title: A six degrees of freedom haptic interface for laparoscopic training

abstract: We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.


title: Giving up Twitter for Lent: how and why we take breaks from social media

abstract: Social media use is widespread, but many people worry about overuse. This paper explores how and why people take breaks from social media. Using a mixed methods approach, we pair data from users who tweeted about giving up Twitter for Lent with an interview study of social media users. We find that 64% of users who proclaim that they are giving up Twitter for Lent successfully do so. Among those who fail, 31% acknowledge their failure; the other 69% simply return. We observe hedging patterns (e.g. "I thought about giving up Twitter for Lent but"?) that surfaced uncertainty about social media behavior. Interview participants were concerned about the tradeoffs of spending time on social media versus doing other things and of spending time on social media rather than in "real life." We discuss gaps in related theory that might help reduce users' anxieties and open design problems related to designing systems and services that can help users manage their own social media use.


title: RELATIONSHIPS BETWEEN ROAD TRANSPORT WORKING CONDITIONS, FATIGUE, HEALTH AND TRAFFIC SAFETY

abstract: The report contains a literature survey evaluating research on the relationship between working and driving hours and rest periods of professional road transport drivers, and fatigue, health and traffic safety. Indications for limitations on driving and working hours in order to protect drivers against health and traffic safety hazards are specifically looked for and evaluated. Only a few investigators have taken professional bus drivers as the subject of their study, the majority of the research concentrates on truck drivers. Hence, the results in this literature survey are limited to these drivers but in a general sense the conclusions are also applicable to bus drivers who work under the same conditions as truck drivers. Truck drivers in North America and Europe work very long days, between 12 and 14 hours on average, and approximately 60% of this time is spent driving the truck. In addition, their daily rest between consecutive working periods is relatively short. There is no clear evidence for a direct relationship between driving hours and accident occurrence. However, in one case a relationship between long working hours and accidents is reported. Accident risk in general appears to be 2.5 times higher for a driver who works over 13 hours per day than for a driver who works 10 hours or less. There is evidence that inadequate daily rest leads to drivers falling asleep at the wheel. However, no limits for the length of rest periods can be set without further research. Future research should concentrate on: (a) the influence of the number of working hours per week and the length of rest periods in between working periods on accident occurrence and health problems of professional road transport drivers; and (b) the circumstances surrounding the onset of fatigue. (TRRL)


title: Approximating Optimal Policies for Partially Observable Stochastic Domains

abstract: The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligence If the state of the world is known at all times, the world can be modeled as a Markov Decision Process (MDP) MDPs have been studied extensively and many methods are known for determining optimal courses of action or policies. The more realistic case where state information is only partially observable Partially Observable Markov Decision Processes (POMDPs) have received much less attention. The best exact algorithms for these problems can be very inefficient in both space and time. We introduce Smooth Partially Observable Value Approximation (SPOVA), a new approximation method that can quickly yield good approximations which can improve over time. This method can be combined with reinforcement learning meth ods a combination that was very effective in our test cases.


title: Moving toward more perfect unions: Daily and long-term consequences of approach and avoidance goals in romantic relationships

abstract: In 2 daily experience studies and a laboratory study, the authors test predictions from approach-avoidance motivational theory to understand how dating couples can maintain feelings of relationship satisfaction in their daily lives and over the course of time. Approach goals were associated with increased relationship satisfaction on a daily basis and over time, particularly when both partners were high in approach goals. Avoidance goals were associated with decreases in relationship satisfaction over time, and people were particularly dissatisfied when they were involved with a partner with high avoidance goals. People high in approach goals and their partners were rated as relatively more satisfied and responsive to a partner's needs by outside observers in the lab, whereas people with high avoidance goals and their partners were rated as less satisfied and responsive. Positive emotions mediated the link between approach goals and daily satisfaction in both studies, and responsiveness to the partner's needs was an additional behavioral mechanism in Study 2. Implications of these findings for approach-avoidance motivational theory and for the maintenance of satisfying relationships over time are discussed.


title: Impacts of cooling technology on solder fatigue for power modules in electric traction drive vehicles

abstract: Describes three power module cooling topologies for electric traction drive vehicles: two advanced options using jet impingement cooling and one option using pin-fin liquid cooling.


title: Designing mobility models based on social network theory

abstract: Validation of mobile ad hoc network protocols relies almost exclusively on simulation. The value of the validation is, therefore, highly dependent on how realistic the movement models used in the simulations are. Since there is a very limited number of available real traces in the public domain, synthetic models for movement pattern generation must be used. However, most widely used models are currently very simplistic, their focus being ease of implementation rather than soundness of foundation. Simulation results of protocols are often based on randomly generated movement patterns and, therefore, may differ considerably from those that can be obtained by deploying the system in real scenarios. Movement is strongly affected by the needs of humans to socialise or cooperate, in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled and that have been studied in social sciences for years. In this paper we propose a new mobility model founded on social network theory. The model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This clustering is then mapped to a topographical space, with movements influenced by the strength of social ties that may also change in time. We have validated our model with real traces by showing that the synthetic mobility traces are a very good approximation of human movement patterns. The impact of the adoption of the proposed algorithm on the performance of AODV and DSR is also presented and discussed.


Overview of Autonomous Vehicle Sensors and Systems


title: Imaging superficial tissues with polarized light.

abstract: OBJECTIVE ::: Polarized light can be used to obtain images of superficial tissue layers such as skin, and some example images are presented. This study presents a study of the transition of linearly polarized light into randomly polarized light during light propagation through tissues. ::: ::: ::: STUDY DESIGN/MATERIALS AND METHODS ::: The transition of polarization was studied in polystyrene microsphere solutions, in chicken muscle (breast) and liver, and in porcine muscle and skin. The transition is discussed in terms of a diffusion process characterized by an angular diffusivity (radians(2)/mean free path) for the change in angular orientation of linearly polarized light per unit optical path traveled by the light. ::: ::: ::: RESULTS ::: Microsphere diffusivity increased from 0.031 to 0.800 for diameters decreasing from 6.04 microm to 0.306 microm, respectively. Tissue diffusivity varied from a very low value (0.0004) for chicken liver to an intermediate value (0.055) for chicken and porcine muscle to a very high value (0.78) for pig skin. ::: ::: ::: CONCLUSION ::: The results are consistent with the hypothesis that birefringent tissues randomize linearly polarized light more rapidly than nonbirefringent tissues. The results suggest that polarized light imaging of skin yields images based only on photons backscattered from the superficial epidermal and initial papillary dermis because the birefringent dermal collagen rapidly randomizes polarized light. This anatomic region of the skin is where cancer commonly arises.


title: Analysis and Prediction of Crimes by Clustering and Classification

abstract: Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.


title: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods

abstract: Abstract The exact delineation of chronic brain lesions is a crucial step when investigating the relationship between brain structure and (dys-)function. For this, manual tracing, although very time-consuming, is still the gold standard. In order to assess the possible contributions from other methods, we compared manual tracing of lesion boundaries with a newly developed semi-automated and a fully automated approach for lesion definition in a sample of chronic stroke patients (n = 11, 5 m, median age 12, range 10–30 years). Manual tracing requires substantially more human input (4.8–9.6 h/subject) than semi-automated (24.9 min/subject) and automated processing (1 min/subject). When compared with manual tracing as the gold standard, both the semi-automated (tested with 4 different smoothing filters) and the automated approach towards lesion definition performed on an acceptable level, with an average Dice's similarity index of .53–.60 (semi-automated) and .49 (automated processing). In all semi-automated and automated approaches, larger lesions were identified with a significantly higher performance than smaller lesions, as were central versus peripheral voxels, indicating that the surface-to-volume ratio explains this trend. The automated approach failed to identify two lesions. In several cases, indirect lesion effects (such as enlarged ventricles) were detected using the semi-automated or the automated approach. We conclude that manual tracing remains the gold standard for exact lesion delineation, but that semi-automated and automated approaches may be alternatives for larger lesions and/or larger studies. The detection of indirect lesion effects may be another application of such approaches in the future.


