrated with simulation studies and a case study in a fused deposition modeling process.


title: Lithium-Ion Battery Aging Experiments at Subzero Temperatures and Model Development for Capacity Fade Estimation

abstract: Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at high-temperature operations. Even vehicle maneuvers at low temperatures $(T contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered low-temperature degradation. This is due to the lack of studies on aging of Li-ion batteries at subzero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP04 Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests ( $-\mbox{20}\ ^\circ\mbox{C}$ and 25 °C). The calendar test was carried out on one single battery at –20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C.


title: Analysis and simulation of power inverter with load variation for induction heating applications

abstract: All induction heating applied systems are developed using electromagnetic induction. Electromagnetic induction refers to the phenomenon by which electric current is generated in a closed circuit by the fluctuation of current in another circuit placed next to it. The basic principle of induction heating is that AC current flowing through a primarily circuit induces a current in the load (the secondary circuit) located near it and heating the load. The intent of this presentation is to present the details of half-bridge power inverter via a comprehensive analysis with operation equations of the circuit and their solving using specific sotware. An alternative to the use of circuit-oriented simulators for study of these circuits operating is to describe the circuit and the controller by means of differential and algebraic equations. We must develop the equations for all possible states in which the circuit may operate. These algebraic/differential equations can be solved by using of software packages specifically designed for this purpose that provide a choice of integration routines, graphical output, and so on.


title: A new approach to unwrap a 3-D fingerprint to a 2-D rolled equivalent fingerprint

abstract: For many years, fingerprints have been captured by pressing a finger against a paper or hard surface. This touch-based fingerprint acquisition introduces some problems such as distortions and deformations in the acquired images, which arise due to the contact of the fingerprint surface with the sensor platen, and degrades the recognition performance. A new touch-less fingerprint technology has been recently introduced to the market, which can address the problems with the contact-based fingerprint systems. In this paper, we propose a new algorithm for unwrapping the acquired 3-D scan of the subject's finger into a 2-D rolled equivalent image. Therefore, The resulting image can be matched with the conventional 2-D scans; it also can be used for matching unwrapped 3-D fingerprints among themselves with the 2-D fingerprint matching algorithms. The algorithm is based on curvature analysis of the 3-D surface. The quality of the resulting image is evaluated and analyzed using NIST fingerprint image software.


title: Online aggregation

abstract: Aggregation in traditional database systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements. These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.


title: Towards Process-driven Mobile Data Collection Applications - Requirements, Challenges, Lessons Learned.

abstract: In application domains like healthcare, psychology and e-learning, data collection is based on specifically tailored paper & pencil questionnaires. Usually, such a paper-based data collection is accomplished by a massive workload regarding the processing, analysis, and evaluation of the data collected. To relieve domain experts from these manual tasks and to increase the efficiency of the data collection process, we developed a generic approach for realizing process-driven smart mobile device applications based on process management technology. According to this approach, the logic of a questionnaire is described in terms of an explicit process model whose enactment is driven by a generic process engine. Our goal is to demonstrate that such a process-aware design of mobile business applications is useful with respect to mobile data collection. Hence, we developed a generic architecture comprising the main components of mobile data collection applications. Furthermore, we used these components for developing mobile electronic questionnaires for psychological studies. The paper presents the challenges identified in this context and discusses the lessons learned. Overall, process management technology offers promising perspectives for developing mobile business applications at a high level of abstraction.


title: Techniques for movie content analysis and skimming: tutorial and overview on video abstraction techniques

abstract: With the proliferation of digital video, video summarization and skimming has become an indispensable tool of any practical video content management system. This paper provides a tutorial on the existing abstraction work for generic videos and presents state-of-the-art techniques for feature film skimming. The paper also describes the authors' recent work on movie skimming using audiovisual tempo analysis and specific cinematic rules. With the maturity of the movie genre classification, content understanding and video abstraction techniques, an automatic movie content analysis system that facilitates navigation, browsing, and search of desired movie content is possible in the near future


title: Handwritten Devanagari Character Recognition Using Gradient Features

abstract: We describe novel methods of feature extraction for recognition of single isolated Devanagari character images. Our approach is flexible in that the same algorithms can be used, without modification, for feature extraction in a variety of OCR problems. These include handwritten, machine-print, grayscale, and binary and low-resolution character recognition. We use the gradient representation as the basis for extraction of features. These algorithms require a few simple arithmetic operations per image pixel which ma kes them suitable for real-time applications. Our dataset consists of 200 samples of each of 36 basic Devanagari characters, which are collected from 20 different writers each contributing to write 10 samples of each of 36 characters. Thus we have used total 7200 character samples. All sample images of Devanagari characters used are normalized to 90*90 pixel sizes. A description of the algorithm and experiment with our data set is presented in this paper. Experimental results using Support Vector Machines (SVM) are presented. Our results demonstrate high performance of these features with cross validation accuracy of 94%. Keywords—Isolated Handwritten Devanagari Character Recognition, Gradient Features, Gradient Feature Extraction, SVM Classifier.


