title: Service-oriented architecture for a scalable videoconferencing system

abstract: The availability of increasing network bandwidth and computing power provides new opportunities for videoconferencing systems over Internet. Multimedia capable devices with broadband Internet connections are spreading rapidly. Even cell phones will have broadband Internet access in the near future. This requires universally accessible and scalable videoconferencing systems that can deliver thousands of concurrent audio and video streams. However, developing videoconferencing systems over Internet is a challenging task, since audio and video communications require high bandwidth and low latency. In addition, the processing of audio and video streams is computing intensive. Therefore, it is particularly difficult to develop scalable systems that support high number of users with various capabilities. Current videoconferencing systems such as IP-Multicast and H.323 can not fully address the problem of scalability and universal accessibility. These systems lack flexible service oriented architecture to support increasingly diverse. We believe that with the advancements in computing power and network bandwidth, more flexible and service oriented systems should be developed. In this paper, we outline a service oriented architecture for videoconferencing, GlobalMMCS, based on a publish/subscribe event brokering network, NaradaBrokering.


title: Effective Question Answering Techniques and their Evaluation Metrics

abstract: Question Answering (QA) is a focused way of information retrieval. Question Answering system tries to get back the accurate answers to questions posed in natural language provided a set of documents. Basically question answering system (QA) has three elements i.e. question classification, information retrieval (IR), and answer extraction. These elements play a major role in Question Answering. In Question classification, the questions are classified depending upon the type of its entity. Information retrieval component is used to determine success by retrieving relevant answer for different questions posted by the intelligent question answering system. Answer extraction module is growing topics in the QA in which ranking and validating a candidate’s answer is the major job. This paper offers a concise discussion regarding different Question Answering types. In addition we describe different evaluation metrics used to evaluate the performance of different question answering systems. We also discuss the recent question answering systems developed and their corresponding techniques.


title: RADIATIVE AND INELASTIC EFFECTS IN DYNAMICS OF DOUBLE SINE-GORDON SOLITONS

abstract: Abstract The dynamics of the so-called wobblers, i.e. 4π-kinks of the double sine-Gordon equation with excited internal oscillations, is studied. The rate of energy emission from a weakly excited wobbler is calculated. Then scattering of a wobbler by a localized inhomogeneity is considered and it is demonstrated that in the first approximation it results in the change of the wobbler's velocity only if the wobbler was excited prior to the collision. The corresponding changes of velocity and of the internal oscillation amplitude are calculated. Inelastic collision of two unexcited wobblers in the presence of the inhomogeneity is briefly considered too.


title: History in Organization and Management Theory: More Than Meets the Eye

abstract: AbstractThere has been a growing debate about the role of history in management research with several authors making suggestions on how to bring the two (back) together and others even highlighting the need for a “historic turn”. What we argue in this paper is that, while history was indeed sidelined by the scientization of management since the late 1950s, it started to make a comeback from the 1980s onwards and is increasingly employed in a number of research programs. We stress that the crucial question for management scholars engaging with history (or wanting to do so) is how it relates to theory. First of all, we present a systematic overview of the way history has been used—both at the micro (organizational) and macro-levels of analysis—distinguishing between what we refer to as “history to theory” and “history in theory”. In the former, we consider those research programs, such as (neo-)institutionalism, where history serves as evidence to develop, modify or test theories. In the case of “history in...


title: Imaging the redshifted 21-cm pattern around the first sources during the cosmic dawn using the SKA

abstract: Understanding properties of the first sources in the Universe using the redshifted \HI ~21-cm signal is one of the major aims of present and upcoming low-frequency experiments. We investigate the possibility of imaging the redshifted 21-cm pattern around the first sources during the cosmic dawn using the SKA1-low. We model the \HI ~21-cm image maps, appropriate for the SKA1-low, around the first sources consisting of stars and X-ray sources within galaxies. In addition to the system noise, we account also for the astrophysical foregrounds by adding them to the signal maps. We find that after subtracting the foregrounds using a polynomial fit and suppressing the noise by smoothing the maps over $10^{'} - 30^{'}$ angular scale, the isolated sources at $z \sim 15$ are detectable with $\sim 4 - 9 \, \sigma$ confidence level in 2000 h of observation with the SKA1-low. Although the 21-cm profiles around the sources get altered because of the Gaussian smoothing, the images can still be used to extract some of the source properties. We account for overlaps in the patterns of the individual sources by generating realistic \HI ~21-cm maps of the cosmic dawn that are based on $N$-body simulations and a one-dimensional radiative transfer code. We find that these sources should be detectable in the SKA1-low images at $z = 15$ with an SNR of $\sim 14 (4)$ in 2000 (200) h of observations. One possible observational strategy thus could be to observe multiple fields for shorter observation times, identify fields with SNR $\gtrsim 3$ and observe these fields for much longer duration. Such observations are expected to be useful in constraining the parameters related to the first sources.


title: Distributed Swarm Optimization Modeling for Waste Collection Vehicle Routing Problem

abstract: In this paper, we consider a complex garbage collection problem, where the residents of a particular area dispose of recyclable garbage, which is collected and managed using a fleet of trucks with different weight capacities and volume. This tour is characterized by a set of constraints such as the maximum tour duration (in term of distance and the timing) consumed to collect wastes from several locations. This problem is modeled as a garbage collection vehicle routing problem, which aims to minimize the cost of traveling routes (minimizing the distance traveled) by finding optimal routes for vehicles such that all waste bins are emptied and the waste is driven towards the disposal locations. We propose a distributed technique based on the Ant Colony system Algorithm to find optimal routes that help vehicles to visit all the wastes bins using interactive agents consumed based on the behavior of real ants. The designed solution will try to create a set of layers to control and manage the waste collection, each layer will be handled by an intelligent agent which is characterized by a specific behavior, in this architecture a set of behaviors have been designed to optimizing routes and control the real time capacity of vehicles. Finally, manage the traffic messages between the different agents to select the best solutions that will be assigned to each vehicle. The developed solution performs well compared to the traditional solution on small cases.


title: The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments

abstract: This paper experimentally investigates the role of visual complexity (VC) and prototypicality (PT) as design factors of websites, shaping users' first impressions by means of two studies. In the first study, 119 screenshots of real websites varying in VC (low vs. medium vs. high) and PT (low vs. high) were rated on perceived aesthetics. Screenshot presentation time was varied as a between-subject factor (50ms vs. 500ms vs. 1000ms). Results reveal that VC and PT affect participants' aesthetics ratings within the first 50ms of exposure. In the second study presentation times were shortened to 17, 33 and 50ms. Results suggest that VC and PT affect aesthetic perception even within 17ms, though the effect of PT is less pronounced than the one of VC. With increasing presentation time the effect of PT becomes as influential as the VC effect. This supports the reasoning of the information-processing stage model of aesthetic processing (Leder et al., 2004), where VC is processed at an earlier stage than PT. Overall, websites with low VC and high PT were perceived as highly appealing.


title: Modeling of LTE DRX in RRC Idle state

abstract: In LTE/LTE-A the Discontinuous Reception (DRX) mechanism is the main approach for power saving in User Equipments (UEs) when in Connected and Idle mode. In this paper, an Idle mode DRX mechanism model is proposed also enabling evaluation of closely related mechanisms such as paging and Tracking Area Update (TAU). Two performance metrics are derived, namely the reachability delay and the power saving factor.