title: Developing a computer science-specific learning taxonomy

abstract: Bloom's taxonomy of the cognitive domain and the SOLO taxonomy are being increasingly widely used in the design and assessment of courses, but there are some drawbacks to their use in computer science. This paper reviews the literature on educational taxonomies and their use in computer science education, identifies some of the problems that arise, proposes a new taxonomy and discusses how this can be used in application-oriented courses such as programming.


title: A 300-mV 220-nW Event-Driven ADC With Real-Time QRS Detection for Wearable ECG Sensors

abstract: This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 $\mu {\rm m}$ CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.


title: Understanding user perceptions of transparent authentication on a mobile device

abstract: Due to the frequency with which smartphone owners use their devices, effortful authentication methods such as passwords and PINs are not an effective choice for smartphone authentication. Past research has offered solutions such as graphical passwords, biometrics and password hardening techniques. However, these solutions still require the user to authenticate frequently, which may become increasingly frustrating over time. Transparent authentication has been suggested as an alternative to such effortful solutions. It utilizes readily available behavioral biometrics to provide a method that runs in the background without requiring explicit user interaction. In this manner, transparent authentication delivers a less effortful solution with which the owner does not need to engage as frequently. We expand the current research into transparent authentication by surveying the user, an important stakeholder, regarding their opinions towards transparent authentication on a smartphone. We asked 30 participants to complete a series of tasks on a smartphone that was ostensibly protected with varying degrees of transparent authentication. We then surveyed participants regarding their opinions of transparent authentication, their opinions of the sensitivity of tasks and data on smartphones, and their perception of the level of protection provided to the data and apps on the device. We found that 90% of those surveyed would consider using transparent authentication on their mobile device should it become available. Furthermore, participants had widely varying opinions of the sensitivity of the experiment’s tasks, showing that a more granular method of smartphone security is justified. Interestingly, we found that the complete removal of security barriers, which is commonly cited as a goal in authentication research, does not align with the opinions of our participants. Instead, we found that having a few barriers to device and data access aided the user in building a mental model of the on-device security provided by transparent authentication. These results provide a valuable understanding to inform development of transparent authentication on smartphones since they provide a glimpse into the needs and wants of the end user.


title: Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

abstract: Traditional fault tree (FT) analysis is widely used for reliability and safety assessment of complex and critical engineering systems. The behavior of components of complex systems and their interactions such as sequence- and functional-dependent failures, spares and dynamic redundancy management, and priority of failure events cannot be adequately captured by traditional FTs. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. Modeling test and maintenance information on spare components is also very difficult. To address these difficulties, Monte Carlo simulation-based approach is used in this work to solve dynamic gates. The approach is first applied to a problem available in the literature which is having non-repairable components. The obtained results are in good agreement with those in literature. The approach is later applied to a simplified scheme of electrical power supply system of nuclear power plant (NPP), which is a complex repairable system having tested and maintained spares. The results obtained using this approach are in good agreement with those obtained using analytical approach. In addition to point estimates of reliability measures, failure time, and repair time distributions are also obtained from simulation. Finally a case study on reactor regulation system (RRS) of NPP is carried out to demonstrate the application of simulation-based DFT approach to large-scale problems.


EXPLORING THE POTENTIAL FOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES IN POLICE REPORT ANALYSIS - A Design Research Approach


title: Improved registration for 3D image creation using multiple texel images and incorporating low-cost GPS/INS measurements

abstract: The creation of 3D imagery is an important topic in remote sensing. Several methods have been developed to create 3D images from fused ladar and digital images, known as texel images. These methods have the advantage of using both the 3D ladar information and the 2D digital imagery directly, since texel images are fused during data acquisition. A weakness of these methods is that they are dependent on correlating feature points in the digital images. This can be difficult when image perspectives are significantly different, leading to low correlation values between matching feature points. This paper presents a method to improve the quality of 3D images created using existing approaches that register multiple texel images. The proposed method incorporates relatively low accuracy measurements of the position and attitude of the texel camera from a low-cost GPS/INS into the registration process. This information can improve the accuracy and robustness of the registered texel images over methods based on point-cloud merging or image registration alone. In addition, the dependence on feature point correlation is eliminated. Examples illustrate the value of this method for significant image perspective differences.


title: PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

abstract: As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.


title: Social media: A new vector for cyber attack

abstract: Internet with Social media have open the new ways of communication in the hyper connected world. This communication involves tweets, sharing photos, images, likes and comments etc. among the people all around the world. Over the past several years, we have seen that online social networking sites plays a crucial role which connect users to communicate with family, friends, professional groups and other community by using social media like, Twitter, Facebook, LinkedIn, MySpace, YouTube and Digg etc. Today social media is a great way to build network with people. However, it is unfortunate to note that social media sites can pose a variety of serious security risk and threats to users and organizations. This paper provides an in-depth detail of threats, security risks and different types of attacks using social media.


title: Autonomous Soft Robotic Fish Capable of Escape Maneuvers Using Fluidic Elastomer Actuators

abstract: In this work we describe an autonomous soft-bodied robot that is both self-contained and capable of rapid, continuum-body motion. We detail the design, modeling, fabrication, and control of the soft fish, focusing on enabling the robot to perform rapid escape responses. The robot employs a compliant body with embedded actuators emulating the slender anatomical form of a fish. In addition, the robot has a novel fluidic actuation system that drives body motion and has all the subsystems of a traditional robot onboard: power, actuation, processing, and control. At the core of the fish's soft body is an array of fluidic elastomer actuators. We design the fish to emulate escape responses in addition to forward swimming because such maneuvers require rapid body accelerations and continuum-body motion. These maneuvers showcase the performance capabilities of this self-contained robot. The kinematics and controllability of the robot during simulated escape response maneuvers are analyzed and compared with studies on biological fish. We show that during escape responses, the soft-bodied robot has similar input-output relationships to those observed in biological fish. The major implication of this work is that we show soft robots can be both self-contained and capable of rapid body motion.


title: Building Manipuri-English Machine Readable Dictionary by Implementing Ontology

abstract: Any system that hopes to process natural languages as people do must have information about words, their meaning, concept, relative words in another language and meaningful sentences are composed of meaningful words. Traditionally information is provided through electronic dictionaries. But these dictionary entries evolved for the convenience of human readers, not for machines. So, machine readable electronic dictionary becomes the central resources for Natural Language applications. Dictionaries and other lexical resources are not yet widely available in electronic form for Manipuri language. And there is no Manipuri-English machine readable dictionary that can provide both of lexical resources and conceptual information. This paper describes the process for developing Manipuri-English dictionary by implementing ontology. This implementation should provide a more effective combination of traditional Manipuri-English bilingual lexicographic information and their conceptual information.


title: Entropic Causal Inference

abstract: We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is simple in the true causal direction. We quantify simplicity using R\'enyi entropy. Our main result is that, under natural assumptions, if the exogenous variable has low $H_0$ entropy (cardinality) in the true direction, it must have high $H_0$ entropy in the wrong direction. We establish several algorithmic hardness results about estimating the minimum entropy exogenous variable. We show that the problem of finding the exogenous variable with minimum entropy is equivalent to the problem of finding minimum joint entropy given $n$ marginal distributions, also known as minimum entropy coupling problem. We propose an efficient greedy algorithm for the minimum entropy coupling problem, that for $n=2$ provably finds a local optimum. This gives a greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon Entropy). Our greedy entropy-based causal inference algorithm has similar performance to the state of the art additive noise models in real datasets. One advantage of our approach is that we make no use of the values of random variables but only their distributions. Our method can therefore be used for causal inference for both ordinal and also categorical data, unlike additive noise models.