title: Recent Positive Selection in Genes of the Mammalian Epidermal Differentiation Complex Locus

abstract: The Epidermal Differentiation Complex (EDC) is the most rapidly evolving locus in the human genome compared to that of the chimpanzee. Yet the EDC genes that are undergoing positive selection across mammals and in humans are not known. We sought to identify the positively selected genetic variants and determine the evolutionary events of the EDC using mammalian-wide and clade-specific branch- and branch-site likelihood ratio tests and a genetic algorithm branch test. Significant non-synonymous substitutions were found in filaggrin, SPRR4, LELP1, and S100A2 genes across 14 mammals. By contrast, we identified recent positive selection in SPRR4 in primates. Additionally, the genetic algorithm branch test discovered lineage-specific evolution for distinct EDC genes occurring in each of the nodes in the 14-mammal phylogenetic tree. Multiple instances of positive selection for FLG, TCHHL1, SPRR4, LELP1, and S100A2 were noted among the primate branch nodes. Branch-site likelihood ratio tests further revealed positive selection in specific sites in SPRR4, LELP1, filaggrin, and repetin across 14 mammals. However, in addition to continuous evolution of SPRR4, site-specific positive selection was also found in S100A11, KPRP, SPRR1A, S100A7L2, and S100A3 in primates and filaggrin, filaggrin2, and S100A8 in great apes. Very recent human positive selection was identified in the filaggrin2 L41 site that was present in Neanderthal. Together, our results identifying recent positive selection in distinct EDC genes reveal an underappreciated evolution of epidermal skin barrier function in primates and humans.


title: SciMAT : A new science mapping analysis software tool

abstract: This article presents a new open-source software tool, SciMAT, which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis. © 2012 Wiley Periodicals, Inc.


A NEW ONE ROUND IMAGE ENCRYPTION ALGORITHM BASED ON MULTIPLE CHAOTIC SYSTEMS


title: Constructing an Interactive Natural Language Interface for Relational Databases

abstract: Natural language has been the holy grail of query interface designers, but has generally been considered too hard to work with, except in limited specific circumstances. In this paper, we describe the architecture of an interactive natural language query interface for relational databases. Through a carefully limited interaction with the user, we are able to correctly interpret complex natural language queries, in a generic manner across a range of domains. By these means, a logically complex English language sentence is correctly translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and can be evaluated against an RDBMS. We have constructed a system, NaLIR (Natural Language Interface for Relational databases), embodying these ideas. Our experimental assessment, through user studies, demonstrates that NaLIR is good enough to be usable in practice: even naive users are able to specify quite complex ad-hoc queries.


title: Household Energy Consumption Segmentation Using Hourly Data

abstract: The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.


title: Video reports as a novel alternate assessment in the undergraduate chemistry laboratory

abstract: The increased use of video capable cellular phones to document everyday life presents educators with an exciting opportunity to extend this capability into the introductory laboratory. The study assessed whether students enrolled in a southeastern U.S. university's first-year laboratory course retained technical information at a higher rate after creating a technique video. These videos were created on hand-held video capable devices that students owned prior to enrolling in the course, eliminating additional cost to students. Pre-/post-test analysis (N = 509) was performed to determine short- and long-term learning gains regarding reporting the volume of graduated glassware to the proper number of significant figures. Though both groups used various graduated glassware throughout the term, chi-square analysis showed that students who created a video detailing use of a Mohr pipet reported the volume of graduated glassware correctly on the final exam and laboratory practical at a significantly higher rate than those students who received only verbal instruction on the technique.


title: Using Geometry to Detect Grasp Poses in 3D Point Clouds

abstract: This paper proposes a new approach to using machine learning to detect grasp poses on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand poses that are expected to be good grasps. There are two main contributions. First, we identify a set of necessary conditions on the geometry of a grasp that can be used to generate a set of grasp hypotheses. This helps focus grasp detection away from regions where no grasp can exist. Second, we show how geometric grasp conditions can be used to generate labeled datasets for the purpose of training the machine learning algorithm. This enables us to generate large amounts of training data and it grounds our training labels in grasp mechanics. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at http://wiki.ros.org/agile_grasp.


