title: The association of infrapatellar fat pad oedema with patellar maltracking: a case–control study

abstract: OBJECTIVE ::: To look for any association between oedema in the superolateral portion of the infrapatellar fat pad and patellar maltracking. ::: ::: ::: MATERIALS AND METHODS ::: We compared two groups of knee MRI with regard to five patellar maltracking parameters. The first group included 100 knees with evidence of oedema in the superolateral aspect of the infrapatellar fat pad (the study group). The second group included another 100 knee MRI that had a normal infrapatellar fat pad (the control group). The five patellar maltracking parameters assessed were the trochlear depth, tibial tuberosity-trochlear groove distance (TTTG), patellar translation, patellofemoral angle (PFA) and the Insall-Salvati index. ::: ::: ::: RESULTS ::: There was a statistically significant difference in the Insall-Salvati index, patellar translation and PFA between the two groups (p value of <0.001, <0.001 and 0.004 respectively, Student's t test). There was a higher prevalence of patella alta, lateral patellar displacement (LPD) and lateral patellar tilt in the study group (p value of <0.001, <0.001 and 0.011 respectively, Fisher's exact test). Sixty out of 100 knees in the study group had at least one abnormal patellar maltracking parameter in comparison to 16 out of 100 knees in the control group (p < 0.001, Fisher's exact test). ::: ::: ::: CONCLUSION ::: Oedema in the superolateral portion of Hoffa's fat pad, the MRI feature of fat pad impingement, is associated with patellar maltracking.


title: Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

abstract: Video super-resolution (VSR) has become even more important recently to provide high resolution (HR) contents for ultra high definition displays. While many deep learning based VSR methods have been proposed, most of them rely heavily on the accuracy of motion estimation and compensation. We introduce a fundamentally different framework for VSR in this paper. We propose a novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation. With our approach, an HR image is reconstructed directly from the input image using the dynamic upsampling filters, and the fine details are added through the computed residual. Our network with the help of a new data augmentation technique can generate much sharper HR videos with temporal consistency, compared with the previous methods. We also provide analysis of our network through extensive experiments to show how the network deals with motions implicitly.


title: Through a Glass Darkly: Information Technology Design, Identity Verification, and Knowledge Contribution in Online Communities

abstract: A variety of information technology (IT) artifacts, such as those supporting reputation management and digital archives of past interactions, are commonly deployed to support online communities. Despite their ubiquity, theoretical and empirical research investigating the impact of such IT-based features on online community communication and interaction is limited. Drawing on the social psychology literature, we describe an identity-based view to understand how the use of IT-based features in online communities is associated with online knowledge contribution. Specifically, the use of four categories of IT artifacts---those supporting virtual co-presence, persistent labeling, self-presentation, and deep profiling---is proposed to enhance perceived identity verification, which thereafter promotes satisfaction and knowledge contribution. To test the theoretical model, we surveyed more than 650 members of two online communities. In addition to the positive effects of community IT artifacts on perceived identity verification, we also find that perceived identity verification is strongly linked to member satisfaction and knowledge contribution. This paper offers a new perspective on the mechanisms through which IT features facilitate computer-mediated knowledge sharing, and it yields important implications for the design of the supporting IT infrastructure.


title: SEMILAR: The Semantic Similarity Toolkit

abstract: We present in this paper SEMILAR, the SEMantic simILARity toolkit. SEMILAR implements a number of algorithms for assessing the semantic similarity between two texts. It is available as a Java library and as a Java standalone ap-plication offering GUI-based access to the implemented semantic similarity methods. Furthermore, it offers facilities for manual se-mantic similarity annotation by experts through its component SEMILAT (a SEMantic simILarity Annotation Tool).


title: Intelligent classification and visualization of network scans

abstract: Network scans are a common first step in a network intrusion attempt. In order to gain information about a potential network intrusion, it is beneficial to analyze these network scans. Statistical methods such as wavelet scalogram analysis have been used along with visualization techniques in previous methods. However, applying these statistical methods causes a substantial amount of data loss. This paper presents a study of using associative memory learning techniques to directly compare network scans in order to create a classification which can be used by itself or in conjunction with existing visualization techniques to better characterize the sources of these scans. This produces an integrated system of visual and intelligent analysis which is applicable to real world data.


title: Drug-induced Anaphylaxis Documented in Electronic Health Records

abstract: Background Although drugs represent a common cause of anaphylaxis, few large studies of drug-induced anaphylaxis have been performed. Objective To describe the epidemiology and validity of reported drug-induced anaphylaxis in the electronic health records (EHRs) of a large United States health care system. Methods Using EHR drug allergy data from 1995 to 2013, we determined the population prevalence of anaphylaxis including anaphylaxis prevalence over time, and the most commonly implicated drugs/drug classes reported to cause anaphylaxis. Patient risk factors for drug-induced anaphylaxis were assessed using a logistic regression model. Serum tryptase and allergist visits were used to assess the validity and follow-up of EHR-reported anaphylaxis. Results Among 1,756,481 patients, 19,836 (1.1%) reported drug-induced anaphylaxis; penicillins (45.9 per 10,000), sulfonamide antibiotics (15.1 per 10,000), and nonsteroidal anti-inflammatory drugs (NSAIDs) (13.0 per 10,000) were most commonly implicated. Patients with white race (odds ratio [OR] 2.38, 95% CI 2.27-2.49), female sex (OR 2.20, 95% CI 2.13-2.28), systemic mastocytosis (OR 4.60, 95% CI 2.66-7.94), Sjogren's syndrome (OR 1.94, 95% CI 1.47-2.56), and asthma (OR 1.50, 95% CI 1.43-1.59) had an increased odds of drug-induced anaphylaxis. Serum tryptase was performed in 135 ( Conclusions EHR-reported anaphylaxis occurred in approximately 1% of patients, most commonly from penicillins, sulfonamide antibiotics, and NSAIDs. Females, whites, and patients with mastocytosis, Sjogren's syndrome, and asthma had increased odds of reporting drug-induced anaphylaxis. The low observed frequency of tryptase testing and specialist evaluation emphasize the importance of educating providers on anaphylaxis management.


title: Electromagnetic performances analysis of a new magnetic-planetary-geared permanent magnet brushless machine for hybrid electric vehicles

abstract: This paper proposes a new magnetic-planetary-geared permanent magnet brushless (MPG-PMBL) machine for hybrid electric vehicles. The key is to integrate a magnetic planetary gear into permanent magnet brushless machine. Thus, the resulting machine can achieve both power split and mixing flexibly, and therefore, can obtain ultralow emission and high fuel efficiency at different operational modes. Moreover, by incorporating the concept of non-contact transmission mechanisms of magnetic gear, the proposed machine possesses the merits of small size, light weight, reliability, reduced maintenance and inherent overload protection. By using the finite element method, the electromagnetic performances are analyzed. Finally, multi-operational modes are analyzed to evaluate the proposed system.


title: The ball-pivoting algorithm for surface reconstruction

abstract: The Ball-Pivoting Algorithm (BPA) computes a triangle mesh interpolating a given point cloud. Typically, the points are surface samples acquired with multiple range scans of an object. The principle of the BPA is very simple: Three points form a triangle if a ball of a user-specified radius p touches them without containing any other point. Starting with a seed triangle, the ball pivots around an edge (i.e., it revolves around the edge while keeping in contact with the edge's endpoints) until it touches another point, forming another triangle. The process continues until all reachable edges have been tried, and then starts from another seed triangle, until all points have been considered. The process can then be repeated with a ball of larger radius to handle uneven sampling densities. We applied the BPA to datasets of millions of points representing actual scans of complex 3D objects. The relatively small amount of memory required by the BPA, its time efficiency, and the quality of the results obtained compare favorably with existing techniques.