title: Neighborhood label extension for handwritten/printed text separation in Arabic documents

abstract: This paper addresses the problem of handwritten and printed text separation in Arabic document images. The objective is to extract handwritten text from other parts of the document. This allows the application, in a second time, of a specialized processing on the extracted handwritten part or even on the printed one. Documents are first preprocessed in order to remove eventual noise and correct document orientation. Then, the document is segmented into pseudo-lines that are segmented in turn into pseudo-words. A local classification step, using a Gaussian kernel SVM, associates each pseudo-word into handwritten or printed classes. This label is then propagated in the pseudo-word's neighborhood in order to recover from classification errors. The proposed methodology has been tested on a set of public real Arabic documents achieving a separation rate of around 90%.


title: Analysis and Design Strategy of UHF Micro-Power CMOS Rectifiers for Micro-Sensor and RFID Applications

abstract: Design strategy and efficiency optimization of ultrahigh-frequency (UHF) micro-power rectifiers using diode-connected MOS transistors with very low threshold voltage is presented. The analysis takes into account the conduction angle, leakage current, and body effect in deriving the output voltage. Appropriate approximations allow analytical expressions for the output voltage, power consumption, and efficiency to be derived. A design procedure to maximize efficiency is presented. A superposition method is proposed to optimize the performance of multiple-output rectifiers. Constant-power scaling and area-efficient design are discussed. Using a 0.18-mum CMOS process with zero-threshold transistors, 900-MHz rectifiers with different conversion ratios were designed, and extensive HSPICE simulations show good agreement with the analysis. A 24-stage triple-output rectifier was designed and fabricated, and measurement results verified the validity of the analysis


title: Emotions From Text: Machine Learning For Text-Based Emotion Prediction

abstract: In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naive baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.


title: Difficulties of the set of natural numbers

abstract: In this article some difficulties are deduced from the set of natural numbers. The demonstrated difficulties suggest that if the set of natural numbers exists it would conflict with the axiom of regularity. As a result, we have the conclusion that the class of natural numbers is not a set but a proper class.


title: A soft-switching active-clamp scheme for isolated full-bridge boost converter

abstract: In this paper, the isolated full-bridge boost converter with active clamp is described and a new active-clamping algorithm to improve the efficiency is suggested. In the proposed method, the resonance between the clamp capacitor and the leakage inductor is utilized to reduce switching losses. The loss analysis is performed by simulation and the improved performance is confirmed by experimental results.


title: Experimental Results of High-Efficiency Resonant Coupling Wireless Power Transfer Using a Variable Coupling Method

abstract: We experimentally demonstrate that the efficiency of wireless power transfer utilizing resonant coupling is improved by applying the “matching condition.” The `matching condition' is extracted from an equivalent circuit model of a magnetically coupled wireless power transfer system. This condition is achieved by varying the coupling factor between the source (load) and the internal resonator. Applying this technique results in efficiency improvements of 46.2% and 29.3% at distances of 60 cm and 1 m, respectively. The maximum efficiency is 92.5% at 15 cm. A circuit model based on the extracted parameters of the resonators produces results similar to the experimental data.


title: Attacking the Washington, D.C. internet voting system

abstract: In 2010, Washington, D.C. developed an Internet voting pilot project that was intended to allow overseas absentee voters to cast their ballots using a website. Prior to deploying the system in the general election, the District held a unique public trial: a mock election during which anyone was invited to test the system or attempt to compromise its security. This paper describes our experience participating in this trial. Within 48 hours of the system going live, we had gained near- complete control of the election server. We successfully changed every vote and revealed almost every secret ballot. Election ocials did not detect our intrusion for nearly two business days—and might have remained unaware for far longer had we not deliberately left a prominent clue. This case study—the first (to our knowledge) to analyze the security of a government Internet voting system from the perspective of an attacker in a realistic pre-election deployment—attempts to illuminate the practical challenges of securing online voting as practiced today by a growing number of jurisdictions.


title: Automatic Evidence Retrieval for Systematic Reviews

abstract: Background: Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective: Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods: Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F 1 score. Results: The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F 1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F 1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F 1 score=87.3%) of the 633 correctly retrieved citations. Conclusions: The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. [J Med Internet Res 2014;16(10):e223]


title: The role of new technologies to foster positive youth development

abstract: This article describes a developmental systems approach to applied developmental science (ADS), which provides a framework to design and evaluate technology-rich programs that promote positive development by emphasizing the strengths and assets of young people instead of focusing on diminishing or preventing risk-taking behaviors. Until now, most of the psychoeducational programs conceived within the ADS model have not focused on the role of new technologies in young people's lives. This absence is particularly striking given that, in today's world, new technologies play an important role in different areas of the lives of young people, such as education, entertainment, socialization, and communication. This article presents the concept of identity construction environments (ICE), an interdisciplinary model that proposes guidelines to design and study new technologies purposefully created to promote positive youth development (PYD). Two types of ICE have been developed: one focusing on stand-alone technol...


title: A robust and scalable clustering algorithm for mixed type attributes in large database environment

abstract: Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining problems. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. Zhang et al [8] proposed a clustering method named BIRCH that is especially suitable for very large datasets. We develop a clustering algorithm using our distance measure based on the framework of BIRCH. Similar to BIRCH, our algorithm first performs a pre-clustering step by scanning the entire dataset and storing the dense regions of data records in terms of summary statistics. A hierarchical clustering algorithm is then applied to cluster the dense regions. Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data. For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment.


title: Visual Query Language: Finding patterns in and relationships among time series data

abstract: Many scientific datasets archive a large number of variables over time. These timeseries data streams typically track many variables over relatively long periods of time, and therefore are often both wide and deep. In this paper, we describe the Visual Query Language (VQL) [3], a technology for locating time series patterns in historical or real time data. The user interactively specifies a search pattern, VQL finds similar shapes, and returns a ranked list of matches. VQL supports both univariate and multivariate queries, and allows the user to interactively specify the the quality of the match, including temporal warping, amplitude warping, and temporal constraints between features.


Combining 2-opt , 3-opt and 4-opt with K-swap-kick Perturbations for the Traveling Salesman Problem


title: Investigation of the Influence of the As-Grown ZnO Nanorods and Applied Potentials on an Electrochemical Sensor for In-Vitro Glucose Monitoring

abstract: The influence of the as-grown zinc oxide nanorods (ZnO NRs) on the fabricated electrochemical sensor for in vitro glucose monitoring were investigated. A direct growth of ZnO NRs was performed on the Si/SiO2/Au electrode, using hydrothermal and sol-gel techniques at low temperatures. The structure, consisting of a Si/SiO2/Au/GOx/Nafion membrane, was considered as a baseline, and it was tested under several applied potential 0.1–0.8 V. The immobilized working electrode, with GOx and a nafion membrane, was characterized amperometrically using a source meter Keithely 2410, and an electrochemical impedance Gamry potentiostat. The sensor exhibited the following: a high sensitivity of ~0.468 mA/cm2 mM, a low detection limit in the order of 166.6 µM, and a fast and sharp response time of around 2 s. The highest sensitivity and the lowest limit of detection were obtained at 0.4 volt, after the growth of ZnO NRs. The highest net sensitivity was obtained after subtracting the sensitivity of the baseline, and it was in the order of 0.315 mA/cm2·mM. The device was tested with a range of glucose concentrations from 1–10 mM, showing a linear line from 3–8 mM, and the device was saturated after exceeding high concentrations of glucose. Such devices can be used for in vitro glucose monitoring, since glucose changes can be accurately detected.