title: Ozy: A General Orchestration Container

abstract: Service-Oriented Computing is a paradigm that uses services as building blocks for building distributed applications. The primary motivation for orchestrating services in the cloud used to be distributed business processes, which drove the standardization of the Business Process Execution Language (BPEL) and its central notion that a service is a business process. In recent years, there has been a transition towards other motivations for orchestrating services in the cloud, e.g., XaaS, RMAD. Although it is theoretically possible to make all of those services into WSDL/SOAP services, it would be too complicated and costly for industry adoption. Therefore, the central notion that a service is a business process is too restrictive. Instead, we view a service as a technology neutral, loosely coupled, location transparent procedure. With these ideas in mind, we introduce a new approach to services orchestration: Ozy, a general orchestration container. We define this new approach in terms of existing technology, and we show that the Ozy container relaxes many traditional constraints and allows for simpler, more feature-rich applications.


title: A Next-Best-View System for Autonomous 3-D Object Reconstruction

abstract: The focus of this paper is to design and implement a system capable of automatically reconstructing a prototype 3D model from a minimum number of range images of an object. Given an ideal 3D object model, the system iteratively renders range and intensity images of the model from a specified position, assimilates the range information into a prototype model, and determines the sensor pose (position and orientation) from which an optimal amount of previously unrecorded information may be acquired. Reconstruction is terminated when the model meets a given threshold of accuracy. Such a system has applications in the context of robot navigation, manufacturing, or hazardous materials handling. The system has been tested successfully on several synthetic data models, and each set of results was found to be reasonably consistent with an intuitive human search. The number of views necessary to reconstruct an adequate 3D prototype depends on the complexity of the object or scene and the initial data collected. The prototype models which the system recovers compare well with the ideal models.


title: SCADA honeypots: An in-depth analysis of Conpot

abstract: Supervisory Control and Data Acquisition (SCADA) honeypots are key tools not only for determining threats which pertain to SCADA devices in the wild, but also for early detection of potential malicious tampering within a SCADA device network. An analysis of one such SCADA honeypot, Conpot, is conducted to determine its viability as an effective SCADA emulating device. A long-term analysis is conducted and a simple scoring mechanism leveraged to evaluate the Conpot honeypot.


title: Automated Algorithm Configuration and Parameter Tuning

abstract: The use of automated configuration and parameter tuning techniques plays an increasingly important role in the design, evaluation and application of high-performance algorithms for difficult computational problems. This chapter provides an introduction to these techniques and gives an overview of three families of procedures for automatically optimising the performance of parameterised algorithms. Racing procedures iteratively evaluate parameter settings on problem instances from a given set and use statistical hypothesis tests to eliminate candidate configurations that are significantly outperformed by other configurations. ParamILS uses a powerful stochastic local search method to search within potentially vast spaces of candidate configurations of a given algorithm. And finally, sequential model-based optimisation (SMBO) methods build a response surface model that relates parameter settings to performance, and use this model to iteratively identify promising settings. We also briefly survey other algorithm configuration and parameter tuning procedures, as well as related approaches, such as instance-based algorithm selection and configuration.


title: LLC resonant converter for 48 V to 0.9 V VRM

abstract: Abstrarf48V to 0.9V VRM using LLC resonant converter is presented. ZVS is realized for switches and the converter can operate at higher frequency. How to reduce primary current amplitude by reducing power factor angle is explored. The effect of secondary leakage inductance is studied and FEA method is used to find the optimal transformer winding configuration. The resonant inductor can he integrated with transformer primary leakage inductance, which reduces the total magnetic component counts. A prototype 0148V to 0.9V/50A VRM is built to verify the result.


title: An evolutionary approach for the tuning of a chess evaluation function using population dynamics

abstract: Using the game of chess, we propose an approach for the tuning of evaluation function parameters based on evolutionary algorithms. We introduce an iterative method for population member selection and show how the resulting win, loss, or draw information from competition can be used in conjunction with the statistical analysis of the population to develop evaluation function parameter values. A population of evaluation function candidates are randomly generated and exposed to the proposed learning techniques. An analysis to the success of learning is given and the undeveloped and developed players are examined through competition against a commercial chess program.


title: A Multiband Millimeter-Wave 2-D Array Based on Enhanced Franklin Antenna for 5G Wireless Systems

abstract: This letter presents a novel and compact antenna array suggesting geometrical modifications in the concept of collinear arrays at millimeter-wave (MMW) spectrum. The extension of the standard narrowband Franklin linear antenna array in a compact 2-D assembly is proposed and multiband response is accomplished in this work, which fulfills demands of future fifth-generation (5G) systems. The proposed 2-D antenna array is designed to operate at licensed frequency bands of 28 and 37–39 GHz recommended by the U.S. Federal Communications Commission for 5G wireless networks, as well as at 33 GHz for satellite communication and navigation applications. Measurements validate the triple-band response at the desired frequencies of operation. The proposed antenna array also offers distinguishing performance in terms of high gain and significant efficiency above 70% in all the MMW operating bands.