title: Indications and interpretation of esophageal function testing

abstract: Esophageal symptoms are common, and can arise from mucosal, motor, functional, and neoplastic processes, among others. Judicious use of diagnostic testing can help define the etiology of symptoms and can direct management. Endoscopy, esophageal high-resolution manometry (HRM), ambulatory pH or pH-impedance manometry, and barium radiography are commonly used for esophageal function testing; functional lumen imaging probe is an emerging option. Recent consensus guidelines have provided direction in using test findings toward defining mechanisms of esophageal symptoms. The Chicago Classification describes hierarchical steps in diagnosing esophageal motility disorders. The Lyon Consensus characterizes conclusive evidence on esophageal testing for a diagnosis of gastroesophageal reflux disease (GERD), and establishes a motor classification of GERD. Taking these recent advances into consideration, our discussion focuses primarily on the indications, technique, equipment, and interpretation of esophageal HRM and ambulatory reflux monitoring in the evaluation of esophageal symptoms, and describes indications for alternative esophageal tests.


title: Learning Time-Sensitive Strategies in Space Fortress

abstract: Although there has been remarkable progress and impressive performance on reinforcement learning (RL) on Atari games, there are many problems with challenging characteristics that have not yet been explored in Deep Learning for RL. These include reward sparsity, abrupt context-dependent reversals of strategy and time-sensitive game play. In this paper, we present Space Fortress, a game that incorporates all these characteristics and experimentally show that the presence of any of these renders state of the art Deep RL algorithms incapable of learning. Then, we present our enhancements to an existing algorithm and show big performance increases through each enhancement through an ablation study. We discuss how each of these enhancements was able to help and also argue that appropriate transfer learning boosts performance.


title: Canola (Brassica rapa) Plant Density Influences Tartary Buckwheat (Fagopyrum tataricum) Interference, Biomass, and Seed Yield'

abstract: Field experiments were conducted at Vegreville, Alberta, in 1990, 1991, and 1992 to investigate the influence of canola plant density and row spacing (1991 and 1992 only) on interference from Tartary buckwheat. Nonlinear regres- sion equations incorporating both canola and Tartary buck- wheat density provided good descriptions of the data. Parameter estimates for both weed and crop density were significant (P< 0.05) but did not differ between row spacings. Where no Tartary buckwheat plants were present, canola yield was little affected by canola plant density. At a given Tartary buckwheat density, canola seed yield increased as canola plant density increased, while Tartary buckwheat shoot weight and seed yield decreased. None of the factors significantly affected canola 1000-seed weight, or oil or pro- tein levels in the canola seed. The results suggest that seeding canola to achieve a density of approximately 200 plants m2 will significantly reduce the impact of weeds on canola yield, as well as reduce weed biomass and seed yield. Nomencla- ture: Tartary buckwheat, Fagopyrum tataricum L. J. Gaertn #3 FAGTA; canola, Brassica rapa L. 'Tobin.' Additional index words: Plant density, oil, protein, nonchemi- cal weed control, weed interference, FAGTA.


title: Testing Strategies for the Automated Grading of Student Programs

abstract: Enrollments in introductory computer science courses are growing rapidly, thereby taxing scarce teaching resources and motivating the increased use of automated tools for program grading. Such tools commonly rely on regression testing methods from industry. However, the goals of automated grading differ from those of testing for software production. In academia, a primary motivation for testing is to provide timely and accurate feedback to students so that they can understand and fix defects in their programs. Testing strategies for program grading are therefore distinct from those of traditional software testing. This paper enumerates and describes a number of testing strategies that improve the quality of feedback for different types of programming assignments.


title: Fuzzy PID control of lower limb exoskeleton for elderly mobility

abstract: This paper presents investigations into the development of an assistive device for elderly mobility. An exoskeleton is designed as an assistive device, to enhance the lower extremity and provide supporting torque to augment the torque of hip and knee during the walking cycle. A humanoid and actuated exoskeleton frame are designed in solid works and then imported to Visual Nastran 4D virtual environment. Fuzzy PID Control algorithm uses to control the actuated exoskeleton. Simulated walking tasks are performed with a humanoid wearing the exoskeleton. It is assumed that the humanoid representing an elderly person, is able to provide 60% of the whole torque required to complete the gait cycle, and the assistive device providing 40% of the overall torque.


title: Steganography in Silence Intervals of Speech

abstract: This paper presents a new approach for hiding information in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. This method can be used simultaneously with other methods.


title: Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping

abstract: This paper presents an integrated environment for the rapid prototyping of a robust fuzzy proportional-integral-derivative (PID) controller that allows rapid realization of novel designs. Both the design of the fuzzy PID controller and its integration with the classical PID in a global control system are developed. The architecture of the fuzzy PID controller is basically composed of three parallel fuzzy subcontrollers. Then, the parallel subcontrollers are grouped together to form the overall fuzzy PID controller. The fuzzy proportional, integral, and derivative gains are direct output from the parallel fuzzy subcontrollers and are derived in the error domain. Thus, the proposed architecture presents an alternative to control schemes employed so far. The integrated controller is formulated and implemented in real time, using the speed control of a brushless drive system as a test bed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed hybrid fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.


title: Cloud / Fog Networking in the Internet of Vehicles

abstract: Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information. However, other performance requirements such as life cycle of nodes, stability and security are not set as primary design objectives. This makes data collection ability of vehicular nodes in real application environment inferior. By considering the features of nodes in wireless IoV, such as large scales of deployment, volatility and low time delay, an efficient data collection algorithm is proposed for mobile vehicle network environment. An adaptive sensing model is designed to establish vehicular data collection protocol. The protocol adopts group management in model communication. The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes. It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes. In addition, secure data collection between sensing nodes is undertaken as well. The simulation and experiments show that the vehicular node can realize secure and real-time data collection. Moreover, the proposed algorithm is superior in vehicular network life cycle, power consumption and reliability of data collection by comparing to other algorithms.


title: Adaptive 360-degree video streaming using layered video coding

abstract: Virtual reality and 360-degree video streaming are growing rapidly; however, streaming 360-degree video is very challenging due to high bandwidth requirements. To address this problem, the video quality is adjusted according to the user viewport prediction. High quality video is only streamed for the user viewport, reducing the overall bandwidth consumption. Existing solutions use shallow buffers limited by the accuracy of viewport prediction. Therefore, playback is prone to video freezes which are very destructive for the Quality of Experience(QoE). We propose using layered encoding for 360-degree video to improve QoE by reducing the probability of video freezes and the latency of response to the user head movements. Moreover, this scheme reduces the storage requirements significantly and improves in-network cache performance.


title: Beaver: Towards a Declarative Schema Mapping

abstract: Schema mapping is used to transform data to a desired schema from data sources with different schemas. Manually writing complete schema mapping specifications requires a deep understanding of the source and target schemas, which can be burdensome for the user. Programming By Example (PBE) schema mapping methods allow the user to describe the schema mapping using data records. However, real data records are still harder to specify compared to other useful insights about the desired schema mapping the user might have. In this project, we develop a new schema mapping technique, Beaver, that enables an interaction model that gives the user more flexibility in describing the desired schema mapping. The end user is not limited to providing exact and complete target schema data examples but may also provide incomplete or ambiguous examples. Moreover, the user can provide other types of descriptions, like data type or value range, about the target schema. We design an explore-and-verify search-based algorithm to efficiently discover all satisfying schema mapping specifications. We implemented a prototype of our schema mapping technique and experimentally evaluated the efficiency of the system in handling traditional PBE schema mapping test cases, as well as our newly-proposed declarative schema mapping test cases. The experiment results show that the declarative queries, which we believe are easier for non-expert user to input, often cost around zero to five seconds more than the traditional PBE queries. This suggests we retain a system efficiency comparable to traditional PBE schema mapping systems.


title: PoTrojan: powerful neural-level trojan designs in deep learning models

abstract: With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.


Vegetarianism . A blossoming field of study q


title: The relationship between quality management practices and their effects on quality outcomes

abstract: Recent research into quality management has examined the relationship between quality management and performance. The purpose of this study is to identify the relationships between quality management practices, and to examine the direct and indirect effects of these practices on quality outcomes by means of replication research. The paper uses a path analysis and a research model is tested using cross-section data collected from 106 certified firms in Spain. The findings support the relationships between quality management practices and the positive impact of these practices on quality outcomes. Evidence is also found confirming previous research showing that a firm could transfer the organizational forms and behaviours underlying quality management to other countries with similar cultures. However, as minor differences emerge, managers should consider the cultural issues. The contribution of the paper is that it provides empirical support for direct and indirect effects of quality management on performance in Spain compared to previous studies carried out in USA and Korea.