title: Projective simulation applied to the grid-world and the mountain-car problem

abstract: We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the"grid-world"and the"mountain-car"problem, which challenge the model with large and continuous input space. We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.


title: A Cooperative approach to particle swarm optimization

abstract: The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.


title: Foot shape classification using 3D scanning data

abstract: 3D foot anthropometric data was collected and classified into several foot shapes. A 3D foot anthropometric database which contains 1835 male right foot scanning images was used. The subjects aged from 18 to 60 years old. A 3D foot scanner was used to collect 12 foot dimensions including foot length, ball of foot length, outside ball of foot length, foot breadth, heel breadth, ball circumference, instep circumference, toe height, navicular height, instep height, toe 1 angle and toe 5 angle. The principle component analysis (PCA) and K-means cluster analysis was applied to classify male subjects' foot shapes. The PCA results indicated that foot breadth, foot length and navicular height were selected as three principle components. The percentage of total variance explained by the 3 principle components was 72.96%. The use of K-means clustering can classify male subjects' foot into 6 foot types. In addition, a new foot sizing system was developed for Taiwanese males. Comparing the new sizing system with the current CNS 4800-S1093 sizing system, the new sizing system can reduce the size numbers, and provide updated foot dimensions. Thus, the manufacturer can apply these results for shoe last design and footwear production with better fitness and lower cost.


title: Feeding the Illusion of Growth and Happiness: A Reply to Hagerty and Veenhoven

abstract: In a rebuttal of Easterlin (1995), Hagerty and Veenhoven (2003) analyze data for 21 countries and conclude that “growing national income does go with greater happiness.” But the U.S. experience does not support this conclusion, which they obtain only by mixing together two sets of noncomparable surverys. Moreover, the result of studies of European countries and the U.S. by other scholars do not support their claim either. Furthermore, the experience of 6 out of 7 of their non-European countries fail to support their claim. Finally, if countries in their analysis with quite similar growth rates are grouped, one finds quite disparate trends in happiness, suggesting that factors other than growth in income are responsible for the differential trends in happiness. Instead of straining to feed the illusion that a focus on economic growth will create happiness, an approach is needed that explores the impact on national trends in life satisfaction, not just of material goods, but also of family life, health, work utility, and the like.


title: Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing

abstract: The artificial potential field (APF) approach provides a simple and effective motion planning method for practical purpose. However, artificial potential field approach has a major problem, which is that the robot is easy to be trapped at a local minimum before reaching its goal. The avoidance of local minimum has been an active research topic in path planning by potential field. In this paper, we introduce several methods to solve this problem, emphatically, introduce and evaluate the artificial potential field approach with simulated annealing (SA). As one of the powerful techniques for escaping local minimum, simulated annealing has been applied to local and global path planning.


title: Multiple Description Convolutional Neural Networks for Image Compression

abstract: Multiple description coding (MDC) is able to stably transmit signal in un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, traditional MDC does not well leverage image’s context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework, which efficiently leverages image’s context information to compress the image. First, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image’s content, which are compressed by a standard codec. Second, we present multiple description reconstruction network (MDRN) including side reconstruction networks (SRNs) and central reconstruction network (CRN). When any one of two lossy descriptions is received at decoder, SRN network is used to improve the quality of this decoded lossy description by simultaneously removing compression artifact and up-sampling. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Third, multiple description virtual codec network is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural dis-similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated descriptions are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the effectiveness of our framework.


Actario : A Framework for Reasoning About Actor Systems


title: A Research on Sentence Similarity for Question Answering System Based on Multi-feature Fusion

abstract: If just consider one feature of sentences to calculate sentences similarity, the performance of system is difficult to reach a satisfactory level. This paper presents a method of combining the features of semantic and structural to compute sentences similarity. It first discusses the methods of calculating the semantic similarity of sentences through word embedding and Tongyici Cilin. Next, it discusses the methods of calculating the morphological similarity and order similarity of sentences, and then combines the features through the neutral network to calculate the total similarity of the sentences. We include results from an evaluation of the system's performance and show that a combination of the features works better than any single approach.


title: Mendeley - A Last.fm For Research?

abstract: This paper aims to explore how the principles of a well-known Web 2.0 service, the world?s largest social music service "Last.fm" (www.last.fm), can be applied to research, which potential it could have in the world of research (e.g. an open and interdisciplinary database, usage-based reputation metrics, and collaborative filtering) and which challenges such a model would face in academia. A real-world application of these principles, "Mendeley" (www.mendeley.com), will be demoed at the IEEE e-Science Conference 2008.


QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent


title: Metal gate work function engineering on gate leakage of MOSFETs

abstract: We present a systematic study of tunneling leakage current in metal gate MOSFETs and how it is affected by the work function of the metal gate electrodes. Physical models used for simulations were corroborated by experimental results from SiO/sub 2/ and HfO/sub 2/ gate dielectrics with TaN electrodes. In bulk CMOS results show that, at the same capacitance equivalent oxide thickness (CET) at inversion, replacing a poly-Si gate by metal reduces the gate leakage appreciably by one to two orders of magnitude due to the elimination of polysilicon gate depletion. It is also found that the work function /spl Phi//sub B/ of a metal gate affects tunneling characteristics in MOSFETs. It is particularly significant when the transistor is biased at accumulation. Specifically, the increase of /spl Phi//sub B/ reduces the gate-to-channel tunneling in off-biased n-FET and the use of a metal gate with midgap /spl Phi//sub B/ results in a significant reduction of gate to source/drain extension (SDE) tunneling in both n- and p-FETs. Compared to bulk FET, double gate (DG) FET has much lower off-state leakage due to the smaller gate to SDE tunneling. This reduction in off-state leakage can be as much as three orders of magnitude when high-/spl kappa/ gate dielectric is used. Finally, the benefits of employing metal gate DG structure in future CMOS scaling are discussed.


title: Accurate Motion Detection in Dynamic Scenes Based on Ego-Motion Estimation and Optical Flow Segmentation Combined Method

abstract: This paper presents a novel method for accurate motion detection in dynamic scenes without any prior information about moving object or dynamic scenes. Moving object detection is mainly performed by segmentation of estimated optical flow field, which is calculated by classical Horn Schunck algorithm. Robust ego-motion estimation is performed prior to the optical flow segmentation, which largely decreases the computational complexity in that a compensated background shows very small optical flow vectors and more distinguishable than the optical vector from moving object. Experiments on real video sequences from moving cameras demonstrate the effectiveness of the proposed method.


title: Deep domain adaptation for describing people based on fine-grained clothing attributes

abstract: We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose/lighting/background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed inbetween the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.


