title: C3: internet-scale control plane for video quality optimization

abstract: As Internet video goes mainstream, we see increasing user expectations for higher video quality and new global policy requirements for content providers. Inspired by the case for centralizing network-layer control, we present C3, a control system for optimizing Internet video delivery. The design of C3 addresses key challenges in ensuring scalability and tackling data plane heterogeneity. First, to ensure scalability and responsiveness, C3 introduces a novel split control plane architecture that can tolerate a small increase in model staleness for a dramatic increase in scalability. Second, C3 supports diverse client-side platforms via a minimal clientside sensing/actuation layer and offloads complex monitoring and control logic to the control plane. C3 has been operational for eight years, and today handles more than 100M sessions per day from 244 countries for 100+ content providers and has improved the video quality significantly. In doing so, C3 serves as a proof point of the viability of fine-grained centralized control for Internetscale applications. Our experiences reinforce the case for centralizing control with the continued emergence of new use case pulls (e.g., client diversity) and technology pushes (e.g., big data platforms).


title: Inflammation in Alzheimer disease: driving force, bystander or beneficial response?

abstract: Alzheimer disease is a progressive dementia with unknown etiology that affects a growing number of the aging population. Increased expression of inflammatory mediators in postmortem brains of people with Alzheimer disease has been reported, and epidemiological studies link the use of anti-inflammatory drugs with reduced risk for the disorder. On the initial basis of this kind of evidence, inflammation has been proposed as a possible cause or driving force of Alzheimer disease. If true, this could have important implications for the development of new treatments. Alternatively, inflammation could simply be a byproduct of the disease process and may not substantially alter its course. Or components of the inflammatory response might even be beneficial and slow the disease. To address these possibilities, we need to determine whether inflammation in Alzheimer disease is an early event, whether it is genetically linked with the disease and whether manipulation of inflammatory pathways changes the course of the pathology. Although there is still little evidence that inflammation triggers or promotes Alzheimer disease, increasing evidence from mouse models suggests that certain inflammatory mediators are potent drivers of the disease. Related factors, on the other hand, elicit beneficial responses and can reduce disease.


Intercultural Communication


title: E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time

abstract: In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48–72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.


title: Approximate XML Joins

abstract: XML is widely recognized as the data interchange standard for tomorrow, because of its ability to represent data from a wide variety sources. Hence, XML is likely to be the format through which data from multiple sources is integrated.In this paper we study the problem of integrating XML data sources through correlations realized as join operations. A challenging aspect of this operation is the XML document structure. Two documents might convey approximately or exactly the same information but may be quite different in structure. Consequently approximate match in structure, in addition to, content has to be folded in the join operation. We quantify approximate match in structure and content using well defined notions of distance. For structure, we propose computationally inexpensive lower and upper bounds for the tree edit distance metric between two trees. We then show how the tree edit distance, and other metrics that quantify distance between trees, can be incorporated in a join framework. We introduce the notion of reference sets to facilitate this operation. Intuitively, a reference set consists of data elements used to project the data space. We characterize what constitutes a good choice of a reference set and we propose sampling based algorithms to identify them. This gives rise to a variety of algorithmic approaches for the problem, which we formulate and analyze. We demonstrate the practical utility of our solutions using large collections of real and synthetic XML data sets.


TICS 1472 No . of Pages 10 Opinion Mood as Representation of Momentum


title: Development of a five-finger dexterous hand without feedback control: The TUAT/Karlsruhe humanoid hand

abstract: In order to realize performance gain of a robot or an artificial arm, the end-effector which exhibits the same function as human beings and can respond to various objects and environment needs to be realized. Then, we developed the new hand which paid its attention to the structure of human being's hand which realize operation in human-like manipulation (called TUAT/Karlsruhe Humanoid Hand). Since this humanoid hand has the structure of adjusting grasp shape and grasp force automatically, it does not need a touch sensor and feedback control. It is designed for the humanoid robot which has to work autonomously or interactively in cooperation with humans and for an artificial arm for handicapped persons. The ideal end-effectors for such an artificial arm or a humanoid would be able to use the tools and objects that a person uses when working in the same environment. If this humanoid hand can operate the same tools, a machine and furniture, it may be possible to work under the same environment as human beings. As a result of adopting a new function of a palm and the thumb, the robot hand could do the operation which was impossible until now. The humanoid hand realized operations which hold a kitchen knife, grasping a fan, a stick, uses the scissors and uses chopsticks.