A simple model for probabilistic interval forecasts of wind power chaotic time series


title: Convolutional Neural Networks for Time Series Classification

abstract: This article concerns identifying objects generating signals from various sensors. Instead of using traditional hand-made time series features we feed the signals as input channels to a convolutional neural network. The network learned low- and high-level features from data. We describe the process of data preparation, filtering, and the structure of the convolutional network. Experiment results showed that the network was able to learn to recognize objects with high accuracy.


Similarity Shape Based on Skeleton Graph Matching


title: Evaluation of prostate segmentation algorithms for MRI : the PROMISE12 challenge

abstract: Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05p<0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.


title: Towards Deep Symbolic Reinforcement Learning

abstract: Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system -- though just a prototype -- learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game.


title: A 61 GHz frequency synthesizer in SiGe BiCMOS for 122 GHz FMCW radar

abstract: In this paper, a fractional-N phase-locked loop (PLL) with an integrated chirp generation circuit block for frequency-modulated continuous-wave (FMCW) radar systems is reported. The circuit is composed of a push-push voltage controlled oscillator (VCO), a feedback divider including pre-scaler and programmable divider stages, a phase-frequency detector followed by a current steering charge pump, a sigma-delta modulator (SDM) with an 18-bit resolution, a chirp generator and a serial-peripheral-interface (SPI) for programming the chirp generator. The circuit occupies an area of 2.6 mm2, consumes 310 mW from a 3.3 V voltage supply. In integer-N mode, the PLL phase noise is better than −95 dBc/Hz at 1 MHz offset from 60 GHz center frequency. The rms frequency error is 134 kHz when the chirp slope is 0.4 GHz / 1.28 ms. The circuit is fabricated in a 0.13 μm SiGe BiCMOS technology. It is well suited for sub-harmonic transceiver frontend of an FMCW radar system at 122GHz and offers full programmability in ramp type, ramp duration and ramp bandwidth.


Wind Power Prediction with Optimization and Clustering Techniques


title: How to Construct a Minimal Theory of Mind

abstract: What could someone represent that would enable her to track, at least within limits, others' perceptions, knowledge states and beliefs including false beliefs? An obvious possibility is that she might represent these very attitudes as such. It is sometimes tacitly or explicitly assumed that this is the only possible answer. However, we argue that several recent discoveries in developmental, cognitive, and comparative psychology indicate the need for other, less obvious possibilities. Our aim is to meet this need by describing the construction of a minimal theory of mind. Minimal theory of mind is rich enough to explain systematic success on tasks held to be acid tests for theory of mind cognition including many false belief tasks. Yet minimal theory of mind does not require representing propositional attitudes, or any other kind of representation, as such. Minimal theory of mind may be what enables those with limited cognitive resources or little conceptual sophistication, such as infants, chimpanzees, scrub-jays and human adults under load, to track others' perceptions, knowledge states and beliefs.


title: A direct method for trajectory optimization of rigid bodies through contact

abstract: Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning of rigid-body systems that contact their environment through inelastic impacts and Coulomb friction. This method eliminates the requirement for a priori mode ordering. Motivated by the formulation of multi-contact dynamics as a Linear Complementarity Problem for forward simulation, the proposed algorithm poses the optimization problem as a Mathematical Program with Complementarity Constraints. We leverage Sequential Quadratic Programming to naturally resolve contact constraint forces while simultaneously optimizing a trajectory that satisfies the complementarity constraints. The method scales well to high-dimensional systems with large numbers of possible modes. We demonstrate the approach on four increasingly complex systems: rotating a pinned object with a finger, simple grasping and manipulation, planar walking with the Spring Flamingo robot, and high-speed bipedal running on the FastRunner platform.


title: Quantitative Genetic Variation in Bicyclus Anynana Metapopulations

abstract: Parent-offspring regression was used to estimate heritabilities (h2) of eight wing pattern characters plus five principal components of the butterfly Bicyclus anynana. The experiment consisted of six metapopulations with four subpopulations each with an effective subpopulation size Ne = 6 or 12, and additionally, one larger (Ne = 24) panmictic control population. Although the differences in treatments resulted in substantially different inbreeding coefficients at the ninth generation, there were no significantly differences in estimates of h2 among populations. The h2 estimates were moderately high (overall mean h2 = 0.43), even for the smallest most inbred metapopulation (mean h2males = 0.51, mean h2females = 0.35). We discuss why no significant reduction in h2 was detected in this analysis, while approximately similar sample sizes in molecular and fitness analyses were sufficient to detect severe inbreeding effects.


title: Innovative design of a claw-wheel transformable robot

abstract: This research develops a claw-wheel transformable robot which could move on flat ground, stairways, or uneven terrain environment efficiently with a fairly simple structure. The robot can switch between claw mode and wheel mode by folding and unfolding the front body and rear body of the robot. The transformation could be made by controlling the motors on a pitch joint through the front claw and rear one in 90° angle shift after folding. The claw mode is mainly used to cross various uneven terrain or climb stairways. Yet, the wheel mode is for moving on flat ground. This robot can be adaptive to various riser and tread of stairs by appropriately controlling the transformation mechanism. Testing and verification have been done both in numerical simulation and the field test on the developed prototype. Compared with the existing hybrid robots, it has been proved to be well adaptive to various environments, structurally simple, easy to control and light in weight.


title: Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network

abstract: Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to "forget" the previous state when the visual sequence is inconsistent. In this paper, we propose a novel visual storytelling approach with Bidirectional Multi-thread Recurrent Neural Network (BMRNN). First, based on the mined local storylines, a skip gated recurrent unit (sGRU) with delay control is proposed to maintain longer range visual information. Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline. Third, a new training scheme with a storyline-constrained objective function is proposed by jointly considering both global and local matches. Experiments on three standard storytelling datasets show that the BMRNN model outperforms the state-of-the-art methods.


title: InTouch Tactile Tales: Haptic Feedback and Long-Distance Storytelling

abstract: The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.


A plant-based diet and coronary artery disease: a mandate for effective therapy


title: A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

abstract: The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.


title: Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions

abstract: A filter for impulsive noise removal is presented here. The problem of impulsive noise elimination is closely connected with the problem of maximal preservation of image edges. To avoid smoothing of the image during filtering, all noisy pixels must be detected. We consider here an approach, which is based on threshold Boolean filtering, where the binary slices of an image, obtained by the threshold decomposition, are processed by the impulse-detecting Boolean functions proposed. These functions provide a possibility of single-pass filtering, because they detect and replace impulses at the same time.


title: Bag of Tasks Rescheduling within Real Grid Environments: Different Approaches

abstract: Providing Quality of Service (QoS) in Grid environments is still a challenging task because advance reservation of resources is not always possible. By contrast, scheduling the use of resources in advance is a way of enhancing the provision of QoS, without physically reserving them. However, in a Grid scenario fragmentation may appear as a logic result of the allocation process, leading to poor resource utilization. To try to avoid that problem a rescheduling technique has been developed and applied periodically depending on the existing fragmentation. Nevertheless, reallocating jobs in an optimal way is an NP-Hard problem. Consequently, simple and scalable ways of distributing and allocating those jobs into the resources are presented and evaluated in a real Grid environment involving heterogeneous computing resources distributed across different national organizations, highlighting the benefits of using them to increase the resource usage.