Detecting OOV Named-Entities in Conversational Speech.


title: Compressed K -means for large-scale clustering

abstract: Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering. Specifically, high-dimensional data are compressed into short binary codes, which are well suited for fast clustering. CKM enjoys two key benefits: 1) storage can be significantly reduced by representing data points as binary codes; 2) distance computation is very efficient using Hamming metric between binary codes. We propose to jointly learn binary codes and clusters within one framework. Extensive experimental results on four large-scale datasets, including two million-scale datasets demonstrate that CKM outperforms the state-of-the-art large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.


title: Active Contours without Edges for Vector-Valued Images

abstract: In this paper, we propose an active contour algorithm for object detection in vector-valued images (such as RGB or multispectral). The model is an extension of the scalar Chan?Vese algorithm to the vector-valued case 1]. The model minimizes a Mumford?Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan?Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising step.


Assessing Use Acceptance and Satisfaction Toward Online Travel Agencies


title: Digital Beamforming-Based Massive MIMO Transceiver for 5G Millimeter-Wave Communications

abstract: A 64-channel massive multiple-input multiple-output (MIMO) transceiver with a fully digital beamforming (DBF) architecture for fifth-generation millimeter-wave communications is presented in this paper. The DBF-based massive MIMO transceiver is operated at 28-GHz band with a 500-MHz signal bandwidth and the time division duplex mode. The antenna elements are arranged as a 2-D array, which has 16 columns (horizontal direction) and 4 rows (vertical direction) for a better beamforming resolution in the horizontal plane. To achieve half-wavelength element spacing in the horizontal direction, a new sectorial transceiver array design with a bent substrate-integrated waveguide is proposed. The measured results show that an excellent RF performance is achieved. The system performance is tested with the over-the-air technique to verify the feasibility of the proposed DBF-based massive MIMO transceiver for high data rate millimeter-wave communications. Using the beam-tracking technique and two streams of QAM-64 signals, the proposed millimeter-wave MIMO transceiver can achieve a steady 5.3-Gb/s throughput for a single user in fast mobile environments. In the multiple-user MIMO scenario, by delivering 20 noncoherent data streams to eight four-channel user terminals, it achieves a downlink peak data rate of 50.73 Gb/s with the spectral efficiency of 101.5 b/s/Hz.


title: An Efficient Representation for Filtrations of Simplicial Complexes

abstract: A filtration over a simplicial complex K is an ordering of the simplices of K such that all prefixes in the ordering are subcomplexes of K. Filtrations are at the core of Persistent Homology, a major tool in Topological Data Analysis. To represent the filtration of a simplicial complex, the entire filtration can be appended to any data structure that explicitly stores all the simplices of the complex such as the Hasse diagram or the recently introduced Simplex Tree lAlgorithmica’14r. However, with the popularity of various computational methods that need to handle simplicial complexes, and with the rapidly increasing size of the complexes, the task of finding a compact data structure that can still support efficient queries is of great interest. ::: ::: This direction has been recently pursued for the case of maintaining simplicial complexes. For instance, Boissonnat et al. lAlgorithmica’17r considered storing the simplices that are maximal with respect to inclusion and Attali et al. lIJCGA’12r considered storing the simplices that block the expansion of the complex. Nevertheless, so far there has been no data structure that compactly stores the filtration of a simplicial complex, while also allowing the efficient implementation of basic operations on the complex. ::: ::: In this article, we propose a new data structure called the Critical Simplex Diagram (CSD), which is a variant of the Simplex Array List lAlgorithmica’17r. Our data structure allows one to store in a compact way the filtration of a simplicial complex and allows for the efficient implementation of a large range of basic operations. Moreover, we prove that our data structure is essentially optimal with respect to the requisite storage space. Finally, we show that the CSD representation admits fast construction algorithms for Flag complexes and relaxed Delaunay complexes.


title: Collision Detection for Underwater ROV Manipulator Systems

abstract: Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations.


title: Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System

abstract: Consider a centralized hierarchical cloud-based multimedia system (CMS) consisting of a resource manager, cluster heads, and server clusters, in which the resource manager assigns clients' requests for multimedia service tasks to server clusters according to the task characteristics, and then each cluster head distributes the assigned task to the servers within its server cluster. For such a complicated CMS, however, it is a research challenge to design an effective load balancing algorithm that spreads the multimedia service task load on servers with the minimal cost for transmitting multimedia data between server clusters and clients, while the maximal load limit of each server cluster is not violated. Unlike previous work, this paper takes into account a more practical dynamic multiservice scenario in which each server cluster only handles a specific type of multimedia task, and each client requests a different type of multimedia service at a different time. Such a scenario can be modelled as an integer linear programming problem, which is computationally intractable in general. As a consequence, this paper further solves the problem by an efficient genetic algorithm with an immigrant scheme, which has been shown to be suitable for dynamic problems. Simulation results demonstrate that the proposed genetic algorithm can efficiently cope with dynamic multiservice load balancing in CMS.


title: Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

abstract: We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., changing "screen is just the right size" to "screen is too small"). Our training data includes only sentences labeled with their attribute (e.g., positive or negative), but not pairs of sentences that differ only in their attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., "too small"). Our strongest method extracts content words by deleting phrases associated with the sentence's original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. On human evaluation, our best method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.


title: Joint semantic utterance classification and slot filling with recursive neural networks

abstract: In recent years, continuous space models have proven to be highly effective at language processing tasks ranging from paraphrase detection to language modeling. These models are distinctive in their ability to achieve generalization through continuous space representations, and compositionality through arithmetic operations on those representations. Examples of such models include feed-forward and recurrent neural network language models. Recursive neural networks (RecNNs) extend this framework by providing an elegant mechanism for incorporating both discrete syntactic structure and continuous-space word and phrase representations into a powerful compositional model. In this paper, we show that RecNNs can be used to perform the core spoken language understanding (SLU) tasks in a spoken dialog system, more specifically domain and intent determination, concurrently with slot filling, in one jointly trained model. We find that a very simple RecNN model achieves competitive performance on the benchmark ATIS task, as well as on a Microsoft Cortana conversational understanding task.


title: Kapur's and Otsu's based optimal multilevel image thresholding using social spider and firefly algorithm

abstract: Histogram based multilevel thresholding is one of the most aggressive methods to realize image segmentation. We have used histogram based multilevel thresholding as a gray image applying Firefly and social spider algorithm. For thresholding, we have worked on Kapur's and Otsu's methods to maximize the objective function values. We have used standard images, and the result shows that firefly algorithm has performed well than social spider algorithm as compared with optimal threshold values and computational time.


title: NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment

abstract: Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients.


title: Privacy and trust in Facebook photo sharing: age and gender differences

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to examine gender and age differences regarding various aspects of privacy, trust, and activity on one of the most popular Facebook activity – “photo sharing.” ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The data were collected using an online survey hosted by a web-based survey service for three weeks during December 2014-January 2015. The target audience comprised of Facebook users over 18 years engaged in sharing their photos on the platform. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Women and young Facebook users are significantly more concerned about the privacy of their shared photos. Meanwhile, users from older age groups are less active in using the site, in sharing photos, and in taking privacy-related protective measures. Interestingly, despite having more privacy concerns, young Facebook users display higher trust levels toward the platform than older users. Overall, in the study, there was an extremely significant difference in privacy attitudes among people under and over 35 years of age. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The main contribution of this study is new knowledge regarding the gender and age differences in various privacy-related aspects, trust, and activity. Findings from the study broadens the overall understanding of how these issues positively/negatively influence the photo-sharing activity on Facebook.