title: A blockchain-based Trust System for the Internet of Things

abstract: One of the biggest challenges for the Internet of Things (IoT) is to bridge the currently fragmented trust domains. The traditional PKI model relies on a common root of trust and does not fit well with the heterogeneous IoT ecosystem where constrained devices belong to independent administrative domains. In this work we describe a distributed trust model for the IoT that leverages the existing trust domains and bridges them to create end-to-end trust between IoT devices without relying on any common root of trust. Furthermore we define a new cryptographic primitive, denoted as obligation chain designed as a credit-based Blockchain with a built-in reputation mechanism. Its innovative design enables a wide range of use cases and business models that are simply not possible with current Blockchain-based solutions while not experiencing traditional blockchain delays. We provide a security analysis for both the obligation chain and the overall architecture and provide experimental tests that show its viability and quality.


title: 3D smooth path planning for a UAV in cluttered natural environments

abstract: This paper presents a 3D path planing algorithm for an unmanned aerial vehicle (UAV) operating in cluttered natural environments. The algorithm satisfies the upper bounded curvature constraint and the continuous curvature requirement. In this work greater attention is placed on the computational complexity in comparison with other path-planning considerations. The rapidly-exploring random trees (RRTs) algorithm is used for the generation of collision free waypoints. The unnecessary waypoints are removed by a simple path pruning algorithm generating a piecewise linear path. Then a path smoothing algorithm utilizing cubic Bezier spiral curves to generate a continuous curvature path that satisfies the minimum radius of curvature constraint of UAV is implemented. The angle between two waypoints is the only information required for the generation of the continuous curvature path. The result shows that the suggested algorithm is simple and easy to implement compared with the Clothoids method.


title: A Big Data architecture for knowledge discovery in PubMed articles

abstract: The need of smart information retrieval systems is in contrast with the difficulties to deal with huge amount of data. In this paper we present a Big Data Analytics architecture used to implement a semantic similarity search tool for natural language texts in biomedical domain. The implemented methodology is based on Word Embeddings (WEs) models obtained using the word2vec algorithm. The system has been assessed with documents extracted from the whole PubMed library. It will be also presented a user friendly web front-end able to assess the methodology on a real context.


title: A split-capacitor vcm-based capacitor-switching scheme for low-power SAR ADCs

abstract: A split-capacitor Vcm-based capacitor-switching scheme is proposed for successive approximation register (SAR) analog-to-digital converters (ADCs) to reduce the capacitor-switching energy. By rearranging the structure and procedure of the capacitive array, the scheme can save the capacitor-switching energy by about 92% than the conventional scheme with better monotonicity. Meanwhile, a two-segment DC offset correction scheme for the comparator is also proposed to meet the speed and accuracy requirements. These techniques are utilized in the design of a 10b 70MS/s SAR ADC in 65nm 1P9M CMOS technology. Measurement results show a peak signal-to-noise-and-distortion ratio (SNDR) of 53.2dB, while consuming 960μW from 1.2V supply. The figure of merit (FoM) is 36.8fJ/Conversion-step and the total active area is 220×220μm2.


title: doi:10.1155/2008/372651 Research Article Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments

abstract: A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops, it builds upon complexity reduction concepts that previously have been applied within maximum likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both BPSK-modulated and BOC-(1,1) modulated navigation signals.


title: Review: Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

abstract: In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.


Challenges in adopting a Devops approach to software development and operations


title: Failure diagnosis using decision trees

abstract: We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each request and apply automated machine learning and data mining techniques to identify the causes of failures. We train decision trees on the request traces from time periods in which user-visible failures are present. Paths through the tree are ranked according to their degree of correlation with failure, and nodes are merged according to the observed partial order of system components. We evaluate this approach using actual failures from eBay, and find that, among hundreds of potential causes, the algorithm successfully identifies 13 out of 14 true causes of failure, along with 2 false positives. We discuss some results in applying simplified decision trees on eBay's production site for several months. In addition, we give a cost-benefit analysis of manual vs. automated diagnosis systems. Our contributions include the statistical learning approach, the adaptation of decision trees to the context of failure diagnosis, and the deployment and evaluation of our tools on a high-volume production service.


title: Deep Predictive Coding Networks

abstract: The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and context-sensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic model which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the top-down connections by showing the robustness of the proposed model to structured noise.


title: Automated Inference on Sociopsychological Impressions of Attractive Female Faces

abstract: This article is a sequel to our earlier work [25]. The main objective of our research is to explore the potential of supervised machine learning in face-induced social computing and cognition, riding on the momentum of much heralded successes of face processing, analysis and recognition on the tasks of biometric-based identification. We present a case study of automated statistical inference on sociopsychological perceptions of female faces controlled for race, attractiveness, age and nationality. Our empirical evidences point to the possibility of training machine learning algorithms, using example face images characterized by internet users, to predict perceptions of personality traits and demeanors.


title: Acoustic noise mitigation for high pole count switched reluctance machines through skewing method with multiphysics FEA simulations

abstract: This study investigates the effect of different skewing methods and angles for switched reluctance machines (SRM) having high pole count using multiphysics simulations. A 24/16 SRM is modeled for each skewing method and different skewing angles using 3D electromagnetic finite element analysis (FEA). Mechanical and acoustical analyses are performed to evaluate the effectiveness of different skewing methods. The results for each method are compared for the same torque levels. For Stator-only and Rotor-only skewing methods, torque output drops significantly, which reduces the effectiveness of these methods in achieving desired torque levels. However, implementing skewing on both the stator and rotor together has less impact on the torque production and has a significant impact on distributing the radial force on the stator back iron. Given an SRM designed to achieve 150kW of output power, multiphysics simulations predict a 10.5 dBA reduction in the acoustic noise for an optimized rotor/stator skewing angle of 13.5°.


Chapter 4 European guidelines for the management of chronic nonspecific low back pain


title: Radiation doses to children during modified barium swallow studies

abstract: BACKGROUND ::: There are minimal data on radiation doses to infants and children undergoing a modified barium swallow (MBS) study. ::: ::: ::: OBJECTIVE ::: To document screening times, dose area product (DAP) and effective doses to children undergoing MBS and to determine factors associated with increased screening times and effective dose. ::: ::: ::: MATERIALS AND METHODS ::: Fluoroscopic data (screening time, DAP, kVp) for 90 consecutive MBS studies using pulse fluoroscopy were prospectively recorded; effective dose was calculated and data were analyzed for effects of behavior, number of swallow presentations, swallowing dysfunction and medical problems. ::: ::: ::: RESULTS ::: Mean effective dose for the entire group was 0.0826 +/- 0.0544 mSv, screening time 2.48 +/- 0.81 min, and DAP 28.79 +/- 41.72 cGy cm2. Significant differences were found across three age groups (<or=1.0, >1.0-3.0 and >3.0 years) for effective dose (mean 0.1188, 0.0651 and 0.0529 mSv, respectively; P < 0.001), but not for screening time or DAP. Effective dose was correlated with screening time (P = 0.007), DAP (P < 0.001), number of swallow presentations (P = 0.007), lower age (P = 0.017), female gender (P = 0.004), and height (P < 0.001). Screening time was correlated with total number of swallow presentations (P < 0.001) and DAP (P < 0.001). ::: ::: ::: CONCLUSION ::: Screening times, DAP, effective dose, and child and procedural factors associated with higher effective doses are presented for children undergoing MBS studies.


title: Nursing Theory and Practice: Connecting the Dots

abstract: The authors propose connecting the dots among theory, practice, and research by adopting an expanded conceptual-theoretical-empirical structure of nursing knowledge and matrix process to guide the placement of nursing knowledge in a contextual whole. An overview of the theoretical journey of nursing knowledge development is contrasted with the journey from practice resulting in a theory-practice disconnect. Both approaches are united to present an integrated view of the dimensions of the knowledge development of nursing as a professional discipline.