title: Microstructural maturation of the human brain from childhood to adulthood

abstract: Brain maturation is a complex process that continues well beyond infancy, and adolescence is thought to be a key period of brain rewiring. To assess structural brain maturation from childhood to adulthood, we charted brain development in subjects aged 5 to 30 years using diffusion tensor magnetic resonance imaging, a novel brain imaging technique that is sensitive to axonal packing and myelination and is particularly adept at virtually extracting white matter connections. Age-related changes were seen in major white matter tracts, deep gray matter, and subcortical white matter, in our large (n = 202), age-distributed sample. These diffusion changes followed an exponential pattern of maturation with considerable regional variation. Differences observed in developmental timing suggest a pattern of maturation in which areas with fronto-temporal connections develop more slowly than other regions. These in vivo results expand upon previous postmortem and imaging studies and provide quantitative measures indicative of the progression and magnitude of regional human brain maturation.


title: Repeated labeling using multiple noisy labelers

abstract: This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.


title: Content-Based Image Annotation Refinement

abstract: Automatic image annotation has been an active research topic due to its great importance in image retrieval and management. However, results of the state-of-the-art image annotation methods are often unsatisfactory. Despite continuous efforts in inventing new annotation algorithms, it would be advantageous to develop a dedicated approach that could refine imprecise annotations. In this paper, a novel approach to automatically refining the original annotations of images is proposed. For a query image, an existing image annotation method is first employed to obtain a set of candidate annotations. Then, the candidate annotations are re-ranked and only the top ones are reserved as the final annotations. By formulating the annotation refinement process as a Markov process and defining the candidate annotations as the states of a Markov chain, a content-based image annotation refinement (CIAR) algorithm is proposed to re-rank the candidate annotations. It leverages both corpus information and the content feature of a query image. Experimental results on a typical Corel dataset show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones.


Power Consumption Analysis of Smartphone Applications using UPPAAL


title: The safety of hydroquinone: a dermatologist's response to the 2006 Federal Register.

abstract: Recently, the US Food and Drug Administration proposed a ban on over-the-counter hydroquinone mainly on the basis of high absorption, reports of exogenous ochronosis in humans, and murine hepatic adenomas, renal adenomas, and leukemia with large doses over extended time periods. Systemic exposure to hydroquinone from routine topical application is no greater than that from quantities present in common foods. While murine hepatic adenomas increased, murine hepatocellular carcinomas decreased, suggesting a protective effect. Renal tumors are sex, species, and age specific and therefore do not appear relevant to humans after decades of widespread use. Murine leukemia has not been reproducible and would not be expected from small topical doses. Finally, a literature review of exogenous ochronosis and clinical studies employing hydroquinone (involving over 10,000 exposures under careful clinical supervision) reveal an incidence of exogenous ochronosis in the United States of 22 cases in more than 50 years. Therefore, the proposed ban appears to be unnecessarily extreme.


title: Divide and correct: using clusters to grade short answers at scale

abstract: In comparison to multiple choice or other recognition-oriented forms of assessment, short answer questions have been shown to offer greater value for both students and teachers; for students they can improve retention of knowledge, while for teachers they provide more insight into student understanding. Unfortunately, the same open-ended nature which makes them so valuable also makes them more difficult to grade at scale. To address this, we propose a cluster-based interface that allows teachers to read, grade, and provide feedback on large groups of answers at once. We evaluated this interface against an unclustered baseline in a within-subjects study with 25 teachers, and found that the clustered interface allows teachers to grade substantially faster, to give more feedback to students, and to develop a high-level view of students' understanding and misconceptions.


Outliers Detection on Educational Data using Fuzzy Association Rule Mining


title: Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence

abstract: Introduction The application of knowledge management theory to management in different fields can make an important contribution. One example of such application is in the processes underlying the innovative development of new pharmaceuticals and drugs essential to human health outcomes. Recent advances in medicine, such as the emergence of biologics, or protein-based drugs, including gene-based (gene-therapy) biologics, offer new hope where no other treatments have been previously available (FDA, 2015). Similarly, the outcomes of innovative research, such as the recent discovery of a new antibiotic without detectable bacterial resistance, namely, teixobactin (Ling et al., 2015), offer new hope in the face of the rise of 'superbugs' like methicillin-resistant staphylococcus aureus (MRSA) (Northeastern University, 2015) or others recently found to be resistant to last-line drug colistin (Pierson & Berkrot, 2016). At the heart of this scientific progress, however, is the application of knowledge management theory, which relates research processes and the management of knowledge and information to knowledge creation. Knowledge problems related to constraints to innovative scientific production require knowledge management theory development for scientific progress to occur. Arguably, a knowledge problem exists, which is reflected in constrained pharmaceutical innovation (Grasela & Slusser, 2014; E. C. Hayden, 2014; F. Hayden, 2006; Munos, 2009). With the global innovation pipeline providing new innovations and pharmaceutical drug development having been 'nearly broken' (McKenna, 2014), human populations may increasingly be under threat. Staying with the example of health disasters, emergent threats exist in the form of Ebola (Krishnan, 2014), antibiotic resistance (Halifax, 2013) and disease re-emergence in the form of polio in Nigeria (Ajumobi, 2014) and Pakistan (Zia, 2014), for example. Although recent successful innovation outputs such as biologics (FDA, 2015) and resistance-free antibiotics (Ling et al., 2015) may offer limited evidence against arguments that the pharmaceutical innovation pipeline is 'nearly broken' (McKenna, 2014), this paper argues that this pipeline exists under structural 'bottleneck' conditions which are associated with the knowledge aggregation problem, making it unable to increase innovation dramatically, but that techno-centric knowledge management theory premised on maximizing knowledge sharing, which is synthesized from swarm intelligence literatures, can offer new opportunities for innovations via the more effective harnessing of collective intelligence. Swarm intelligence relates to the collective behavior of decentralized, self-organized systems, either as found in nature or applied to solving problems in science (Kennedy, Eberhart, & Shi, 2001). The contribution of this paper is the identification of a source of theory around collective intelligence (Malone & Klein, 2007; Malone, Laubacher, & Johns, 2011; Surowiecki, 2004; A. W. Woolley, Chabris, Pentland, Hashmi, & Malone, 2010) in terms of how knowledge is managed in nature, for example, by social insects (Farmer, Packard, & Perelson, 1986; Krishnanand & Ghose, 2005; Storn & Price, 1997; Yang, 2010), or of how certain theory derived from the field of swarm intelligence (Bonabeau & Theraulaz, 2000), as well as the theory of markets (Fama, 1970, 1995; A. Smith, 1776/2003), can be applied in the area of knowledge management to improve 'real-time' problem-solving capability. Drawing from theory relating to swarm intelligence and other aspects of collective intelligence, an approach to real-time research problem solving is presented, namely, human swarm solving, offering a framework for understanding the implications of maximizing collaborations in support of urgent and important scientific problem solving. By offering a re-conceptualization of real-time research problem solving, human swarm solving as a method, in turn, offers a useful heuristic exercise in modeling what maximized collaboration in scientific problem solving may look like, offering theoretical, methodological and practical implications that are potentially useful for those across fields seeking to understand the effectiveness and efficiency of research systems. …


title: Influence of teacher empowerment on teachers' organizational commitment, professional commitment and organizational citizenship behavior in schools

abstract: Abstract The present study focuses on the relationship between teacher empowerment and teachers’ organizational commitment, professional commitment (PC) and organizational citizenship behavior (OCB). It examines which subscales of teacher empowerment can best predict these outcomes. The data were collected through a questionnaire returned by a sample of 983 teachers in Israeli middle and high schools. Pearson correlations and multiple regression analyses indicated that teachers’ perceptions of their level of empowerment are significantly related to their feelings of commitment to the organization and to the profession, and to their OCBs. Among the six subscales of empowerment, professional growth, status and self-efficacy were significant predictors of organizational and PC, while decision-making, self-efficacy, and status were significant predictors of OCB. Practical implications of the study are discussed in relation to teachers, principals and policy-makers.