title: Popularity Prediction of Videos in YouTube as Case Study: A Regression Analysis Study

abstract: Consuming and watching videos in YouTube is an integral part of our daily lives. Predicting videos popularity is of great importance for many services and application ranging from supporting the design and evaluation of a wide range of systems, including the targeted advertising to earn more money, ensure an effective search and recommendation systems, and design an effective caching system. The goal of this paper is to use machine learning techniques to predict the popularity of YouTube videos. Here, we present two simple models from machine learning for predicting the popularity of videos based on videos parameters and the proposed popularity function. The experimental results on YouTube provide a good accuracy of popularity prediction and show the performance of our approach.


title: Improving the Peer Assessment Experience on MOOC Platforms

abstract: Massive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.


title: Subpixel rendering without color distortions for diamond-shaped PenTile displays

abstract: A novel subpixel rendering algorithm for diamond-shaped PenTile displays, which improves the apparent display resolution and reduces color fringing artifacts, is proposed in this work. We develop two types of filters: main filters and nonnegative filters. First, the main filters are derived to minimize the error between an original input image and a perceived image. Second, the nonnegative filters are modified from the main filters. While the main filters are optimized for improving the resolution, the nonnegative filters suppress color distortions effectively. We analyze local characteristics of an image and estimate the amount of color distortions in each region. Based on the color distortion analysis, we render the image by applying adaptive combinations of the main filters and the nonnegative filters. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms, by not only improving the apparent resolution but also suppressing color distortions effectively.


title: A Novel 24-Slots/10-Poles Winding Topology for Electric Machines

abstract: The fractional slots tooth concentrated windings are characterized with high space MMF harmonics which results to undesirable effects on electric machine, such as localised core saturation, eddy current loss in the rotor and noise and vibration. A new and high efficiency method is presented in this paper to reduce simultaneously the sub- and high MMF harmonics of these winding types. The method is based on doubling the number of stator slots, using two identical winding systems connected in series and shifted to each other for a specific angle, using stator core with different tooth width and using different turns per coil for the neighbouring phase coils. With the proposed technique the unwanted sub- and high winding MMF harmonics can be reduced or completely canceled.


title: A federated architecture for information management

abstract: An approach to the coordinated sharing and interchange of computerized information is described emphasizing partial, controlled sharing among autonomous databases. Office information systems provide a particularly appropriate context for this type of information sharing and exchange. A federated database architecture is described in which a collection of independent database systems are united into a loosely coupled federation in order to share and exchange information. A federation consists of components (of which there may be any number) and a single federal dictionary. The components represent individual users, applications, workstations, or other components in an office information system. The federal dictionary is a specialized component that maintains the topology of the federation and oversees the entry of new components. Each component in the federation controls its interactions with other components by means of an export schema and an import schema. The export schema specifies the information that a component will share with other components, while the import schema specifies the nonlocal information that a component wishes to manipulate. The federated architecture provides mechanisms for sharing data, for sharing transactions (via message types) for combining information from several components, and for coordinating activities among autonomous components (via negotiation). A prototype implementation of the federated database mechanism is currently operational on an experimental basis.


title: Double Actuator Unit with Planetary Gear Train for a Safe Manipulator

abstract: Control of a robot manipulator in contact with the environment is usually conducted by the direct feedback control system using a force-torque sensor or the indirect impedance control scheme. Although these methods have been successfully applied to many applications, simultaneous control of force and position cannot be achieved. Furthermore, collision safety has been of primary concern in recent years with emergence of service robots in direct contact with humans. To cope with such problems, redundant actuation has been used to enhance the performance of a position/force controller. In this paper, the novel design of a double actuator unit (DAU) composed of double actuators and a planetary gear train is proposed to provide the capability of simultaneous control of position and force as well as the improved collision safety. Since one actuator controls position and the other actuator modulates stiffness, DAU can control the position and stiffness simultaneously at the same joint. The torque exerted on the joint can be estimated without an expensive torque/force sensor. DAU is capable of detecting dynamic collision by monitoring the speed of the stiffness modulator. Upon detection of dynamic collision, DAU immediately reduces its joint stiffness according to the collision magnitude, thus providing the optimum collision safety. It is shown from various experiments that DAU can provide good performance of position tracking, force estimation and collision safety.


title: Automotive MIMO radar for urban environments

abstract: A high azimuth and elevation resolution multiple-input-multiple-output(MIMO) radar prototype with 16 Tx and 16 Rx antenna elements was developed in this work to address the autonomous driving vehicle challenges in complex urban environments. This article describes the technological scope of the developed prototype, details the performance challenges and discusses the system design considerations. The main goal of the developed automotive radar prototype is to achieve a high 2D angular resolution in the presence of a large number of radar echoes while maintaining an industry acceptable antenna aperture size and reasonable cost.


title: A classification and comparison of usability evaluation methods for interactive adaptive systems

abstract: Usability is a crucial factor for interactive adaptive system success. Several methods have been proposed to support the usability evaluation of interactive adaptive systems. However, the diversity of these methods leaves the evaluators wondering what would be the most appropriate usability evaluation methods for their specific goals. This paper examines the characteristics of usability evaluation methods for interactive adaptive systems and identifies the most relevant features for categorizing them. The comparison and classification of these methods will serve as a reference for researchers in the field of interactive adaptive systems and as a guide for the choice of suitable usability evaluation methods.


title: The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation

abstract: This paper describes the Arabic handwriting recognition systems proposed by A2iA to the NIST OpenHaRT2013 evaluation. These systems were based on an optical model using Long Short-Term Memory (LSTM) recurrent neural networks, trained to recognize the different forms of the Arabic characters directly from the image, without explicit feature extraction nor segmentation.Large vocabulary selection techniques and n-gram language modeling were used to provide a full paragraph recognition, without explicit word segmentation. Several recognition systems were also combined with the ROVER combination algorithm. The best system exceeded 80% of recognition rate.


title: EEG-based emotion recognition during watching movies

abstract: This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.


title: A Longitudinal Examination of the Link Between Youth Physical Fitness and Academic Achievement

abstract: BACKGROUND: Childhood obesity has been linked with other persistent health problems, but research is just beginning to examine its relationship with academic performance. This article tracks students longitudinally to examine the ways student physical fitness and changes in fitness align with school performance. ::: ::: METHODS: Using matched administrative data and individual growth modeling, we examine the relationship between academic achievement and overall physical fitness longitudinally from fourth to seventh and sixth to ninth grades for students in a California community. ::: ::: RESULTS: Comparing those who are persistently fit to those who are persistently unfit, we find disparities in both math and English language arts test scores. These academic disparities begin even before students begin fitness testing in fifth grade and are larger for girls and Latinos. Overall physical fitness is a better predictor of academic achievement than obesity as measured by body mass index. Socioeconomic status acts as a buffer for those who have poor physical fitness but strong academic performance. ::: ::: CONCLUSION: The findings indicate the presence of a physical fitness achievement gap that has consequences for potential students' future educational and health outcomes. This gap begins as early as fourth grade, which is before physical fitness testing begins in California.