title: Real-time High Performance Anomaly Detection over Data Streams: Grand Challenge

abstract: Real-time analytics over data streams are crucial for a wide range of use cases in industry and research. Today's sensor systems can produce high throughput data streams that have to be analyzed in real-time. One important analytic task is anomaly or outlier detection from the streaming data. In many industry applications, sensing devices produce a data stream that can be monitored to know the correct operation of industry devices and consequently avoid damages by triggering reactions in real-time. While anomaly detection is a well-studied topic in data mining, the real-time high-performance anomaly detection from big data streams require special studies and well-optimized implementation. This paper presents our implementation of a real-time anomaly detection system over data streams. We outline details of our two separate implementations using the Java and C++ programming languages, and provide technical details about the data processing pipelines. We report experimental results and describe performance tuning strategies.


title: Multi-Mode GF-3 Satellite Image Geometric Accuracy Verification Using the RPC Model

abstract: The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are 12 different imaging models to meet the needs of different industry users. However, to use SAR satellite images for related applications, they must possess high geometric accuracy. In order to verify the geometric accuracy achieved by the different modes of GF-3 images, we analyze the SAR geometric error source and perform geometric correction tests based on the RPC model with and without ground control points (GCPs) for five imaging modes. These include the spotlight (SL), ultra-fine strip (UFS), Fine Strip I (FSI), Full polarized Strip I (QPSI), and standard strip (SS) modes. Experimental results show that the check point residuals are large and consistent without GCPs, but the root mean square error of the independent checkpoints for the case of four corner control points is better than 1.5 pixels, achieving a similar level of geometric positioning accuracy to that of international satellites. We conclude that the GF-3 satellite can be used for high-accuracy geometric processing and related industry applications.


title: Target-driven visual navigation in indoor scenes using deep reinforcement learning

abstract: Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. ::: We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment. ::: The supplementary video can be accessed at the following link: this https URL


title: Graph-based approaches to word sense induction

abstract: This thesis is a study of Word Sense Induction (WSI), the Natural Language Processing (NLP) task of automatically discovering word meanings from text. WSI is an open problem in NLP whose solution would be of considerable benefit to many other NLP tasks. It has, however, has been studied by relatively few NLP researchers and often in set ways. Scope therefore exists to apply novel methods to the problem, methods that may improve ::: upon those previously applied. This thesis applies a graph-theoretic approach to WSI. In this approach, word senses are identifed by finding particular types of subgraphs in word co-occurrence graphs. A number of original methods for constructing, analysing, and partitioning graphs are introduced, with these methods then incorporated into graphbased WSI systems. These systems are then shown, in a variety of evaluation scenarios, to return results that are comparable to those of the current best performing WSI systems. The main contributions of the thesis are a novel parameter-free soft clustering algorithm that runs in time linear in the number of edges in the input graph, and novel generalisations of the clustering coeficient (a measure of vertex cohesion in graphs) to the weighted case. Further contributions of the thesis include: a review of graph-based WSI systems that have been proposed in the literature; analysis of the methodologies applied in these systems; analysis of the metrics used to evaluate WSI systems, and empirical evidence to verify the usefulness of each novel method introduced in the thesis for inducing word senses.


title: Semantics and complexity of SPARQL

abstract: SPARQL is the W3C candidate recommendation query language for RDF. In this paper we address systematically the formal study of SPARQL, concentrating in its graph pattern facility. We consider for this study a fragment without literals and a simple version of filters which encompasses all the main issues yet is simple to formalize. We provide a compositional semantics, prove there are normal forms, prove complexity bounds, among others that the evaluation of SPARQL patterns is PSPACE-complete, compare our semantics to an alternative operational semantics, give simple and natural conditions when both semantics coincide and discuss optimizations procedures.


title: Automation of street light for smart city

abstract: Energy consumption in metropolitan cities is increasing day by day. In every city, considerable amount of electricity is being used for the purpose of street lighting system. Some areas of the city may have low frequency of passerby, but it's observed that the amount of energy the street lights consume in these areas is same as that of areas with high frequency passerby. As a result enormous amount of energy is wasted without being used. In the proposed system, high intensity discharge lamps are replaced by LED's which can alter its intensity based on the need. Movement of vehicles is sensed using LDR (Light Dependent Resistor) and the intensity of the street light is reduced when not in use. The system also detects fault in the system and indicates it to the base station using GSM (Global System for Mobile communication) technology by sending SMS (short message service).