title: Energy monitoring and control using Internet of Things (IoT) system

abstract: The use of technology has become an essential part of improving lifestyle, work efficiency, and a catalyst for economic growth. The benefit of the Internet of Things (IoT) and connected nodes has been on a steep incline in recent years. This paper aims to research, build, test and implement a low-cost energy monitoring and control system using IoT devices. Electrical appliances (e.g., air conditioning units and overhead lighting) can be controlled and monitored using IoT technology from any place in the world. In order to accomplish this goal, a complete front-end to back-end system that includes a smart device application (iOS platform), a cloud-based database, an Application Programming Interface (API), and a hardware development is proposed. A small programmable specialized computing device (e.g., Raspberry Pi) for preliminary testing. This smart node was chosen due to familiarity, and its capabilities, such as general purpose pins and built-in Wi-Fi chip. The end goal is to observe energy efficiency by monitoring and controlling air conditioning appliances and standard overhead lighting units. These smart IoT devices allow for the usage energy data from each unit to be collected and stored in a Cloud-based database that can be analyzed and reported for energy conservation and analysis.


title: Binary and analogue markers of skeletal maturity: clinical utility of the thenar and plantar sesamoids

abstract: Purpose ::: We investigate the thenar and plantar sesamoids as markers of skeletal maturity, and grade appearance using two scales, a binary system (absent or present), and an analogue system that relies upon judging regular changes in morphological appearance. ::: ::: ::: Methods ::: We studied 94 healthy children (49 female and 45 male patients) between ages three and 18 years who had approximately 700 serially acquired sets of radiographs and physical examinations. The children had at least annual radiographs taken of the left hand and left foot. Velocity of growth was calculated and curves were fit to a cubic spline model to determine age of maximum height velocity, or peak height velocity (PHV). Appearance of the plantar and thenar sesamoids was recorded using a binary system classifying the sesamoids as absent or present and an analogue system classifying the sesamoid as absent, present as a small ossification centre or larger than a small ossification centre. ::: ::: ::: Results ::: The plantar sesamoids appear 1.67 years before PHV and reach mature size 1.02 years after PHV. The thenar sesamoids appear 0.32 years before PHV and reach mature size 2.25 years after PHV. The plantar sesamoids are present and thenar sesamoids are absent at a mean 1.5 years prior to PHV. No patients had the thenar sesamoids present while the plantar sesamoids were absent. ::: ::: ::: Conclusion ::: As binary markers, when the plantar and thenar sesamoids are considered together it is possible to localize maturity. As analogue markers, they offer more information. The sesamoids also allow clarification of the calcaneal and Sanders stages. ::: ::: ::: Level of Evidence ::: Not Applicable.


title: Using mobile phones to write in air

abstract: Numerous sensors in modern mobile phones enable a range of people-centric applications. This paper envisions a system called PhonePoint Pen that uses the in-built accelerometer in mobile phones to recognize human writing. By holding the phone like a pen, a user should be able to write short messages or draw simple diagrams in the air. The acceleration due to hand gestures can be translated into geometric strokes, and recognized as characters. We prototype the PhonePoint Pen on the Nokia N95 platform, and evaluate it through real users. Results show that English characters can be identified with an average accuracy of 91.9%, if the users conform to a few reasonable constraints. Future work is focused on refining the prototype, with the goal of offering a new user-experience that complements keyboards and touch-screens.


title: Exploring personality and game preferences in the younger and older population: a pilot study

abstract: Aim: ::: Engagement in gamified applications can be increased by effectively meeting end-user preferences for game content. To design this tailored content insight in user preferences is necessary, obtained from user classification models. This pilot study aims to explore the hypothesised relation between personality traits and preference for game characteristics that is the basis for a new user classification model, deduced from the Five Domains of Play theory. ::: Methods: An online questionnaire consisted of the 10-item Big Five Inventory to determine personality, and five questions on the preference for game examples to determine game preference. ::: ::: Results: ::: 216 participants completed the questionnaire (M=39 years, SD=17). For the group of participants younger than 60, four out of five personality traits correlate significantly but weakly with their corresponding game preference domains (r=0,13-0,30, p<0,05). For the participants older than 60, no significant correlations were found. ::: ::: Conclusion: ::: Personality and game preferences are weakly related in persons younger than sixty years old, while no relation was found for the older participants. For the latter, this may be due to a lack of gaming experience. We therefore propose to extend research towards a field study by providing actual games to play on beforehand.


title: The life and times of a zookeeper

abstract: A distributed application may comprise a range of processors, from two to thousands. For the processors of an application comprising many different machines to work together, they must often coordinate. Coordination may be as simple as agreeing upon a basic configuration, e.g., a list of server addresses and ports for example. In the context of distributed systems, even agreeing upon a basic configuration can be complicated: servers may be down during configuration changes, servers may need to adapt quickly to dynamic configuration changes, and during changes servers may have conflicting configurations. Most distributed applications also require more sophisticated coordination primitives such as leader election, group membership, and rendezvous. At Yahoo!, we noticed that many distributed applications were re-implementing coordination primitives in their distributed applications. In theory, this is completely reasonable: coordination protocols are well known, and usually the coordination logic is intermingled with the application logic. In practice, the story is much different; these protocols have sometimes subtle requirements that can easily be overlooked when implementing them. Further, an application developer is usually much more interested in working on application logic than the coordination protocol that the logic depends on. We found many cases of applications whose coordination primitives were buggy, a single point of failure, poorly performing, or oversimplified; in some cases the applications suffered from all of the above. We started ZooKeeper as a system that could address all these problems in a general way so that all of our applications could use it for coordination and application developers could focus on developing their applications. By providing a general system that all applications could use, we could devote the time to making it robust, fault-tolerant, and with good enough performance to be used extensively by applications. We also needed to balance two possibly conflicting goals: ZooKeeper needed to be general enough to address our coordination needs and simple enough to implement a correct high performance service. We found that we were able to achieve our goals by trading strong synchronization for strong ordering guarantees and a wait-free interface. Inside and outside of Yahoo! developers of distributed applications have enthusiastically embraced ZooKeeper. Developers who have previously dealt with the difficulties of implementing distributed applications quickly see the benefits of ZooKeeper, and we have many applications inside of Yahoo! that make use of it. It was developed quickly by a small group of developers in a small amount of time. The general techniques we use in ZooKeeper are not fundamentally new. For instance, ZooKeeper uses a leader-based atomic broadcast protocol that may seem at a first glance not novel. However, a deeper inspection reveals that it has some key propertiesthat are crucial to guarantee the properties that developers of large-scale applications require. To the best of our knowledge, no previous algorithm presented all necessary properties "out-of-the-box". In this talk, we share previous work that influenced ZooKeeper. In particular, we compare at the protocol level with Paxos and Viewstamped Replication, and at the service level with services like Chubby and Boxwood. We give a high level overview of the service itself and describe its implementation. Along the way we point out some of the design and implementation details that were key in achieving the correctness and performance that we need. Finally, we show how ZooKeeper performance has evolved over time. We have increased the performance of ZooKeeper an order of magnitude from our first implementation, and none of that increase has come from protocol changes. Indeed, our protocol has remained constant from the very beginning.


title: Low-complexity near-optimal signal detection for uplink large-scale MIMO systems

abstract: The minimum mean square error (MMSE) signal detection algorithm is near-optimal for uplink multi-user large-scale multiple-input-multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from O(K 3 ) to O(K 2 ), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.


title: A Review of GaN on SiC High Electron-Mobility Power Transistors and MMICs

abstract: Gallium-nitride power transistor (GaN HEMT) and integrated circuit technologies have matured dramatically over the last few years, and many hundreds of thousands of devices have been manufactured and fielded in applications ranging from pulsed radars and counter-IED jammers to CATV modules and fourth-generation infrastructure base-stations. GaN HEMT devices, exhibiting high power densities coupled with high breakdown voltages, have opened up the possibilities for highly efficient power amplifiers (PAs) exploiting the principles of waveform engineered designs. This paper summarizes the unique advantages of GaN HEMTs compared to other power transistor technologies, with examples of where such features have been exploited. Since RF power densities of GaN HEMTs are many times higher than other technologies, much attention has also been given to thermal management-examples of both commercial “off-the-shelf” packaging as well as custom heat-sinks are described. The very desirable feature of having accurate large-signal models for both discrete transistors and monolithic microwave integrated circuit foundry are emphasized with a number of circuit design examples. GaN HEMT technology has been a major enabler for both very broadband high-PAs and very high-efficiency designs. This paper describes examples of broadband amplifiers, as well as several of the main areas of high-efficiency amplifier design-notably Class-D, Class-E, Class-F, and Class-J approaches, Doherty PAs, envelope-tracking techniques, and Chireix outphasing.


title: Document Image Binarization Techniques, Developments and Related Issues: A Review

abstract: Image binarization is still a relevant research area due to its wide range of applications in the field of document analysis and recognition. Accuracy of binarization methods affected by many factors such as shadows non-uniform illumination, low contrast, large signal-dependent noise etc. This paper provides a comprehensive survey of major binarization techniques. We also emphasis on the problems being encountered and the related issues in the research area of document image binarization. In addition, some important issues affecting the performance of image binarization methods are also discussed. This literature review suggests that designing a suitable document image binarization method is a prerequisite for a successful document image analysis and recognition.


title: Counterfactual reasoning and learning systems: the example of computational advertising

abstract: This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.