DASS FOR CHILDREN AND ADOLESCENTS 1 Running Head : DASS FOR CHILDREN AND ADOLESCENTS Depression anxiety stress scale ( DASS ) : Is it valid for children and adolescents ?


Surgical Veneer Grafting Compensation for Natural Labial Plate Remodeling After Immediate Implant Placement


title: Comprehensive logfiles for autonomic systems

abstract: Summary form only given. A proposal for a new generation of logfiles with regard to new challenges posed by autonomic computing is presented. While a variety of techniques are being developed on the way to autonomic computing, the problem of a system's logfiles remains to be critical. Several recommendations for logfile design are introduced: (a) Event type and source of events have to be distinguishably incorporated into the design, (b) Logfiles should incorporate hierarchical numbering schemes, (c) Information contained in logfiles should be categorized into classes, (d) Logfiles have to easily lend themselves to automatic analysis, (e) Information density of logfiles should be considered in the design of logging functionality. A metric to measure information entropy of logfiles is proposed. The type of information to be included in logfiles in order to support various aspects of autonomic computing as originally defined by IBM's eight elements is specified.


title: Controlling tensegrity robots through evolution

abstract: Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400% better than a hand-coded solution, while the multiagent evolution performs 800% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.


title: Plant phenology recognition using deep learning: Deep-Pheno

abstract: Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national agriculture monitoring network systems, since inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions. In this paper, we utilize a deep learning architecture to recognize and classify phenological stages of several types of plants purely based on the visual data captured every half an hour by cameras mounted on the ground agro-stations that have been planted all over Turkey as part of an agriculture monitoring network system. A pre-trained Convolutional Neural Network architecture (CNN) is employed to automatically extract the features of images. In order to evaluate the performance of the approach proposed in this paper, the results obtained through CNN model are compared with those obtained by employing hand crafted feature descriptors. Experimental results suggest that CNN architecture outperforms the machine learning algorithms based on hand crafted features for the discrimination of phenological stages.


title: Powering a Ventricular Assist Device (VAD) With the Free-Range Resonant Electrical Energy Delivery (FREE-D) System

abstract: Wireless data communication technology has eliminated wired connections for data transfer to portable devices. Wireless power technology offers the possibility of eliminating the remaining wired connection: the power cord. For ventricular assist devices (VADs), wireless power technology will eliminate the complications and infections caused by the percutaneous wired power connection. Integrating wireless power technology into VADs will enable VAD implants to become a more viable option for heart failure patients (of which there are 80 000 in the United States each year) than heart transplants. Previous transcutaneous energy transfer systems (TETS) have attempted to wirelessly power VADs ; however, TETS-based technologies are limited in range to a few millimeters, do not tolerate angular misalignment, and suffer from poor efficiency. The free-range resonant electrical delivery (FREE-D) wireless power system aims to use magnetically coupled resonators to efficiently transfer power across a distance to a VAD implanted in the human body, and to provide robustness to geometric changes. Multiple resonator configurations are implemented to improve the range and efficiency of wireless power transmission to both a commercially available axial pump and a VentrAssist centrifugal pump [3]. An adaptive frequency tuning method allows for maximum power transfer efficiency for nearly any angular orientation over a range of separation distances. Additionally, laboratory results show the continuous operation of both pumps using the FREE-D system with a wireless power transfer efficiency upwards of 90%.


title: Continuous dimensionality characterization of image structures

abstract: Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patches, edge-like structures and junctions. The main novelty of our approach is the representation of confidences as prior probabilities which can be used within a probabilistic framework. To show the potential of our continuous representation, we highlight applications in various contexts such as image structure classification, feature detection and localisation, visual scene statistics and optic flow evaluation.


title: Rho signaling inEntamoeba histolytica modulates actomyosin-dependent activities stimulated during invasive behavior

abstract: Interaction of Entamoeba histolytica trophozoites with target cells and substrates activates signaling pathways in the parasite. Phosphorylation cascades triggered by phospho-inositide and adenyl-cyclase-dependent pathways modulate reorganization of the actin cytoskeleton to form structures that facilitate adhesion. In contrast, little is known about participation of Rho proteins and Rho signaling in actin rearrangements. We report here the in vivo expression of at least one Rho protein in trophozoites, whose activation induced actin reorganization and actin-myosin interaction. Antibodies to EhRhoA1 recombinant protein mainly localized Rho in the cytosol of nonactivated amoebae, but it was translocated to vesicular membranes and to some extent to the plasma membrane after treatment with lysophosphatidic acid (LPA), a specific agonist of Rho activation. Activated Rho was identified in LPA-treated trophozoites. LPA induced striking polymerization of actin into distinct dynamic structures. Disorganization of these structures by inhibition of Rho effector, Rho-kinase (ROCK), and by ML-7, an inhibitor of myosin light chain kinase dependent phosphorylation of myosin light chain, suggested that the actin structures also contained myosin. LPA stimulated concanavalin-A-mediated formation of caps, chemotaxis, invasion of extracellular matrix substrates, and erythrophagocytosis, but not binding to fibronectin. ROCK inhibition impaired LPA-stimulated functions and to some extent adhesion to fibronectin. Similar results were obtained with ML-7. These data suggest the presence and operation of Rho-signaling pathways in E. histolytica, that together with other, already described, signaling routes modulate actomyosin-dependent motile processes, particularly stimulated during invasive behavior.


Linguistic Relativity


title: Converting a Plant to a Battery and Wireless Sensor with Scatter Radio and Ultra-Low Cost

abstract: Electric potential (EP) signals are produced in plants through intracellular processes, in response to external stimuli (e.g., watering, mechanical stress, light, and acquisition of nutrients). However, wireless transmission of a massive amount of biologic EP signals (from one or multiple plants) is hindered by existing battery-operated wireless technology and increased associated monetary cost. In this paper, a self-powered batteryless EP wireless sensor is presented that harvests near-maximum energy from the plant itself and transmits the EP signal tens of meters away with a single switch, based on inherently low-cost and low-power bistatic scatter radio principles. The experimental results confirm the ability of the proposed wireless plant sensor to achieve a fully autonomous operation by harvesting the energy generated by the plant itself. In addition, EP signals experimentally acquired by the proposed wireless sensor from multiple plants have been processed using nonnegative matrix factorization, demonstrating a strong correlation with environmental light irradiation intensity and plant watering. The proposed low-cost batteryless plant-as-sensor-and-battery instrumentation approach is a first but solid step toward large-scale electrophysiology studies of important socioeconomic impact in ecology, plant biology, and precision agriculture.


title: A WBG based three phase 12.5 kW 500 kHz CLLC resonant converter with integrated PCB winding transformer

abstract: The three phase resonant converter gains its popularity in high power areas like off-board charger due to its ability to reduce the current stress on devices and achieve higher efficiency. However, very few takes this advantage for the transformer integration to reduce the size of the magnetic components. In addition, by using wide-band-gap devices, the switching frequency of power electronics converters can be pushed to a much higher level, making it possible to implement Printed Circuit Board (PCB) transformer. In this paper, a novel 6-layer only PCB winding transformer is proposed for a three phase CLLC resonant converter. Not only all the resonant inductors are integrated into the transformer, but also the three phase transformers are integrated together so that the size of the magnetic component is significantly reduced. A 12.5 kW 500 kHz hardware prototype with over 97.3% efficiency and 155 W/in3 power density is built to verify the feasibility of the proposed structure.


title: Java™ on the bare metal of wireless sensor devices: the squawk Java virtual machine

abstract: The Squawk virtual machine is a small Java™ virtual machine (VM) written mostly in Java that runs without an operating system on a wireless sensor platform. Squawk translates standard class file into an internal pre-linked, position independent format that is compact and allows for efficient execution of bytecodes that have been placed into a read-only memory. In addition, Squawk implements an application isolation mechanism whereby applications are represented as object and are therefore treated as first class objects (i.e., they can be reified). Application isolation also enables Squawk to run multiple applications at once with all immutable state being shared between the applications. Mutable state is not shared. The combination of these features reduce the memory footprint of the VM, making it ideal for deployment on small devices.Squawk provides a wireless API that allows developers to write applications for wireless sensor networks (WSNs), this API is an extension of the generic connection framework (GCF). Authentication of deployed files on the wireless device and migration of applications between devices is also performed by the VM.This paper describes the design and implementation of the Squawk VM as applied to the Sun™ Small Programmable Object Technology (SPOT) wireless device; a device developed at Sun Microsystems Laboratories for experimentation with wireless sensor and actuator applications.


title: Building ontology based knowledge maps to assist business process re-engineering

abstract: Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.