title: Tapping on the potential of q&a community by recommending answer providers

abstract: The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information.


title: ASL recognition based on a coupling between HMMs and 3D motion analysis

abstract: We present a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data. The data are obtained by using physics-based three-dimensional tracking methods and then presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, we model context-dependent HMMs and present a novel method of coupling three-dimensional computer vision methods and HMMs by temporally segmenting the data stream with vision methods. We then use the geometric properties of the segments to constrain the HMM framework for recognition. We show in experiments with a 53 sign vocabulary that three-dimensional features outperform two-dimensional features in recognition performance. Furthermore, we demonstrate that context-dependent modeling and the coupling of vision methods and HMMs improve the accuracy of continuous ASL recognition.


title: Robot Programming by Demonstration with Crowdsourced Action Fixes

abstract: Programming by Demonstration (PbD) can allow end-users to teach robots new actions simply by demonstrating them. However, learning generalizable actions requires a large number of demonstrations that is unreasonable to expect from end-users. In this paper, we explore the idea of using crowdsourcing to collect action demonstrations from the crowd. We propose a PbD framework in which the end-user provides an initial seed demonstration, and then the robot searches for scenarios in which the action will not work and requests the crowd to fix the action for these scenarios. We use instance-based learning with a simple yet powerful action representation that allows an intuitive visualization of the action. Crowd workers directly interact with these visualizations to fix them. We demonstrate the utility of our approach with a user study involving local crowd workers (N=31) and analyze the collected data and the impact of alternative design parameters so as to inform a real-world deployment of our system.


title: Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Smart Grid

abstract: Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.


title: Context-aware Argument Mining and Its Applications in Education

abstract: Context is crucial for identifying arguments and argumentative relations in text, but existing argument studies have not addressed context dependence adequately. In this thesis, we propose context-aware argument mining that makes use of contextual features extracted from writing topics and context sentences to improve state-of-the-art argument component and argumentative relation classifications. The effectiveness as well as generality of our proposed contextual features is proven through its application in different argument mining tasks in student essays. We further evaluate the applicability of our proposed argument mining models in automated persuasive essay scoring tasks.


title: Multi-clue fusion for emotion recognition in the wild

abstract: In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.


title: Mashup Service Clustering Based on an Integration of Service Content and Network via Exploiting a Two-Level Topic Model

abstract: The rapid growth in the number and diversity of Mashup services, coupled with the myriad of functionally similar Mashup services, makes it difficult to find suitable Mashup services to develop Mashup-based software applications due to an unprecedentedly large number of choices of Mashup services. Even if the existing latent factor based methods show significant improvements in Mashup service clustering and discovery, it is still challenging to find Mashup services with high accuracy due to overlooking of relationships among Mashup services. The relationships among Mashup services actually can be exploited in mining latent functional factors to improve the accuracy of clustering and discovery. In this paper, we propose a Mashup service clustering method based on an integration of service content and network via exploiting a two-level topic model. This method, firstly designs a two-level topic model to mine latent topics for representing functional features of Mashup services. Secondly, it uses two different random walk processes to derive and incorporate the topic distribution of Mashup services at service network level into the topic distribution of Mashup services at the service content level. Thirdly, K-means and Agnes algorithm are used to perform Mashup service clustering based on latent topics' similarity. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing clustering approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, purity and entropy.


title: Runtime Verification of Ethereum Smart Contracts

abstract: The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.


title: Instant Messaging as Information Goldmines to Digital Forensic: A Systematic Review

abstract: The use of instant messaging (IM) application is very communal among smartphone users, as it delivers ease in interaction and communication. The speed at which technologies, growth, and expansion of networks infrastructure for high-speed data networks (e.g. wifi, 4G, etc.) are changing and the high number of IM applications are available. IM application usage is significantly escalating, also for doing illegitimate purposes, particularly to organize and coordinate criminal acts. They are practically exploited by the criminality groups involved when they target and interact with the victims, or try to run away from criminal occurrences. By systematically conducting a review to understand and to investigate IM applications topic coming from a number of scientific sources (e.g. manuscripts, metadata, files, etc.), a broader picture and opportunity to regain the valuable evidence in the investigation is academically provided.