Automatic Detection of Region-Mura Defect in TFT-LCD


title: Statistical matching in the presence of anonymization and obfuscation: Non-asymptotic results in the discrete case

abstract: Many popular applications use traces of user data to tune services to their users but come with a significant risk to user privacy. In particular, even if user traces are anonymized, statistical matching of these traces to prior user behavior can be used to identify the user and their behavior. Because of this threat, there has been significant recent work exploring the theoretical foundations of this problem in the limit of a large number of users and/or observations, where the asymptotic nature of the approaches allows for clean analytical results. In this paper, we turn attention to exact performance analysis for a finite number of users and observations. We consider the case where a user is distributed over a discrete set of states according to a probability distribution drawn at random, which we assume is known to the adversary based on his/her analysis of past user behavior. The finite-length traces are then anonymized and obfuscated at a cost in user utility. We analyze the ability of the adversary to correctly identify user data samples as a function of the rate of anonymization and degree of obfuscation, and we arrive at complicated yet readily numerically evaluated expressions. These results allow us to investigate interesting questions left open by the asymptotic nature of previous work.


title: Implementation and Optimization of the Accelerator Based on FPGA Hardware for LSTM Network

abstract: Today, artificial neural networks (ANNs) are important machine learning methods which are widely used in a variety of applications. As the emerging field of ANNs, recurrent neural networks (RNNs) are often used for sequencerelated applications. And Long Short-Term Memory (LSTM) is an improved RNN which contains complex computational logic. To achieve high accuracy, researchers always build largescale LSTM networks which are time-consuming and powerconsuming. Thus the acceleration of LSTM networks, low power & energy consumption become the hot issues in today's research. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. To optimize our implementation, we also use multiple methods including tiled matrix-vector multiplication, binary adder tree, and overlap of computation and data access. Through the acceleration and optimization methods, our accelerator is power-efficient and has a better performance than ARM Cortex A9 processor and Intel Core i5 processor.


title: UNIQ: Uniform Noise Injection for the Quantization of Neural Networks.

abstract: We present a novel method for training deep neural network amenable to inference in low-precision arithmetic with quantized weights and activations. The training is performed in full precision with random noise injection emulating quantization noise. In order to circumvent the need to simulate realistic quantization noise distributions, the weight and the activation distributions are uniformized by a non-linear transformation, and uniform noise is injected. An inverse transformation is then applied. This procedure emulates a non-uniform k-quantile quantizer at inference time, which is shown to achieve state-of-the-art results for training low-precision networks on CIFAR-10 and ImageNet-1K datasets. In particular, we observe no degradation in accuracy for MobileNet and ResNet-18 on ImageNet with as low as 2-bit quantization of the activations and minimal degradation for as little as 4 bits for the weights.


Context-aware User Interaction for Mobile Recommender Systems


title: Gait-based human age estimation using age group-dependent manifold learning and regression

abstract: Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research or automatic age-based access control restriction for a specific area because the gait can be observable at a distance from a camera (e.g., CCTV). Although the aging process of gait significantly differs among age groups (e.g., children, adults, and the elderly), previous studies on gait-based human age estimation employ a single age group-independent estimation model that suffers from large estimation errors when the age variation increases. We therefore propose an age group-dependent gait-based human age estimation method for better accuracy. Specifically, in the training phase, we first compose age groups that are well-separated from each other by clustering gait features along with their age labels. We then learn a classifier that classifies the gait features for multiple age groups using a directed acyclic graph support vector machine. Next, we learn an age regression model for each age group using support vector regression with a Gaussian kernel in conjunction with a manifold learning technique, i.e., orthogonal locality preserving projection, to better characterize the gait feature. In the test phase, given a gait feature, it is first classified into an age group and then its age is estimated with the age regression model of the classified age group. Experimental results on a gait database that has the world’s largest population of participants ranging from 2 to 90 years old demonstrate the state-of-the-art performance of the proposed method.


Composition of Augmented Reality and GIS To Visualize Environmental Changes


A Survey on VANETs Applications and Its Challenges


title: Additively reciprocal fuzzy pairwise comparison matrices and multiplicative fuzzy priorities

abstract: The transformations between multiplicatively and additively reciprocal fuzzy pairwise comparison matrices are dealt with, and formulas for obtaining multiplicative fuzzy priorities from additively reciprocal fuzzy pairwise comparison matrices are proposed in this paper. The formulas are based on the concept of constrained fuzzy arithmetic and preserve the additive reciprocity of pairwise comparisons. Further, the consistency issue is approached in the paper; the consistency check is employed directly in the formulas for obtaining multiplicative fuzzy priorities of objects both from multiplicatively and additively reciprocal fuzzy pairwise comparison matrices. Two definitions of consistency based on transitivity are employed in this paper—the traditional additive/multiplicative transitivity and the weak consistency. However, also several other transitivity conditions are discussed in this context. Finally, the proposed formulas for obtaining multiplicative fuzzy priorities from an additively reciprocal fuzzy pairwise comparison matrix and the formulas with employed consistency check are applied in an illustrative example. Triangular fuzzy numbers are used for the fuzzy extension in this paper. However, all the formulas can be modified easily to be applied on intervals, trapezoidal fuzzy numbers or any other fuzzy numbers given by \(\alpha \)-cuts.


title: Linear Equalization and PVT-Independent DC Wander Compensation for AC-Coupled PCIe 3.0 Receiver Front End

abstract: An alternating current (ac)-coupled high-speed receiver analog front end is capable of shifting the input direct current (dc) level without extra current consumption and signal degradation at high frequencies. However, the dc wander due to the high-pass characteristic of the ac-coupled front end may degrade the receiver performance because it equivalently modulates the sampling threshold voltage of the sampler. While this modulation may be negligible for highly dc-balanced input, it shows observable degradation in Peripheral Component Interconnect Express (PCIe) 3.0 receivers operating at 8 Gb/s. In addition, analog equalization is generally required for a PCIe 3.0 receiver front end. This paper presents techniques to add linear equalization and dc wander compensation into an ac-coupled receiver front end. The proposed linear equalization scheme attenuates the low-frequency energy and keeps the high-frequency energy. The proposed dc wander-compensation method is independent of process, voltage, and temperature; therefore, there is no need for calibration or compensation. The proposed schemes have very low power consumption and very good linearity because only passive and linear components are utilized in the main signal path. The proposed schemes were confirmed by simulation.


title: Learning domain taxonomies: the TaxoLine approach

abstract: Purpose ::: ::: ::: ::: ::: The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.


title: LED based street lighting with automatic intensity control using solar PV

abstract: Due to increasing concerns about global warming and climate change, renewable energy sources like wind and solar are receiving greater attention from researchers world-wide. Solar PV is an excellent source of energy available all over the globe but in DC form. If it can be utilized without converting this into AC, it can be really energy efficient. LEDs for lighting purposes have become extremely popular lately due to their higher lumens yield with less wattage consumption; also, they work on DC. So, Solar PV-LED is an ideal combination for outdoor lighting. This paper explores this combination for street lighting purposes. Depending on the yield of the solar PV and the traffic scenario, the intensity of the street light is being adjusted from 0% to 100% automatically by making use of a DC-DC converter. The street light is being equipped with a Li-ion battery whose charging and discharging are also controlled by DC-DC converters. This paper explores the combination of a solar PV with MPPT supplying power to a street light equipped with LEDs with automatic intensity control through a DC-DC converter.