A Framework to Secure Medical Image Storage in Cloud Computing Environment


title: Research Challenges in Financial Data Modeling and Analysis.

abstract: Abstract Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. These challenges are also of interest to the legal profession as well as to regulators. The discussion is relevant to technology firms that support the growing field of FinTech.


title: CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis

abstract: We present CoCoSpot, a novel approach to recognize botnet command and control channels solely based on traffic analysis features, namely carrier protocol distinction, message length sequences and encoding differences. Thus, CoCoSpot can deal with obfuscated and encrypted C&C protocols and complements current methods to fingerprint and recognize botnet C&C channels. Using average-linkage hierarchical clustering of labeled C&C flows, we show that for more than 20 recent botnets and over 87,000 C&C flows, CoCoSpot can recognize more than 88% of the C&C flows at a false positive rate below 0.1%.


abstract: Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248).


title: Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

abstract: Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle optimal actions. To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment. Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN). The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection. With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy. The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.


title: A 200ns settling time fully integrated low power LDO regulator with comparators as transient enhancement

abstract: The design of a low power low drop out voltage regulator with no off-chip capacitor and fast transient responses is presented in this paper. The LDO regulator uses a combination of a low power operational trans-conductance amplifier and comparators to drive the gate of the PMOS pass element. The amplifier ensures stability and accurate setting of the output voltage in addition to power supply rejection. The comparators ensure fast response of the regulator to any load or line transients. A settling time of less than 200ns is achieved in response to a load transient step of 50mA with a rise time of 100ns with an output voltage spike of less than 200mV at an output voltage of 3.25 V. A line transient step of 1V with a rise time of 100ns results also in a settling time of less than 400ns with a voltage spike of less than 100mV when the output voltage is 2.6V. The regulator is fabricated using a standard 0.35μm CMOS process and consumes a quiescent current of only 26 μA.


title: Response of phytoplankton to ecological engineering remediation of a Canadian Shield Lake affected by acid mine drainage

abstract: Abstract Multivariate analyses were used to relate changes in water chemistry with those in the composition of the phytoplankton in two adjacent northern Ontario shield Lakes in Canada. One of the lakes is affected by both acid mine drainage (AMD) and a succession of remediation measures. The other, much larger lake is pristine and was monitored for both water quality and phytoplankton. The AMD lake, already contaminated at the start of the 17-year project, experienced further increases in Zn concentrations from around 10 mg L −1 to 35 mg L −1 and a pH decrease from 5 to 3 during the project. Over the same period, phytoplankton diversity declined from 52 taxa in 59 samples to 42 taxa in 74 samples. Only Ochromonas spp. and Chlamydomonas spp., apparently tolerant of a wide range of physical and chemical conditions, were consistently present in both lakes. Other taxa ( Peridinium , Pinnularia and Euglena ) were frequent in the AMD lake in the early stages of the project but then declined. Asterionella and Botrycoccus (although common in the pristine lake), were rarely observed in the AMD lake. Lepocinclis proliferated as metal concentrations increased and pH declined in the AMD lake but was rarely found in the clean lake. Such gradual adaptation or changes in phytoplankton populations have not been reported previously for such extreme chemical conditions. The multivariate statistical analysis (PCA, CCA, DCA) revealed that the highest number of taxa were found to be controlled by pH and Ni in the years 1986–1992. One-third of the taxa in that period were more closely associated with pH alone. In the late stages of the study (1999–2003) the predominant controlling factors appeared to be metals and conductivity. Most of the taxa followed the time/pH/metal axis, suggesting that occurrence changed with slowly changing water conditions. Those taxa which fell outside the time/pH/Zn/Cu/Al axis included mostly Chrysophytes, Cryptophytes, and Chlorophytes, although there were some Euglenophyceae and diatoms in this group. By the end of the study in 2003, the combined effect of the restoration measures brought about the development of an extensive periphytic underwater meadow, along with a slight improvement in water quality and an increase in the number of phytoplankton taxa.


title: Information Diffusion Analysis of Rumor Dynamics over a Social-Interaction Based Model

abstract: Rumors may potentially cause undesirable effect such as the widespread panic in the general public. Especially, with the unprecedented growth of different types of social and enterprise networks, rumors could reach a larger audience than before. Many researchers have proposed different approaches to analyze and detect rumors in social networks. However, most of them either study on theoretical models without real data experiments or use content-based analysis and limited information diffusion analysis without fully considering social interactions. In this paper, we propose a social interaction based model FAST by taking four major properties of social interactions into account including familiarity, activeness, similarity, and trustworthiness. Also, we evaluate our model on real data from Sina Weibo (Twitter-like social network in China), which contains around 200 million tweets and 14 million Weibo users. Based on our model, we create a new metrics Fractional Directed Power Community Index (FD-PCI) derived from μ-PCI to identify the influential spreaders in social networks. FD-PCI shows better performance than conventional metrics such as K-core index and PageRank. Moreover, we obtain interesting influential features to detect rumors by the comparison between rumor and real news dynamics.


title: A Real-Time fMRI-Based Spelling Device Immediately Enabling Robust Motor-Independent Communication

abstract: Summary Human communication entirely depends on the functional integrity of the neuromuscular system. This is devastatingly illustrated in clinical conditions such as the so-called locked-in syndrome (LIS) [1], in which severely motor-disabled patients become incapable to communicate naturally—while being fully conscious and awake. For the last 20 years, research on motor-independent communication has focused on developing brain-computer interfaces (BCIs) implementing neuroelectric signals for communication (e.g., [2–7]), and BCIs based on electroencephalography (EEG) have already been applied successfully to concerned patients [8–11]. However, not all patients achieve proficiency in EEG-based BCI control [12]. Thus, more recently, hemodynamic brain signals have also been explored for BCI purposes [13–16]. Here, we introduce the first spelling device based on fMRI. By exploiting spatiotemporal characteristics of hemodynamic responses, evoked by performing differently timed mental imagery tasks, our novel letter encoding technique allows translating any freely chosen answer (letter-by-letter) into reliable and differentiable single-trial fMRI signals. Most importantly, automated letter decoding in real time enables back-and-forth communication within a single scanning session. Because the suggested spelling device requires only little effort and pretraining, it is immediately operational and possesses high potential for clinical applications, both in terms of diagnostics and establishing short-term communication with nonresponsive and severely motor-impaired patients.


title: A graph-based approach to commonsense concept extraction and semantic similarity detection

abstract: Commonsense knowledge representation and reasoning support a wide variety of potential applications in fields such as document auto-categorization, Web search enhancement, topic gisting, social process modeling, and concept-level opinion and sentiment analysis. Solutions to these problems, however, demand robust knowledge bases capable of supporting flexible, nuanced reasoning. Populating such knowledge bases is highly time-consuming, making it necessary to develop techniques for deconstructing natural language texts into commonsense concepts. In this work, we propose an approach for effective multi-word commonsense expression extraction from unrestricted English text, in addition to a semantic similarity detection technique allowing additional matches to be found for specific concepts not already present in knowledge bases.