title: Predicting Dropout in Higher Education Based on Secondary School Performance

abstract: Predicting student performance, preventing failure and identifying the factors influencing student dropout are issues that have attracted a great deal of research interest recently. In this study, we employ and evaluate several machine learning algorithms to identify students at-risk and predict student dropout of university programs based on the data available at the time of enrollment (secondary school performance, personal details). We also present a data-driven decision support platform for education directorate and stakeholders. The models are built on data of 15,825 undergraduate students from Budapest University of Technology and Economics enrolled between 2010 and 2017 and finished their undergraduate studies either by graduation or dropping out. We handle the problem of missing data by imputation. After performing feature extraction and feature selection, a wide range of classifiers have been trained including Decision Tree-based algorithms, Naive Bayes, k-NN, Linear Models and Deep Learning with different input settings. The methods were tested using 10-fold cross-validation and the AUC of the best models, Gradient Boosted Trees and Deep Learning, were 0.808 and 0.811 respectively.


title: Survey on selenium tool in software testing

abstract: We are moving towards a general public where web is the need of hour. Today the vast majority of the product applications executed, are composed as online applications which are keep running in a web program. Testing programming applications is critical. Numerous associations make utilization of a specific web application, so the same web applications are tried habitually by diverse clients from distinctive regions physically. Testing a web application physically is tedious, so we go for test automation. In test automation we make utilization of a product device to run repeatable tests against the application to be tried. There are various focal points of test automation. They are exceptionally exact and have more prominent preparing pace when contrasted with manual automation. There are various open source and business devices accessible for test mechanization. Selenium is one of the broadly utilized open source device for test computerization. Test automation enhances the effectiveness of programming testing procedures. Test automation gives quick criticism to engineers. It additionally discovers the imperfections when one may miss in the manual testing. In test automation we can perform boundless emphases for testing the same example of code ceaselessly commonly.


The Design and Implementation of ViPER


title: Multi-view graph learning with adaptive label propagation

abstract: Graphs play an essential role in many data mining paradigms, such as semi-supervised classification. Conventional graph learning methods mainly focus on constructing graphs from single-view data. Nowadays data can be collected from multiple views using various sensors. How to construct a robust and reliable graph from multi-view data is still an open problem. In this paper, we propose a multi-view graph learning (MVGL) approach with adaptive label propagation for semi-supervised classification. MVGL integrates latent factor extraction, graph sparsification, and label propagation into a unified framework. It seeks shared latent factors from multi-view data as view-independent data representations, and then constructs a sparse graph accordingly. Meanwhile, the label propagation is adaptively optimized during graph construction. An efficient optimization algorithm is designed to solve the model. Experimental results on two benchmark datasets show remarkable improvements over both single-view and multi-view learning baselines.


title: Impact of Walking on Glycemic Control and Other Cardiovascular Risk Factors in Type 2 Diabetes: A Meta-Analysis

abstract: BACKGROUND ::: Walking is the most popular and most preferred exercise among type 2 diabetes patients, yet compelling evidence regarding its beneficial effects on cardiovascular risk factors is still lacking. The aim of this meta-analysis of randomized controlled trials (RCTs) was to evaluate the association between walking and glycemic control and other cardiovascular risk factors in type 2 diabetes patients. ::: ::: ::: METHODS ::: Three databases were searched up to August 2014. English-language RCTs were eligible for inclusion if they had assessed the walking effects (duration ≥8 weeks) on glycemic control or other cardiovascular risk factors among type 2 diabetes patients. Data were pooled using a random-effects model. Subgroup analyses based on supervision status and meta-regression analyses of variables regarding characteristics of participants and walking were performed to investigate their association with glycemic control. ::: ::: ::: RESULTS ::: Eighteen studies involving 20 RCTs (866 participants) were included. Walking significantly decreased glycosylated haemoglobin A1c (HbA1c) by 0.50% (95% confidence intervals [CI]: -0.78% to -0.21%). Supervised walking was associated with a pronounced decrease in HbA1c (WMD -0.58%, 95% CI: -0.93% to -0.23%), whereas non-supervised walking was not. Further subgroup analysis suggested non-supervised walking using motivational strategies is also effective in decreasing HbA1c (WMD -0.53%, 95% CI: -1.05% to -0.02%). Effects of covariates on HbA1c change were generally unclear. For other cardiovascular risk factors, walking significantly reduced body mass index (BMI) and lowered diastolic blood pressure (DBP), but non-significantly lowered systolic blood pressure (SBP), or changed high-density or low-density lipoprotein cholesterol levels. ::: ::: ::: CONCLUSIONS ::: This meta-analysis supports that walking decreases HbA1c among type 2 diabetes patients. Supervision or the use of motivational strategies should be suggested when prescribed walking to ensure optimal glycemic control. Walking also reduces BMI and lowers DBP, however, it remains insufficient regarding the association of walking with lowered SBP or improved lipoprotein profiles. ::: ::: ::: TRIAL REGISTRATION ::: PROSPERO CRD42014009515.