title: To Intercept or Not to Intercept: Analyzing TLS Interception in Network Appliances

abstract: Many enterprise-grade network appliances host a TLS proxy to facilitate interception of TLS-protected traffic for various purposes, including malware scanning, phishing detection, and preventing data exfiltration. When deployed, the TLS proxy acts as the security validating client for external TLS web servers, on behalf of the original requesting client; on the other hand, the proxy acts as the web server to the client. Consequently, TLS proxies must maintain a reliable level of security, at least, at the same level as modern web browsers and properly configured TLS servers. Failure to do so increases the attack surface of all the proxied clients served the network appliance. We develop a framework for testing TLS inspecting appliances, combining and extending tests from existing work on client-end and network-based interception. Utilizing this framework, we analyze six representative network appliances, and uncover several security issues regarding TLS version and certificate parameters mapping, CA trusted stores, private keys, and certificate validation tests. For instance, we found that two appliances perform no certificate validation at all, exposing their end-clients to trivial Man-in-the-Middle attacks. The remaining appliances that perform certificate validation, still do not follow current best practices, and thus making them vulnerable against certain attacks. We also found that all the tested appliances deceive the requesting clients, by offering TLS parameters that are different from the proxy-to-server TLS parameters, such as the TLS versions, hashing algorithms, and RSA key sizes. We hope that this work bring focus on the risks and vulnerabilities of using TLS proxies that are being widely deployed in many enterprise and government environments, potentially affecting all their users and systems.


title: Data Cleaning Methods

abstract: Cleaning methods are used for finding duplicates within a file or across sets of files. This overview provides background on the Fellegi-Sunter model of record linkage. The Fellegi-Sunter model provides an optimal theoretical classification rule. Fellegi and Sunter introduced methods for automatically estimating optimal parameters without training data that we extend to many real world situations.


title: Cyberbullying and academic performance

abstract: This research investigates the emotional and physiological effects of cyber bullying on the university students. The primary objective of this investigation is to identify the victims of cyber bullying and critically analyze their emotional state and frame of mind in order to provide them with a workable and feasible intervention in fighting cyber bullying. In this research a triangulation method (quantitative, qualitative and descriptive) is employed in the investigation. The instruments used in this study, in which 365 students participated, included questionnaires, interviews, checklists and observations. The results of the study indicated that a significant number of the respondents 35 (13%) had suffered emotionally due to cyber bullying. Furthermore, 300 (85%) of the respondents indicated that in their views cyber bullying causes emotional and psychological stress. Further, majority of the respondents 255 (70%) agreed that cyber bullying adversely affects students’ academic performance. Results further designate that 60 (16.6%) of the respondents specified that they had bullied someone inside the university at least 2 or 3 times a month while 4 (1.1%) of the participants said that they had bullied someone outside the university at least 2 or 3 times a month. It is interesting to note that majority of the students 75 (20.8%) signify that they have heard bullying taking place inside the university. Index Terms: Computer, internet, bullying, student, emotional psychology


title: IMPLEMENTATION OF AES AND BLOWFISH ALGORITHM

abstract: Small embedded devices (including smart cards, RFID, sensor nodes) are now deployed in many applications. They are usually characterized by strong cost constraints. Yet, as they may manipulate sensitive data, they also require cryptographic protection .As a result, many algorithms have been proposed in order to allow strong security at lower cost than standard solution and lack of comparative studies prevent a good understanding for these cryptographic algorithms. Thus in this thesis we study the cryptographic algorithms like AES and Blowfish and compare different parameters and then do further implementation as the implementation of encryption/decryption algorithm is the most essential part of the secure communication. The algorithms are further considered for VLSI implementation. The evaluation is performed in terms of encryption speed, the CPU utilization with time and the battery power consumption. The experimental results specify the efficiency of the algorithms.


title: Navigating car-like robots in unstructured environments using an obstacle sensitive cost function

abstract: We propose a method for navigating a car-like vehicle within an unstructured environment. Path planning is posed as a graph search problem. The search graph is set up in a way that implies derivation of a feed forward term for a downstream closed loop controller. An informed search algorithm is used that is guided by a heuristic cost function that accounts for both kinematic constraints of the vehicle and the topology of the vehiclepsilas free space. Configuration space obstacles are computed from an obstacle map acquired from a high definition laser range scanner and search is restricted to the collision free subset of the configuration space. The algorithm allows for solving all of the following problems: Precise parking maneuvers, narrow turns, long distance navigation. The system has been used successfully on board the autonomous car ANNIEWAY in the DARPA urban challenge competition of 2007.


title: Female Genital Cutting

abstract: Abstract Objective To strengthen the national framework for care of adolescents and women affected by female genital cutting (FGC) in Canada by providing health care professionals with: (1) information intended to strengthen their knowledge and understanding of the practice; (2) directions with regard to the legal issues related to the practice; (3) clinical guidelines for the management of obstetric and gynaecological care, including FGC related complications; and (4) guidance on the provision of culturally competent care to adolescents and women with FGC. Evidence Published literature was retrieved through searches of PubMed, CINAHL, and The Cochrane Library in September 2010 using appropriate controlled vocabulary (e.g., Circumcision, Female) and keywords (e.g., female genital mutilation, clitoridectomy, infibulation). We also searched Social Science Abstracts, Sociological Abstracts, Gender Studies Database, and ProQuest Dissertations and Theses in 2010 and 2011. There were no date or language restrictions. Searches were updated on a regular basis and incorporated in the guideline to December 2011. Grey (unpublished) literature was identified through searching the websites of health technology assessment and health technology-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies. Values The quality of evidence in this document was rated using the criteria described in the Report of the Canadian Task Force on Preventive Health Care (Table 1).


title: Shannon's Energy Based Algorithm in ECG Signal Processing

abstract: Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. The analysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important. In this article, a method on Shannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided. At first, this algorithm computes the Shannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold. Then, the signal peaks are determined. The efficiency of the algorithm is tested on 70 cases. Of all 12 standard leads, ECG signals include 840 leads of the PTB Diagnostic ECG Database (PTBDB). The algorithm shows that the Shannon energy (SE) sensitivity is equal to 99.924%, the detection error rate (DER) is equal to 0.155%, Positive Predictivity (


title: Bluetooth Worms: Models, Dynamics, and Defense Implications

abstract: Recent occurrences of mobile worms like Cabir, Mabir and CommWarrior have created growing concerns over the security of data stored on mobile devices such as cell phones and PDAs. These worms have in common that they all use Bluetooth communication as their infection channel. In order to prepare effective defense strategies against such worms, we study the nature, characteristics, and spreading dynamics of Bluetooth worms in the safe environment of simulation. Our key findings are: (i) Mobility may not boost the Bluetooth worm propagation; instead, link instability owing to it has negative impact on the worm spreading speed; (ii) The inherent capacity constraints imposed by the wireless channel (e.g. interference) and the specifics of the Bluetooth protocol can significantly slow down the Bluetooth worm propagation; (iii) Intelligently designed worms can improve their propagation speed to a noticeable degree by strategically selecting worm model parameters or exploiting out-of-band propagation capabilities.


title: Foundations of Attack Trees

abstract: Attack trees have found their way to practice because they have proved to be an intuitive aid in threat analysis. Despite, or perhaps thanks to, their apparent simplicity, they have not yet been provided with an unambiguous semantics. We argue that such a formal interpretation is indispensable to precisely understand how attack trees can be manipulated during construction and analysis. We provide a denotational semantics, based on a mapping to attack suites, which abstracts from the internal structure of an attack tree, we study transformations between attack trees, and we study the attribution and projection of an attack tree.