title: FACTORS AFFECTING CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN EGYPT

abstract: The study makes a significant contribution to the corporate social responsibility (CSR) disclosure literature by offering the first study of its type undertaken in Egypt as an example of a developing country that examines the determinants of individual and aggregated types of CSR information. Using a sample of 111 Egyptian listed companies for the period of 2005–2010, we find that 66% of the Egyptian listed companies disclose on average 10–50 CSR statements. In addition, we find that product/customer information is used extensively by Egyptian listed companies compared with other types of CSR information. Finally we find that profitability is the main determinant for the aggregated and most of individual CSR information in Egypt.


title: New gait metrics for biometric authentication using a 3-axis acceleration

abstract: Biometric authentication mechanisms are excellent alternatives to often inconvenient interaction authentication methods such as PIN numbers in mobile devices. This research introduces the new concept of using gait signature metrics for biometric authentication. This procedure verifies each subject using only acceleration. We first use a single wireless sensor device to collect data on subjects' gait patterns. By dividing each gait cycle into an Acceleration Phase and a Deceleration Phase, we derive seven periodic and characteristic gait signature metrics. Gait signature metrics can be classified as acceleration metrics, deceleration metrics, and ratio metric. Acceleration metrics represent a degree of dynamic activity when heel-strike actions and mid-stance actions occur, whereas deceleration metrics measure a degree of dynamic activity when mid-stance actions and successive heel-strike of other foot. The last metric, ratio metric, present the relationship between the acceleration metrics and the deceleration metrics. Using the gait signature metrics, we succeeded in differentiating each subject with 100% accuracy.


Claims, Errors, and Compensation Payments in Medical Malpractice Litigation


title: Artistic composition for painterly rendering

abstract: We present a technique for painterly renderings that follows a decomposition of the canvas into a set of regions and layers (coarse to fine). The regions reflect the spatial arrangement of the composition and the order in which the painting is to be created (typically back to front), and are produced in a way that new strokes only minimally paint over existing ones. Layers reflect the application of tools and are optimized for certain brush sizes. The number of strokes and colors that are needed to represent an input image are minimized by this decomposition, which is good for software, but essential for hardware-based rendering. Our method allows us to apply different painting styles to different regions as well as layers, and to create painterly renderings with more artistic freedom. We demonstrate our decomposition technique on images that are processed using hierarchical segmentation techniques.


title: Being Accurate Is Not Enough: New Metrics for Disk Failure Prediction

abstract: Traditionally, disk failure prediction accuracy is used to evaluate disk failure prediction model. However, accuracy may not reflect their practical usage (protecting against failures, rather than only predicting failures) in cloud storage systems. In this paper, we propose two new metrics for disk failure prediction models: migration rate, which measures how much at-risk data is protected as a result of correct failure predictions, and mismigration rate, which measures how much data is migrated needlessly as a result of false failure predictions. To demonstrate their effectiveness, we compare disk failure prediction methods: (a) a classification tree (CT) model vs. a state-of-the-art recurrent neural network (RNN) model, and (b) a proposed residual life prediction model based on gradient boosted regression trees (GBRTs) vs. RNN. While prediction accuracy experiments favor the RNN model, migration rate experiments can favor the CT and GBRT models (depending on transfer rates). We conclude that prediction accuracy can be a misleading metric. Moreover, the proposed GBRT model offers a practical improvement in disk failure prediction in real-world data centers.


title: Stochastic Security for Operations Planning With Significant Wind Power Generation

abstract: In their attempt to cut down on greenhouse gas emissions from electricity generation, several countries are committed to install wind power generation up to and beyond the 10%-20% penetration mark. However, the large-scale integration of wind power represents a challenge for power system operations planning because wind power 1) cannot be dispatched in the classical sense; and 2) its output varies as weather conditions change. This warrants the investigation of alternative short-term power system operations planning methods capable of better coping with the nature of wind generation while maintaining or even improving the current reliability and economic performance of power systems. To this end, this paper formulates a short-term forward electricity market-clearing problem with stochastic security capable of accounting for nondispatchable and variable wind power generation sources. The principal benefit of this stochastic operation planning approach is that, when compared to a deterministic worst-case scenario planning philosophy, it allows greater wind power penetration without sacrificing security.


title: Groundnut leaf disease detection and classification by using back probagation algorithm

abstract: Many studies shows that quality of agricultural products may be reduced from many causes. One of the most important factors contributing to low yield is disease attack. The plant disease such as fungi, bacteria and viruses. The leaf disease completely destroys the quality of the leaf. Common ground nut disease is cercospora. It is one of the type of disease in early stage of ground nut leaf. The upgraded processing pattern comprises of four leading steps. Initially a color renovation constitute intended for the input RGB image is formed, This RGB is converted into HSV because RGB is for Color generation and color descriptor. The next step is plane separation. Next performed the color features. Then using back propagation algorithm detection of leaf disease is done.


title: Fabrication of omni-directional driving system using unconstrained steel ball

abstract: An omni-directional driving system with one spherical wheel is proposed in this paper. This system can overcome the limitations of existing driving systems, such as structural limitations that reduce vertical, horizontal, and diagonal movements. The proposed driving system is composed of two stepper motors, a steel ball covered by steel ball bearing, a counterweight to eliminate eccentricity, and ball plungers for balance. All parts of this structure are located at the same distance from the center of the system to place the center of gravity at the system center. The ball bearing was designed for even rotation and smooth directional changes of the spherical wheel using a model similar to that in a computer mouse ball. Steel was selected for the ball bearing material to prevent slipping on the ground. Moreover, the steel ball is stable because of the support from the ball bearing. One of the stepping motors is used for driving the spherical wheel. The other stepping motor enables movement in the desired direction by rotating on the central axis. The ATmega128 microcontroller is used for controlling the two stepping motors. Driving experiments were executed in a variety of environments, confirming the performance and validity of the omni-directional driving system.


Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome


title: FACTORS AFFECTING E-GOVERNMENT ADOPTION IN THE STATE OF QATAR

abstract: Electronic government (e-government) initiatives are in their early stages in many developing countries and faced with various issues pertaining to their implementation, adoption and diffusion. Although egovernment has increased transparency and improved communication and access to information for citizens, digital diffusion of information is often achieved at high cost to government agencies. Conversely, citizens’ adoption of e-government services has been less than satisfactory in most countries. While studies by researchers continue to outline the most salient adoption constructs, as well as various frameworks and models for understanding adoption, research by independent consultancy/research organisations has produced a host of statistics and league tables of good and bad practices of service delivery. Like many other developing countries, the e-government initiative in the state of Qatar has faced a number of challenges since its inception in 2000. This study utilises the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the adoption of e-government services in the state of Qatar. 1179 citizens were surveyed to collect primary data. A regression analysis was conducted to examine the influence of the factors adapted from the UTAUT on e-government adoption. Reliability test reported values of the various constructs vary between (0.74) and (0.91). The findings reveal that effort expectancy and social influences determine citizens’ behavioural intention towards e-government. Additionally, facilitating conditions and behavioural intention were found to determine citizens’ use of e-government services in Qatar. Implications for practice and research are discussed.


title: An Improving Deception Detection Method in Computer-Mediated Communication

abstract: Online deception is disrupting our daily life, organizational process, and even national security. Existing deception detection approaches followed a traditional paradigm by using a set of cues as antecedents, and used a variety of data sets and common classification models to detect deception, which were demonstrated to be an accurate technique, but the previous results also showed the necessity to expand the deception feature set in order to improve the accuracy. In our study, we propose a novel feature selection method of the combination of CHI statistics and hypothesis testing, and achieve the accuracy level of 86% and F-measure of 0.84 by using the novel feature sets and SVM classification models, which exceeds the previous experiment results.