Evaluation of the Rhode Island probation specialized domestic violence supervision unit


title: Realtime lane tracking of curved local road

abstract: A lane detection system is an important component of many intelligent transportation systems. We present a robust realtime lane tracking algorithm for a curved local road. First, we present a comparative study to find a good realtime lane marking classifier. Once lane markings are detected, they are grouped into many lane boundary hypotheses represented by constrained cubic spline curves. We present a robust hypothesis generation algorithm using a particle filtering technique and a RANSAC (random sample concensus) algorithm. We introduce a probabilistic approach to group lane boundary hypotheses into left and right lane boundaries. The proposed grouping approach can be applied to general part-based object tracking problems. It incorporates a likelihood-based object recognition technique into a Markov-style process. An experimental result on local streets shows that the suggested algorithm is very reliable


title: Crime Topic Modeling

abstract: The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime type labels. This information loss impacts our ability to not only understand the causes of crime, but also how to develop optimal crime prevention strategies. We apply machine learning methods to short narrative text descriptions accompanying crime records with the goal of discovering ecologically more meaningful latent crime classes. We term these latent classes ‘crime topics’ in reference to text-based topic modeling methods that produce them. We use topic distributions to measure clustering among formally recognized crime types. Crime topics replicate broad distinctions between violent and property crime, but also reveal nuances linked to target characteristics, situational conditions and the tools and methods of attack. Formal crime types are not discrete in topic space. Rather, crime types are distributed across a range of crime topics. Similarly, individual crime topics are distributed across a range of formal crime types. Key ecological groups include identity theft, shoplifting, burglary and theft, car crimes and vandalism, criminal threats and confidence crimes, and violent crimes. Though not a replacement for formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime.


title: A Pilot Randomized Controlled Trial of the Effects of Chair Yoga on Pain and Physical Function Among Community‐Dwelling Older Adults With Lower Extremity Osteoarthritis

abstract: OBJECTIVES ::: To determine effects of Sit 'N' Fit Chair Yoga, compared to a Health Education program (HEP), on pain and physical function in older adults with lower extremity osteoarthritis (OA) who could not participate in standing exercise. ::: ::: ::: DESIGN ::: Two-arm randomized controlled trial. ::: ::: ::: SETTING ::: One HUD senior housing facility and one day senior center in south Florida. ::: ::: ::: PARTICIPANTS ::: Community-dwelling older adults (N = 131) were randomly assigned to chair yoga (n = 66) or HEP (n = 65). Thirteen dropped after assignment but prior to the intervention; six dropped during the intervention; 106 of 112 completed at least 12 of 16 sessions (95% retention rate). ::: ::: ::: INTERVENTIONS ::: Participants attended either chair yoga or HEP. Both interventions consisted of twice-weekly 45-minute sessions for 8 weeks. ::: ::: ::: MEASUREMENTS ::: Primary: pain, pain interference; secondary: balance, gait speed, fatigue, functional ability measured at baseline, after 4 weeks of intervention, at the end of the 8-week intervention, and post-intervention (1 and 3 months). ::: ::: ::: RESULTS ::: The chair yoga group showed greater reduction in pain interference during the intervention (P = .01), sustained through 3 months (P = .022). WOMAC pain (P = .048), gait speed (P = .024), and fatigue (P = .037) were improved in the yoga group during the intervention (P = .048) but improvements were not sustained post intervention. Chair yoga had no effect on balance. ::: ::: ::: CONCLUSION ::: An 8-week chair yoga program was associated with reduction in pain, pain interference, and fatigue, and improvement in gait speed, but only the effects on pain interference were sustained 3 months post intervention. Chair yoga should be further explored as a nonpharmacologic intervention for older people with OA in the lower extremities. ::: ::: ::: TRIAL REGISTRATION ::: ClinicalTrials.gov: NCT02113410.


title: Book recommendation using Neo4j graph database in BibTeX book metadata

abstract: In digital era, book has an important role in life. There are not only a lot of books for different purpose. But also, there are many book metadata which can use for another reason, such as book recommendation. By processing the book metadata, an information can be given to user that needs book recommendation. By combining BibTeX book metadata and Graph Database from Neo4j, data from metadata can be processed. Then, with cypher query by inputting author's parameter or book type's parameter, user can get book recommendation based on their input's criteria. The result is exactly the same with process the metadata manually in relational database. Neo4j, from this paper, takes 180 milliseconds to execute cypher query with author's criteria and takes 184 milliseconds to execute cypher query with book type's criteria.


title: The smart floor: a mechanism for natural user identification and tracking

abstract: We have created a system for identifying people based on their footstep force profiles and have tested its accuracy against a large pool of footstep data. This floor system may be used to identify users transparently in their everyday living and working environments. We have created user footstep models based on footstep profile features and have been able to achieve a recognition rate of 93%. We have also shown that the effect of footwear is negligible on recognition accuracy.


title: Service robotics for the home: a state of the art review

abstract: This paper provides a review of the state of the art of service robotics for the home. We were primarily interested in major features of commercially available robots and prototype research robots, as well as remaining research challenges and gaps for the future. The review was undertaken in support of an initiative to build advanced smart homes for disabled military service members and their families. These homes will be constructed with the aim to include advanced home automation, robotics, and other assistive technologies.


title: BIGEYE (THUNNUS OBESUS) BY-CATCH ESTIMATES FROM THE ALBACORE SPANISH SURFACE FISHERY IN THE NORTHEAST ATLANTIC: 2002-2003

abstract: SUMMARY Data on by-catch of bigeye (Thunnus obesus) by the Spanish surface fleets, both troll and bait boats, targeting albacore (Thunnus alalunga) and bluefin (Thunnus thynnus) in the Bay of Biscay and northeastern Atlantic fishing grounds are presented here. Monthly catch statistics and samples on length distribution have been collected in the main landing fishing ports along to the north Spanish coast during the summer season fishery for the years of 2002 and 2003. Estimates of Task I and Task II data are included. RESUME


title: Source Camera Identification Using Large Components of Sensor Pattern Noise

abstract: Digital image forensics has attracted a lot of attention recently for its role in identifying the origin of digital image. Although different forensic approaches have been proposed, one of the most popular approaches is to rely on the imaging sensor pattern noise, where each sensor pattern noise uniquely corresponds to an imaging device and serves as the intrinsic fingerprint. The correlation-based detection is heavily dependent upon the accuracy of the extracted pattern noise. In this work, we discuss the way to extract the pattern noise, in particular, explore the way to make better use of the pattern noise. Unlike current methods that directly compare the whole pattern noise signal with the reference one, we propose to only compare the large components of these two signals. Our detector can better identify the images taken by different cameras. In the meantime, it needs less computational complexity.


title: Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation

abstract: We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point, instead of a correct structure, is observed in learning. We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1-BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation. In our experiment bandit feedback is obtained by evaluating BLEU on reference translations without revealing them to the algorithm. This can be thought of as a simulation of interactive machine translation where an SMT system is personalized by a user who provides single point feedback to predicted translations. Our experiments show that our approach improves translation quality and is comparable to approaches that employ more informative feedback in learning.


title: Opening Platforms: When, How and Why?

abstract: Platform-mediated networks encompass several distinct types of participants, including end users, complementors, platform providers who facilitate users' access to complements, and sponsors who develop platform technologies. Each of these roles can be opened - that is, structured to encourage participation - or closed. This paper reviews factors that motivate decisions to open or close mature platforms. At the platform provider and sponsor levels, these decisions entail: 1) interoperating with established rival platforms; 2) licensing additional platform providers; or 3) broadening sponsorship. With respect to end users and complementors, decisions to open or close a mature platform involve: 1) backward compatibility with prior platform generations; 2) securing exclusive rights to certain complements; or 3) absorbing complements into the core platform. Over time, forces tend to push both proprietary and shared platforms toward hybrid governance models characterized by centralized control over platform technology (i.e., closed sponsorship) and shared responsibility for serving users (i.e., an open provider role).