title: Holistic features for real-time crowd behaviour anomaly detection

abstract: This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness [1] and crowd conflict [2], with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abnormal training data is available we use a Support Vector Machine (SVM) for binary classification. We evaluate on two crowd behaviour anomaly detection datasets, achieving both state-of-the-art classification performance on the violent-flows dataset [3] as well as better than real-time processing performance (40 frames per second).


title: Positioning for the Internet of Things: A 3GPP Perspective

abstract: Many use cases in the Internet of Things (IoT) will require or benefit from location information, making positioning a vital dimension of the IoT. The 3GPP has dedicated a significant effort during its Release 14 to enhance positioning support for its IoT technologies to further improve the 3GPPbased IoT eco-system. In this article, we identify the design challenges of positioning support in LTE-M and NB-IoT, and overview the 3GPP's work in enhancing the positioning support for LTE-M and NB-IoT. We focus on OTDOA, which is a downlink based positioning method. We provide an overview of the OTDOA architecture and protocols, summarize the designs of OTDOA positioning reference signals, and present simulation results to illustrate the positioning performance.


title: AlphaGAN: Generative adversarial networks for natural image matting

abstract: We present the first generative adversarial network (GAN) for natural image matting. Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify well-composited images. Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information. We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others. Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production.


title: Cyber security strategy for future distributed energy delivery system

abstract: Energy delivery systems in future manner will be referred to a modernization of delivery system so it monitors, protects and automatically optimize the operation of its interconnected elements. Its contain power generations, transmission network and user automation. It characterized by two way flow of electricity and information to create an automated distributed energy delivery system. From the viewing side of information flow, there are management and protection of the system that must be strategized to ensure the effective operation of the energy delivery system. This cyber security strategy will examine both domain-specific and common requirement to ensure interoperability of solution across different part of infrastructure. This strategy will require risk management framework developed by both private and public sector. This framework will establish the processes for combining impact, vulnerability, and threat information to produce an assessment of risk to the distributed energy delivery system. The approach of this risk management will consider an organizational perspective, cyber security requirement, standard security system, and electricity security guideline and protection plan. This risk management will be applied on an asset, system, and network basis, as applicable. Some security architecture such as key management, virtualization security within cloud computing, and smart privacy will be linked to the energy delivery system. The final strategy of this effort will be a set of recommended cyber security requirements that will be allocated to interfaces of the Smart Grid. The goal is to ensure that a comprehensive assessment of the systems and component of the distributed energy delivery system is completed.


title: Short circuit detection using the gate charge characteristic for Trench/Fieldstop-IGBTs

abstract: In this paper, the detection of insulated-gate bipolar transistors (IGBT) short-circuit failures based on the gate charge characteristic is investigated. The main focus are impacts of the parasitic inductances and the operating point on the gate charge characteristic, as well as limitations for this protection concept by this effects. An analogous detection circuit is developed and relevant implementation aspects are given. The concept is validated with a state-of-the-art Trench/Fieldstop-IGBT for electric vehicle traction application.


title: Cannabinoids and the immune system: an overview.

abstract: Cannabinoids can influence the immune network. Data on the impact of exogenous cannabinoid ligands on immune function serve not only to understand how the endocannabinoid system modulates immune phenomena associated with infection or inflammation, but also to identify therapeutic targets for immune diseases. Cannabinoids can modulate immune reactions in the periphery but also in the brain, influence T cell subset balance and cytokine expression and play a role in the balance between neuroinflammation and neurodegeneration. Immune cells can synthesize endocannabinoids and also be influenced by cannabinoid analogues. Cannabinoid receptors show different expression on immune cells depending on activation status and stimuli. The complexity of relation between cannabinoid ligands of various classes and cannabinoid receptors brought the need to refine the simple conceptual frame of agonist-antagonists and offered potential implications for understanding interactions in pathological conditions. The immune influence of cannabinoid ligands is not fully elucidated. However, aspects of their immunomodulatory effects provide the basis for a context-dependent targeted therapeutic approach, thus leading to the possibility for the use of cannabinoids in the treatment of inflammatory disease.


title: Intelligent Control for Brake Systems

abstract: There exist several problems in the control of brake systems including the development of control logic for antilock braking systems (ABS) and "base-braking." Here, we study the base-braking control problem where we seek to develop a controller that can ensure that the braking torque commanded by the driver will be achieved. In particular, we develop a fuzzy model reference learning controller, a genetic model reference adaptive controller, and a general genetic adaptive controller, and investigate their ability to reduce the effects of variations in the process due to temperature. The results are compared to those found in previous research.


title: Can You Call the Software in Your Device be Firmware?

abstract: In addition to storing a plethora of code and functionality, devices also possess a certain set of data and commands, known as firmware. The ability of devices to perform specific tasks relies on the firmware. In order to understand the scope of firmware we conducted a qualitative analysis of its properties, functionalities and security. We examined the use of the terminology over a period of years and found that "firmware" is used in numerous situations in a range of contexts. In this paper, we propose a taxonomy of firmware to classify the field and understand it better.


A Wakeup Call for Internet Monitoring Systems:


title: Ontological user profile modeling for context-aware application personalization

abstract: Existing context-aware adaptation techniques are limited in their support for user personalization. There is relatively less developed research involving adaptive user modeling for user applications in the emerging areas of mobile and pervasive computing. This paper describes the creation of a User Profile Ontology for context-aware application personalization within mobile environments. We analyze users' behavior and characterize users' needs for context-aware applications. Special emphasis is placed in the ontological modeling of dynamic components for use in adaptable applications. We illustrate the use of the model in the context of a case study, focusing on providing personalized services to older people via smart-device technologies.


title: Online book recommendation system

abstract: Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Generally they are used in Internet shops to increase the profit. This paper proposes a quick and intuitive book recommendation system that helps readers to find appropriate book to read next. The overall architecture is presented with it's detailed description. We used a collaborative filtering method based on Pearson correlation coefficient. Finally the experimental results based on the online survey are provided with some discussions.


title: Fast, lean, and accurate: modeling password guessability using neural networks

abstract: Human-chosen text passwords, today's dominant form of authentication, are vulnerable to guessing attacks. Unfortunately, existing approaches for evaluating password strength by modeling adversarial password guessing are either inaccurate or orders of magnitude too large and too slow for real-time, client-side password checking. We propose using artificial neural networks to model text passwords' resistance to guessing attacks and explore how different architectures and training methods impact neural networks' guessing effectiveness. We show that neural networks can often guess passwords more effectively than state-of-the-art approaches, such as probabilistic context-free grammars and Markov models. We also show that our neural networks can be highly compressed-to as little as hundreds of kilobytes-without substantially worsening guessing effectiveness. Building on these results, we implement in JavaScript the first principled client-side model of password guessing, which analyzes a password's resistance to a guessing attack of arbitrary duration with sub-second latency. Together, our contributions enable more accurate and practical password checking than was previously possible.


title: Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems

abstract: We present algorithms for solving symmetric, diagonally-dominant linear systems to accuracy e in time linear in their number of non-zeros and log (κ f (A) e), where κ f (A) is the condition number of the matrix defining the linear system. Our algorithm applies the preconditioned Chebyshev iteration with preconditioners designed using nearly-linear time algorithms for graph sparsification and graph partitioning.


title: Hand pose estimation with multi-scale network

abstract: Hand pose estimation plays an important role in human-computer interaction. Because it is a problem of high-dimensional nonlinear regression, the accuracy achieved by the existing methods of hand pose estimation are still unsatisfactory. With the development of deep neural networks, more and more people have begun to adopt the method involving deep neural network.We proposed a multi-scale convolutional neural network for the single depth image of the hand. The network, which is end-to-end, directly calculates the three-dimensional coordinates of the joints of the hand,and the multi-scale structure enhances the convergence speed and the output accuracy of the network. In addition, an output function for the output layer, called Stair Rectified Linear Units, is used to limit the output value. As a result of experiments, the optimization method with momentum is found not suitable for hand pose estimation because it is a task of unstable regression. Finally our proposed method has state-of-the-art performance on the NYU Hand Pose Dataset.