title: Estimating PageRank on graph streams

abstract: This article focuses on computations on large graphs (e.g., the web-graph) where the edges of the graph are presented as a stream. The objective in the streaming model is to use small amount of memory (preferably sub-linear in the number of nodes n) and a smaller number of passes. In the streaming model, we show how to perform several graph computations including estimating the probability distribution after a random walk of length l, the mixing time M, and other related quantities such as the conductance of the graph. By applying our algorithm for computing probability distribution on the web-graph, we can estimate the PageRank p of any node up to an additive error of se p+e in O(sM/α) passes and O(min(nα+1/esM/α+(1/e)Mα, α nsMα + (1/e)sM/α)) space, for any α i (0,1]. Specifically, for e e M/n, α e M−1/2, we can compute the approximate PageRank values in O(nM−1/4) space and O(M3/4) passes. In comparison, a standard implementation of the PageRank algorithm will take O(n) space and O(M) passes. We also give an approach to approximate the PageRank values in just O(1) passes although this requires O(nM) space.


title: The magnocellular deficit theory of dyslexia: the evidence from contrast sensitivity

abstract: A number of authors have made the claim that dyslexia is the result of a deficit in the magnocellular part of the visual system. Most of the evidence cited in support of this claim is from contrast sensitivity studies. The present review surveys this evidence. The result of this survey shows that the support for the magnocellular deficit theory is equivocal. In the case of spatial contrast sensitivity there clearly are results that are consistent with the magnocellular deficit theory; however, these results are outnumbered both by studies that have found no loss of sensitivity and by studies that have found contrast sensitivity reductions that are inconsistent with a magnocellular deficit. Many of the studies of temporal contrast sensitivity are also difficult to reconcile with a magnocellular deficit. The evidence from studies of contrast sensitivity is therefore highly conflicting with regard to the magnocellular system deficit theory of dyslexia.


title: A Multi-tenant Web Application Framework for SaaS

abstract: Software as a Service (SaaS) is a software delivery model in which software resources are accessed remotely by users. Enterprises find SaaS attractive because of its low cost. SaaS requires sharing of application servers among multiple tenants for low operational costs. Besides the sharing of application servers, customizations are needed to meet requirements of each tenant. Supporting various levels of configuration and customization is desirable for SaaS frameworks. This paper describes a multi-tenant web application framework for SaaS. The proposed framework supports runtime customizations of user interfaces and business logics by use of file-level namespaces, inheritance, and polymorphism. It supports various client-side web application technologies.


Effect of a Two Dimensional Shadowing Model on System Level Performance


title: Efficient temporal consistency for streaming video scene analysis

abstract: We address the problem of image-based scene analysis from streaming video, as would be seen from a moving platform, in order to efficiently generate spatially and temporally consistent predictions of semantic categories over time. In contrast to previous techniques which typically address this problem in batch and/or through graphical models, we demonstrate that by learning visual similarities between pixels across frames, a simple filtering algorithfiltering algorithmm is able to achieve high performance predictions in an efficient and online/causal manner. Our technique is a meta-algorithm that can be efficiently wrapped around any scene analysis technique that produces a per-pixel semantic category distribution. We validate our approach over three different scene analysis techniques on three different datasets that contain different semantic object categories. Our experiments demonstrate that our approach is very efficient in practice and substantially improves the consistency of the predictions over time.


title: Low-Profile Top-Hat Monopole Yagi Antenna for End-Fire Radiation

abstract: A top-hat monopole Yagi antenna is presented to produce an end-fire radiation beam. The antenna has an extremely low profile and wide operating bandwidth. It consists of a folded top-hat monopole as the driven element and four short-circuited top-hat monopoles as parasitic elements. A broad bandwidth can be achieved by adjusting the different resonances introduced by the driven and parasitic elements. A prototype operating at the UHF band ( ${\mathbf{f}_\mathbf{0}} = \mathbf{550}\;\mathbf{MHz}$ ) is fabricated and tested. Measured results show that a fractional bandwidth ( $\vert{\mathbf{S}_{\mathbf{11}}}\vert ) of 20.5% is obtained while the antenna height is only ${\mathbf{\lambda }_\mathbf{0}}/\mathbf{28}$ at the center frequency.


THE PARAMETRIZATION FOR THE MATRIX PENCIL A + BKC WITH CONSTANT RANK AND ITS APPLICATION


title: Design, Prototyping, and Analysis of a Low Cost Axial-Flux Coreless Permanent-Magnet Generator

abstract: This paper presents the design, prototyping, and analysis of a relatively small and cheap axial-flux three-phase coreless permanent-magnet generator. The excitation of the machine is done by rectangular flat shaped neodymium-iron-boron magnets. A two-dimensional model of the machine is analyzed with finite-element software to obtain the machine parameters. One special feature of the constructed generator is in the design and prototyping of nonferromagnetic holders to counteract centrifugal forces acting on magnets, especially at high speeds. By implementing the nonferromagnetic holders, one can expect to construct high-speed axial-flux permanent-magnet generators at low cost.


title: Depth-Based Hand Pose Estimation: Methods, Data, and Challenges

abstract: Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.


title: Skip Connections Eliminate Singularities

abstract: Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures. A completely satisfactory explanation for their success remains elusive. Here, we present a novel explanation for the benefits of skip connections in training very deep networks. The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model. Several such singularities have been identified in previous works: (i) overlap singularities caused by the permutation symmetry of nodes in a given layer, (ii) elimination singularities corresponding to the elimination, i.e. consistent deactivation, of nodes, (iii) singularities generated by the linear dependence of the nodes. These singularities cause degenerate manifolds in the loss landscape that slow down learning. We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent. Moreover, for typical initializations, skip connections move the network away from the "ghosts" of these singularities and sculpt the landscape around them to alleviate the learning slow-down. These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.


title: Learning Latent Permutations with Gumbel-Sinkhorn Networks

abstract: Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collection of new methods for end-to-end learning in such models that approximate discrete maximum-weight matching using the continuous Sinkhorn operator. Sinkhorn iteration is attractive because it functions as a simple, easy-to-implement analog of the softmax operator. With this, we can define the Gumbel-Sinkhorn method, an extension of the Gumbel-Softmax method (Jang et al. 2016, Maddison2016 et al. 2016) to distributions over latent matchings. We demonstrate the effectiveness of our method by outperforming competitive baselines on a range of qualitatively different tasks: sorting numbers, solving jigsaw puzzles, and identifying neural signals in worms.


title: Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

abstract: Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAVs' trajectory and power control. The formulated problem is a mixed integer non-convex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the non-convex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge to at least a locally optimal solution. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.


title: An architecture for interspatial communication

abstract: Digital infrastructure in modern urban environments is currently very Internet-centric, and involves transmitting data to physically remote environments. The cost for this is data insecurity, high response latency and unpredictable reliability of services. In this paper, we lay out a software architecture that inverts the current model by building an operating system designed to securely connect physical spaces with extremely low latency, high bandwidth local-area computation capabilities and service discovery. We describe our early prototype design OSMOSE, which is based on unikernels and a distributed store.


title: Universal Design for Learning: meeting the challenge of individual learning differences through a neurocognitive perspective

abstract: The traditional “one-size-fits-all” approach to curriculum denies the vast individual differences in learning strengths, challenges, and interests. The focus of this article is a novel approach, called Universal Design for Learning, to addressing the challenge of individual learner differences. Cognitive science research suggests the joint action of three broad sets of neural networks in cognition and learning: one that recognizes patterns, one that plans and generates patterns, and one that determines which patterns are important. These networks, referred to in this paper as recognition, strategic, and affective networks, are subject to individual differences that impact how individual students learn. This paper describes these networks and how the Universal Design for Learning framework makes use of this networks-based perspective to structure the consideration of individual learner differences and guide the design of a flexible, technology-rich curriculum that provides rich options for meeting diverse student needs.


title: Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation

abstract: In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.


title: Stegobot: a covert social network botnet

abstract: We propose Stegobot, a new generation botnet that communicates over probabilistically unobservable communication channels. It is designed to spread via social malware attacks and steal information from its victims. Unlike conventional botnets, Stegobot traffic does not introduce new communication endpoints between bots. Instead, it is based on a model of covert communication over a social-network overlay - bot to botmaster communication takes place along the edges of a social network. Further, bots use image steganography to hide the presence of communication within image sharing behavior of user interaction. We show that it is possible to design such a botnet even with a less than optimal routing mechanism such as restricted flooding. We analyzed a real-world dataset of image sharing between members of an online social network. Analysis of Stegobot's network throughput indicates that stealthy as it is, it is also functionally powerful - capable of channeling fair quantities of sensitive data from its victims to the botmaster at tens of megabytes every month.