title: XNLI: Evaluating Cross-lingual Sentence Representations

abstract: State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.


title: Voltage buffer compensation using Flipped Voltage Follower in a two-stage CMOS op-amp

abstract: In Miller and current buffer compensation techniques, the compensation capacitor often loads the output node. If a voltage buffer is used in feedback, the compensation capacitor obviates the loading on the output node. In this paper, we introduce an implementation of a voltage buffer compensation using a Flipped Voltage Follower (FVF) for stabilizing a two-stage CMOS op-amp. The op-amps are implemented in a 180-nm CMOS process with a power supply of 1.8V while operating with a quiescent current of 110µA. Results indicate that the proposed voltage buffer compensation using FVF improves the Unity Gain Frequency from 5.5MHz to 12.2MHz compared to Miller compensation. Also, the proposed technique enhances the transient response while lowering the compensation capacitance by 47% and 17.7% compared to Miller and common-drain compensation topologies. Utilization of FVF or its variants as a voltage buffer in a feedback compensation network has wide potential applications in the analog design space.


title: Learning to Segment Every Thing

abstract: Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to ~100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world.


title: Mobile Manipulation and Mobility as Manipulation-Design and Algorithms of RoboSimian

abstract: This article presents the hardware design and software algorithms of RoboSimian, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain. The robot has generalized limbs and hands capable of mobility and manipulation, along with almost fully hemispherical three-dimensional sensing with passive stereo cameras. The system is semiautonomous, enabling low-bandwidth, high latency control operated from a standard laptop. Because limbs are used for mobility and manipulation, a single unified mobile manipulation planner is used to generate autonomous behaviors, including walking, sitting, climbing, grasping, and manipulating. The remote operator interface is optimized to designate, parametrize, sequence, and preview behaviors, which are then executed by the robot. RoboSimian placed fifth in the DARPA Robotics Challenge Trials, demonstrating its ability to perform disaster recovery tasks in degraded human environments.


title: Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression

abstract: The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.


title: MockAPI: an agile approach supporting API-first web application development

abstract: In the last years, agile development methodologies have been widely adopted. However, they still lack support for API requirements while, at the same time, public RESTful APIs are fueling a rapid growth of web applications providing services built on other services. On the other hand, whereas Model-Driven Development techniques successfully increase the productivity in the development of data-intensive web applications, they lack the agility required when developing heterogeneous web applications with frequent requirement changes. In this paper we introduce MockAPI, an approach based on annotating user interface mockups that combines the advantages of agile approaches and Model-Driven Development. We introduce a metamodel for annotations and demonstrate how to derive running API prototypes as starting point for agile development. RESTful API best practices and API-first development are introduced into the agile process. The MockAPI approach defines a set of constraints to accelerate the development of web applications. We also show the results of a brief validation applying MockAPI to popular web sites.


title: Integrating heuristic evaluation with cognitive walkthrough: development of a hybrid usability inspection method.

abstract: Developing more usable healthcare information systems has become an important goal in health informatics. Although methods from usability engineering have appeared and been effectively applied in the design and evaluation of healthcare systems, there continues to be reports of deployment of unusable systems and issues with adoption of healthcare IT worldwide. In this paper we propose a new cost-effective usability engineering approach for healthcare IT that integrates two of the major usability inspection approaches (heuristic evaluation and cognitive walkthrough) into one combined approach that leverages the advantages of both heuristic evaluation and cognitive walkthrough. The approach will be described as will a pilot application of the method in evaluating the usability of a well-known electronic health record system. Implications and future work will also be described.


title: On the Capacity Comparison Between MIMO-NOMA and MIMO-OMA

abstract: Non-orthogonal multiple access (NOMA) has been shown in the literature to have a better performance than OMA in terms of sum channel capacity; however, the capacity superiority of NOMA over OMA has been only proved for single antenna systems, and the proof for the capacity superiority of multiple-input multiple-output NOMA (MIMO-NOMA) over conventional MIMO-OMA has not been available yet. In this paper, we will provide our proof to demonstrate that the MIMO-NOMA is strictly better than MIMO-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMO-OMA, there is a power split for which MIMO-NOMA can achieve rate pairs that are strictly larger. Based on this result, we prove that the MIMO-NOMA can also achieve a larger sum ergodic capacity than MIMO-OMA. Our analytical results are verified by simulations.


title: Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

abstract: Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.


Inferring the meaning of chord sequences via lyrics


title: Noisy Networks for Exploration

abstract: We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $\epsilon$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.


title: Modelling polycrystalline semiconductor solar cells”, Thin Solid Films

abstract: Abstract An overview is given of various electronic effects present in polycrystalline thin film solar cells, which do not occur in standard crystalline Si solar cells. It is explained how these effects are treated numerically in a numerical solar cell simulation tool, SCAPS, developed at the author's institute. The capabilities and limitations of SCAPS are discussed. Simulation examples of current–voltage, capacitance–voltage and capacitance–frequency characteristics are given. The agreement between the simulations and measurements is shown and discussed, both for CdTe and for Cu(In,Ga)Se 2 solar cells.


Puberty Suppression Followed by Cross-sex Hormones and Gender Reassignment Surgery : A Prospective Follow-up of Gender Dysphoric Adolescents into Adulthood


title: A Survey of 3D Face Recognition Methods

abstract: Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren't capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future.


title: High precision linear motor control via relay-tuning and iterative learning based on zero-phase filtering

abstract: In this paper, with a modest amount of modeling effort, a feedback-feedforward control structure is proposed for precision motion control of a permanent magnet linear motor for applications which are inherently repetitive in terms of the motion trajectories. First, a proportional-integral-derivative (PID) feedback controller is designed using an automatic relay tuning method. An iterative learning controller based on zero-phase filtering is applied as feedforward controller to the existing relay-tuned PID feedback controller to enhance the trajectory tracking performance by utilizing the experience gained from the repeated execution of the same operations. Experimental results are presented to demonstrate the practical appeal and effectiveness of the proposed scheme.


title: Residual echo reduction based on MMSE estimator in acoustic echo canceller

abstract: In this paper, a residual echo cancellation method is proposed that uses an estimation of the minimum mean-square error (MMSE) based on a statistical model of a speech signal and an echo signal. After the suppression of the echo signal based on the adaptive filter, residual echo is further reduced by the proposed MMSE estimator and the results are compared with the conventional Wiener filter based method.