title: Grid current regulation of a three-phase voltage source inverter with an LCL input filter

abstract: Many grid connected power electronic systems, such as STATCOMs, UPFCs, and distributed generation system interfaces, use a voltage source inverter (VSI) connected to the supply network through a filter. This filter, typically a series inductance, acts to reduce the switching harmonics entering the distribution network. An alternative filter is a LCL network, which can achieve reduced levels of harmonic distortion at lower switching frequencies and with less inductance, and therefore has potential benefits for higher power applications. However, systems incorporating LCL filters require more complex control strategies and are not commonly presented in literature. This paper proposes a robust strategy for regulating the grid current entering a distribution network from a three-phase VSI system connected via a LCL filter. The strategy integrates an outer loop grid current regulator with inner capacitor current regulation to stabilize the system. A synchronous frame PI current regulation strategy is used for the outer grid current control loop. Linear analysis, simulation, and experimental results are used to verify the stability of the control algorithm across a range of operating conditions. Finally, expressions for ""harmonic impedance" of the system are derived to study the effects of supply voltage distortion on the harmonic performance of the system.


title: Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic

abstract: Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics. This paper is the first step towards this goal; we propose a proof-synthesis method for the negation-free propositional logic in which we use a DNN to obtain a guide of proof search. The idea is to view the proof-synthesis problem as a translation from a proposition to its proof. We train seq2seq, which is a popular network in neural machine translation, so that it generates a proof encoded as a $\lambda$-term of a given proposition. We implement the whole framework and empirically observe that a generated proof term is close to a correct proof in terms of the tree edit distance of AST. This observation justifies using the output from a trained seq2seq model as a guide for proof search.


title: Influence of piezoceramic composition, pitch, and layout on the performances of sparse 2D array transducers for medical imaging

abstract: Two dimensinal (2D) array transducers have become of great interest in the last few years, in view of the possibility of real time volumetric ultrasonic imaging. However, due to low signal to noise ratio and to the limited number of channels on available imaging systems, both sensitivity and resolution of such array are lower than those of 1D arrays. First, new high dielectric permittivity PNNZT piezoceramics are characterized and compared to classical PZT. 2D array elements are then manufactured and their experimental performances are compared. PNNZT allows an increase in element pulse echo sensitivity around 6 dB as compared to PZT array elements. The effects of the pitch and layout on the sparse array radiation pattern for several steering angles are investigated. Pseudo-random layouts are shown to have satisfactory acoustic noise level as compared to periodic layouts (vernier). Moreover, such configurations allow the pitch to be increased slightly over the classical half wavelength phased-array value, thus increasing the active area (i.e. sensitivity). Optimized array configuration leads to an increase in acoustic sensitivity of at least 6 dB and a decrease of acoustic noise level around 10 dB.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: The 2012 free and open source GIS software map - A guide to facilitate research, development, and adoption

abstract: Over the last decade an increasing number of free and open source software projects have been founded that concentrate on developing several types of software for geographic data collection, storage, analysis and visualization. We first identify the drivers of such software projects and identify different types of geographic information software, e.g. desktop GIS, remote sensing software, server GIS etc. We then list the major projects for each software category. Afterwards we discuss the points that should be considered if free and open source software is to be selected for use in business and research, such as software functionality, license types and their restrictions, developer and user community characteristics, etc. Finally possible future developments are addressed.


title: A system of serial computation for classified rules prediction in non-regular ontology trees

abstract: Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.


title: Development of seismic demand for Chang-Bin offshore wind farm in Taiwan Strait

abstract: Taiwan is located on the Pacific seismic belt, and the soil conditions of Taiwan’s offshore wind farms are softer than those in Europe. To ensure safety and stability of the offshore wind turbine supporting structures, it is important to assess the offshore wind farms seismic forces reasonably. In this paper, the relevant seismic and geological data are obtained for Chang-Bin offshore wind farm in Taiwan Strait, the probabilistic seismic hazard analysis (PSHA) is carried out, and the first uniform hazard response spectrum for Chang-Bin offshore wind farm is achieved. Compared with existing design response spectrum in the local regulation, this site-specific seismic hazard analysis has influence on the seismic force considered in the design of supporting structures and therefore affects the cost of the supporting structures. The results show that a site-specific seismic hazard analysis is required for high seismic area. The paper highlights the importance of seismic hazard analysis to assess the offshore wind farms seismic forces. The follow-up recommendations and research directions are given for Taiwan’s offshore wind turbine supporting structures under seismic force considerations.