Seasonal abundance of Stelidota geminata (Say) in selected habitats


title: Low-Complexity MMSE Signal Detection Based on Richardson Method for Large-Scale MIMO Systems

abstract: Minimum mean square error (MMSE) signal detection is near-optimal for uplink multi-user large-scale MIMO systems with hundreds of antennas at the base station, but involves matrix inversion with high complexity. In this paper, we first prove that the filtering matrix of the MMSE algorithm in large-scale MIMO is symmetric positive definite, based on which we propose a low-complexity signal detection algorithm by exploiting the Richardson method to avoid the complicated matrix inversion. The proof of the convergence of the proposed scheme is also provided. We then propose a zone-based initial solution by simply checking the values of the received signals, which can accelerate the convergence rate of the Richardson method for high-order modulations to reduce the complexity further. The analysis shows that the complexity can be reduced from O(K3) to O(K2) by the proposed signal detection algorithm, where K is the number of users. Simulation results indicate that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm and achieves the near-optimal performance of the classical MMSE algorithm.


title: Short- and long-term effects of ciliary neurotrophic factor on androgen-sensitive motoneurons in the lumbar spinal cord.

abstract: Motoneuron death in the spinal nucleus of the bulbocavernosus (SNB) and the dorsolateral nucleus (DLN) of the lumbar spinal cord is androgen regulated. As a result, many more SNB and DLN motoneurons die in perinatal female rats than in males, whereas treatment of newborn females with androgen results in a permanent sparing of the motoneurons and their target muscles. We previously observed that a neurotrophic molecule, ciliary neurotrophic factor (CNTF), also arrests the death of SNB motoneurons and their target musculature, at least in the short term. The present study compares the short- and long-term consequences of perinatal CNTF treatment on motoneuron number in the SNB, the DLN, and the retrodorsolateral nucleus (RDLN), a motor pool in the lower lumbar cord that does not exhibit hormone-regulated cell death. Female pups were treated with CNTF or vehicle alone from embryonic day 22 through postnatal day 6 (P6). Motoneuron number in the each nucleus was then determined immediately after treatment on P7, or 10 weeks later (P77). CNTF treatment significantly elevated motoneuron number in the SNB and DLN on P7; the volume of SNB target muscles on P7 was also greater in the CNTF-treated group. These effects were transient, however, as motoneuron number and ratings of muscle size were not different in CNTF-and vehicle-treated females on P77. Perinatal CNTF treatment did not alter cell number in the RDLN at either age. The finding that effects of CNTF on SNB and DLN motoneuron number are short lived contrasts with the permanent effects of early androgen treatment, and has implications for molecular models of the actions of androgen and neurotrophic factors on the developing spinal cord.


title: Multilingual Text-to-Speech Synthesis : The Bell Labs Approach

abstract: List of Figures. List of Tables. Foreword L. Pols. Preface. 1. Introduction J. van Santen, R. Sproat. 2. Methods and Tools J. van Santen, R. Sproat. 3. Multilingual Text Analysis R. Sproat, et al. 4. Further Issues in Text Analysis R. Sproat. 5. Timing J. van Santen. 6. Intonation J. van Santen, et al. 7. Synthesis J. Olive, et al. 8. Evaluation J. van Santen. 9. Further Issues R. Sproat, et al. A: Character Set Encodings. B: Glossary of Grammatical Labels. References. Name Index. Subject Index.


title: Understanding and Predicting Question Subjectivity in Social Question and Answering

abstract: The explosive popularity of social networking sites has provided an additional venue for online information seeking. By posting questions in their status updates, more and more people are turning to social networks to fulfill their information needs. Given that understanding individuals’ information needs could improve the performance of question answering, in this paper, we model the task of intent detection as a binary classification problem, and thus for each question, two classes are defined: subjective and objective. We use a comprehensive set of lexical, syntactical, and contextual features to build the classifier and the experimental results show satisfactory classification performance. By applying the classifier on a larger dataset, we then present in-depth analyses to compare subjective and objective questions, in terms of the way they are being asked and answered. We find that the two types of questions exhibited very different characteristics, and further validate the expected benefits of differentiating questions according to their subjectivity orientations.


title: A 79-GHz LTCC microstrip half-grid array antenna using a laminated waveguide feed

abstract: A microstrip half-grid array antenna (HGA) based on low temperature co-fired ceramic (LTCC) technology is presented in this paper. The antenna is designed for the 77-81 GHz radar frequency band and uses a high permittivity material (er = 7.3). The traditional single-grid array antenna (SGA) uses two radiating elements in the H-plane. For applications using digital beam forming, the focusing of an SGA in the scanning plane (H-plane) limits the field of view (FoV) of the radar system and the width of the SGA enlarges the minimal spacing between the adjacent channels. To overcome this, an array antenna using only half of the grid as radiating element was designed. As feeding network, a laminated waveguide with a vertically arranged power divider was adopted. For comparison, both an SGA and an HGA were fabricated. The measured results show: using an HGA, an HPBW increment in the H-plane can be achieved and their beam patterns in the E-plane remain similar. This compact LTCC antenna is suitable for radar application with a large FoV requirement.


title: Meditation, mindfulness and cognitive flexibility

abstract: This study investigated the link between meditation, self-reported mindfulness and cognitive flexibility as well as other attentional functions. It compared a group of meditators experienced in mindfulness meditation with a meditation-naive control group on measures of Stroop interference and the ‘‘d2-concentration and endurance test”. Overall the results suggest that attentional performance and cognitive flexibility are positively related to meditation practice and levels of mindfulness. Meditators performed significantly better than non-meditators on all measures of attention. Furthermore, self-reported mindfulness was higher in meditators than non-meditators and correlations with all attention measures were of moderate to high strength. This pattern of results suggests that mindfulness is intimately linked to improvements of attentional functions and cognitive flexibility. The relevance of these findings for mental balance and well-being are discussed.


title: An improved design of OLED display driver circuit

abstract: In this paper, an improved design of data driver for organic light-emitting diode (OLED) displays is presented. The proposed scheme controls brightness of OLED by adjusting the amplitude using MSB data and width of the current using LSB data. The simulation results show that the uniformity of the output current is improved. The power consumption also is reduced.


title: R-CNNs for Pose Estimation and Action Detection

abstract: We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images. Our approach involves training an R-CNN detector with loss functions depending on the task being tackled. We evaluate our method on the challenging PASCAL VOC dataset and compare it to previous leading approaches. Our method gives state-of-the-art results for keypoint and action prediction. Additionally, we introduce a new dataset for action detection, the task of simultaneously localizing people and classifying their actions, and present results using our approach.


Farasa: A Fast and Furious Segmenter for Arabic