Network Forensic Investigation of HTTPS Protocol


CEN@Amrita FIRE 2016: Context based Character Embeddings for Entity Extraction in Code-Mixed Text


title: About malicious software in smartphones

abstract: Phones with some of the capabilities of modern computers also have the same kind of drawbacks. These phones are commonly referred to as smartphones. They have both phone and personal digital assistant (PDA) functionality. Typical to these devices is to have a wide selection of different connectivity options from general packet radio service (GPRS) data transfer to multi media messages (MMS) and wireless local area network (WLAN) capabilities. They also have standardized operating systems, which makes smartphones a viable platform for malware writers. Since the design of the operating systems is recent, many common security holes and vulnerabilities have been taken into account during the design. However, these precautions have not fully protected these devices. Even now, when smartphones are not that common, there is a handful of viruses for them. In this paper we will discuss some of the most typical viruses in the mobile environment and propose guidelines and predictions for the future.


title: Latent Intention Dialogue Models

abstract: Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructing deterministic models for learning dialogue sentences that fail to capture natural conversational variability. In this paper, we propose a Latent Intention Dialogue Model (LIDM) that employs a discrete latent variable to learn underlying dialogue intentions in the framework of neural variational inference. In a goal-oriented dialogue scenario, these latent intentions can be interpreted as actions guiding the generation of machine responses, which can be further refined autonomously by reinforcement learning. The experimental evaluation of LIDM shows that the model out-performs published benchmarks for both corpus-based and human evaluation, demonstrating the effectiveness of discrete latent variable models for learning goal-oriented dialogues.


title: Growing up with Parental Alcohol Abuse: Exposure to Childhood Abuse, Neglect, and Household Dysfunction.

abstract: Abstract Objective: This study is a detailed examination of the association between parental alcohol abuse (mother only, father only, or both parents) and multiple forms of childhood abuse, neglect, and other household dysfunction, known as adverse childhood experiences (ACEs). Method: A questionnaire about ACEs including child abuse, neglect, household dysfunction, and exposure to parental alcohol abuse was completed by 8629 adult HMO members to retrospectively assess the relationship of growing up with parental alcohol abuse to 10 ACEs and multiple ACEs (ACE score). Results: Compared to persons who grew up with no parental alcohol abuse, the adjusted odds ratio for each category of ACE was approximately 2 to 13 times higher if either the mother, father, or both parents abused alcohol ( p both an alcohol-abusing mother and father had the highest likelihood of ACEs. The mean number of ACEs for persons with no parental alcohol abuse, father only, mother only, or both parents was 1.4, 2.6, 3.2, and 3.8, respectively ( p Conclusion: Although the retrospective reporting of these experiences cannot establish a causal association with certainty, exposure to parental alcohol abuse is highly associated with experiencing adverse childhood experiences. Improved coordination of adult and pediatric health care along with related social and substance abuse services may lead to earlier recognition, treatment, and prevention of both adult alcohol abuse and adverse childhood experiences, reducing the negative sequelae of ACEs in adolescents and adults.


title: Surface Networks

abstract: We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. Recent works have developed models that exploit the intrinsic geometry of manifolds and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants, which learn from the local metric tensor via the Laplacian operator. Despite offering excellent sample complexity and built-in invariances, intrinsic geometry alone is invariant to isometric deformations, making it unsuitable for many applications. To overcome this limitation, we propose several upgrades to GNNs to leverage extrinsic differential geometry properties of three-dimensional surfaces, increasing its modeling power. In particular, we propose to exploit the Dirac operator, whose spectrum detects principal curvature directions --- this is in stark contrast with the classical Laplace operator, which directly measures mean curvature. We coin the resulting models \emph{Surface Networks (SN)}. We prove that these models define shape representations that are stable to deformation and to discretization, and we demonstrate the efficiency and versatility of SNs on two challenging tasks: temporal prediction of mesh deformations under non-linear dynamics and generative models using a variational autoencoder framework with encoders/decoders given by SNs.


title: The Motivation at Work Scale: Validation Evidence in Two Languages:

abstract: The Motivation at Work Scale (MAWS) was developed in accordance with the multi dimensional conceptualization of motivation postulated in self  determination theory. The authors examined the structure of the MAWS in a group of 1,644 workers in two different languages, English and French. Results obtained from these samples suggested that the structure of motivation at work across languages is consistently organized into four different types: intrinsic motivation, identified regulation, introjected regulation, and external regulation. The MAWS subscales were predictably associated with organizational behavior constructs. The importance of this new multidimensional scale to the development of new work motivation research is discussed.


Eyelid Dermatitis Caused by Allergic Contact to Acrylates in Artificial Nails


title: Cell Segmentation Proposal Network for Microscopy Image Analysis

abstract: Accurate cell segmentation is vital for the development of reliable microscopy image analysis methods. It is a very challenging problem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell segmentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.


title: Composing Music by Composing Rules: Design and Usage of a Generic Music Constraint System

abstract: This research presents the design, usage, and evaluation of a highly generic music constraint system called Strasheela. Strasheela simplifies the definition of musical constraint satisfaction problems (CSP) by predefining building blocks required for such problems. At the same time, Strasheela preserves a high degree of generality and is reasonably efficient. Strasheela is highly generic, because it is highly programmable. In particular, three fundamental components are more programmable in Strasheela compared with existing music constraint systems: the music representation, the rule application mechanism, and the search process. Strasheela features an expressive symbolic music representation. This representation supports explicitly storing score information by sets of score objects, their attributes, and their hierarchic nesting. Any information available in the score is accessible from any object in the score and can be used to obtain derived information. The representation is complemented by the notion of variables: score information can be unknown and such information can be constrained. This research proposes a rule formalism which combines convenience and full user control to express which score variable sets are constrained by a given rule. A rule is a first-class function. A rule application mechanism is a higher-order function. A rule application mechanism traverses the score in order to apply a given rule to variable sets. This text presents rule application mechanisms suitable for a large set of musical CSPs and reproduces important mechanisms of existing systems. Strasheela is founded on a constraint programming model which makes the search process programmable at a high-level. The Strasheela user can optimise the search for a particular constraint satisfaction problem by programming a distribution strategy (a dynamic variable and value ordering) independent of the problem definition. Special distribution strategies for efficiently solving various musical CSPs – including complex polyphonic problems – are presented.


title: A Brief Review of Facial Emotion Recognition Based on Visual Information

abstract: Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling "end-to-end" learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.


title: Effects of action observation on corticospinal excitability: Muscle specificity, direction, and timing of the mirror response

abstract: Abstract Many human behaviours and pathologies have been attributed to the putative mirror neuron system, a neural system that is active during both the observation and execution of actions. While there are now a very large number of papers on the mirror neuron system, variations in the methods and analyses employed by researchers mean that the basic characteristics of the mirror response are not clear. This review focuses on three important aspects of the mirror response, as measured by modulations in corticospinal excitability: (1) muscle specificity; (2) direction; and (3) timing of modulation. We focus mainly on electromyographic (EMG) data gathered following single-pulse transcranial magnetic stimulation (TMS), because this method provides precise information regarding these three aspects of the response. Data from paired-pulse TMS paradigms and peripheral nerve stimulation (PNS) are also considered when we discuss the possible mechanisms underlying the mirror response. In this systematic review of the literature, we examine the findings of 85 TMS and PNS studies of the human mirror response, and consider the limitations and advantages of the different methodological approaches these have adopted in relation to discrepancies between their findings. We conclude by proposing a testable model of how action observation modulates corticospinal excitability in humans. Specifically, we propose that action observation elicits an early, non-specific facilitation of corticospinal excitability (at around 90 ms from action onset), followed by a later modulation of activity specific to the muscles involved in the observed action (from around 200 ms). Testing this model will greatly advance our understanding of the mirror mechanism and provide a more stable grounding on which to base inferences about its role in human behaviour.


title: Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

abstract: Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable reinforcement learning (RL) with an epistemic and aleatoric risk element. Our experiments illustrate the usefulness of the resulting decomposition in active learning and reliable RL.