title: Winograd Schema - Knowledge Extraction Using Narrative Chains

abstract: The Winograd Schema Challenge (WSC) is a test of machine intelligence, designed to be an improvement on the Turing test. A Winograd Schema consists of a sentence and a corresponding question. To successfully answer these questions, one requires the use of commonsense knowledge and reasoning. This work focuses on extracting common sense knowledge which can be used to generate answers for the Winograd schema challenge. Common sense knowledge is extracted based on events (or actions) and their participants; called Event-Based Conditional Commonsense (ECC). I propose an approach using Narrative Event Chains [Chambers et al., 2008] to extract ECC knowledge. These are stored in templates, to be later used for answering the WSC questions. This approach works well with respect to a subset of WSC tasks.


title: Radar fall motion detection using deep learning

abstract: Radar has a great potential to be one of the leading technologies to perform in-home monitoring of elderly. Radar signal returns corresponding to human gross-motor activities are nonstationary in nature. As such, time-frequency (TF) analysis plays a fundamental role in revealing constant and higher order velocity components of various parts of the human body under motion which are important for motion discrimination. In this paper, we consider radar for fall detection using TF-based deep learning approach. The proposed approach learns and captures the intricate properties of the TF signatures without human intervention and feeds the underlying features to the classifier. Experimental data is used to demonstrate the effectiveness of the proposed fall detection deep learning approach in comparison with the principal component analysis method and techniques incorporating manual selections of a few dominant features.


title: A novel UWB Wilkinson power divider

abstract: A novel UWB power divider formed by installing one overlapped butterfly radial stub on each branch is proposed. Such structure makes branch line shorter and circuit's size smaller compares to power divider with single radial stub. It's also achieved a compact size of 18 mm by 16 mm. The simulation and measurement results of the UWB power divider are presented and shown good agreement.


MC1R gene mutation and its association with oculocutaneous albinism type (OCA) phenotype in a consanguineous Pakistani family


title: High-resolution CT findings of severe acute respiratory syndrome at presentation and after admission.

abstract: OBJECTIVE ::: The aim of this study was to assess the high-resolution CT (HRCT) findings at presentation and after hospital admission in patients with severe acute respiratory syndrome (SARS). ::: ::: ::: MATERIALS AND METHODS ::: We reviewed the HRCT findings at presentation (n = 12) and after hospital admission (n = 25) of 29 patients with SARS and compared the HRCT findings with the radiographic findings. HRCT scans were obtained using 1-mm (n = 28) or 2-mm (n = 1) collimation. The radiographs and HRCT scans were reviewed independently by two observers who reached a decision by consensus. ::: ::: ::: RESULTS ::: All patients had abnormal findings on HRCT at presentation. Eight of these 12 patients had normal findings on radiographs. The predominant HRCT findings at presentation consisted of unilateral (n = 6) or bilateral (n = 2) ground-glass opacities or focal unilateral (n = 2) or bilateral (n = 2) areas of consolidation. All patients showed progression of disease on follow-up. The predominant HRCT findings on follow-up CT scans consisted of unilateral (n = 2) or bilateral ground-glass opacities (n = 13), unilateral (n = 2) or bilateral consolidation (n = 5), or a mixed bilateral pattern of ground-glass attenuation, consolidation, and reticulation (n = 3). Reticulation with associated architectural distortion and mild traction bronchiectasis was present in eight patients. ::: ::: ::: CONCLUSION ::: HRCT can show parenchymal abnormalities in patients with SARS who have normal findings on radiographs at presentation. Follow-up CT scans obtained in hospitalized patients show findings consistent with fibrosis in a small percentage of patients.


title: Reward, Motivation, and Reinforcement Learning

abstract: There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concerning the impact of motivational manipulations, show that these models are unreasonable. We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role.


title: Toward a more precise definition of self-citation

abstract: The definition assigned to self-citations is nontrivial. This decision can affect research outputs in a number of ways. The current paper considers the self-citation definition used by the Web of Science, and compares this with an alternative definition, advanced in the present study, within the context of the work of an individual researcher. A discussion follows.


title: Internal Compact Dual-Band Printed Loop Antenna for Mobile Phone Application

abstract: A novel dual-band printed loop antenna very promising for application in mobile phones as an internal antenna is presented. The antenna comprises an outer loop strip and an inner inverted-L strip connected to and enclosed by the outer loop strip. The antenna occupies a compact area of 15 times 50 mm2 only, yet generating three resonant modes to form two wide bands at about 900 and 1800 MHz for the Global System for Mobile Communication/Digital Communication System operation. Detailed design considerations of the three excited modes are described in the paper. When a shielding metal case is placed close to the antenna, small effects on the antenna's impedance matching are seen, allowing compact integration of the antenna with nearby conducting elements or electronic components. The antenna is also found to result in small excited surface current distributions on the system ground plane of the mobile phone. This behavior is expected to lead to reduced user's hand effects on the radiation efficiency of the antenna