title: Prediction and classification of Alzheimer disease based on quantification of MRI deformation

abstract: Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.


title: R.B.: A Buyer’s Guide to Conic Fitting

abstract: In this paper we evaluate several methods of fitting data to conic sections. Conic fitting is a commonly required task in machine vision, but many algorithms perform badly on incomplete or noisy data. We evaluate several algorithms under various noise and degeneracy conditions, identify the key parameters which affect sensitivity, and present the results of comparative experiments which emphasize the algorithms' behaviours under common examples of degenerate data. In addition, complexity analyses in terms of flop counts are provided in order to further inform the choice of algorithm for a specific application.


title: Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients

abstract: Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencingbased analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N. The ISME Journal (2012) 6, 1007–1017; doi:10.1038/ismej.2011.159; published online 1 December 2011 Subject Category: microbial ecology and functional diversity of natural habitats


title: A New Sharing Paradigm for the Personal Cloud

abstract: Pushed by recent legislation and smart disclosure initiatives, personal cloud solutions emerge and hold the promise of giving the control back to the individual on her data. However, this shift leaves the privacy and security issues in user’s hands, a role that few people can properly endorse. Considering the inadequacy of existing sharing models, we advocate the definition of a new sharing paradigm dedicated to the personal cloud context. This sharing paradigm, called SWYSWYK (Share What You See with Who You Know), allows to derive intuitive sharing rules from the personal cloud content, to self-administer the subjects and the sensitive permissions, and to visualize the net effects of the sharing policy on the user’s personal cloud. We then propose a reference architecture providing the users tangible guarantees about the enforcement of the SWYSWYK policies. An instance of this architecture has been implemented on top of an existing personal cloud platform to demonstrate the practicality of the approach.


title: On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

abstract: Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of electroencephalography (EEG)-based biometric recognition. We first compare the characteristics of different stimuli that have been used for evoking biometric information bearing EEG signals. This is followed by a survey of the reported features and classifiers employed for EEG biometric recognition. To highlight the usability challenges of using EEG for biometric recognition in real-life scenarios, we propose a novel usability assessment framework which combines a number of user-related factors to evaluate the reported systems. The evaluation scores indicate a pattern of increasing usability, particularly in recent years, of EEG-based biometric systems as efforts have been made to improve the performance of such systems in realistic application scenarios. We also propose how this framework may be extended to take into account Aging effects as more performance data becomes available.


title: A 0.22-to-2.4V-input fine-grained fully integrated rational buck-boost SC DC-DC converter using algorithmic voltage-feed-in (AVFI) topology achieving 84.1% peak efficiency at 13.2mW/mm2

abstract: Most existing switched-capacitor (SC) DC-DC converters only offer a few voltage conversion ratios (VCRs), leading to significant efficiency fluctuations under wide input/output dynamics (e.g. up to 30% in [1]). Consequently, systematic SC DC-DC converters with fine-grained VCRs (FVCRs) become attractive to achieve high efficiency over a wide operating range. Both the Recursive SC (RSC) [2,3] and Negator-based SC (NSC) [4] topologies offer systematic FVCR generations with high conductance, but their binary-switching nature fundamentally results in considerable parasitic loss. In bulk CMOS, the restriction of using low-parasitic MIM capacitors for high efficiency ultimately limits their achievable power density to <1mW/mm2. This work reports a fully integrated fine-grained buck-boost SC DC-DC converter with 24 VCRs. It features an algorithmic voltage-feed-in (AVFI) topology to systematically generate any arbitrary buck-boost rational ratio with optimal conduction loss while achieving the lowest parasitic loss compared with [2,4]. With 10 main SC cells (MCs) and 10 auxiliary SC cells (ACs) controlled by the proposed reference-selective bootstrapping driver (RSBD) for wide-range efficient buck-boost operations, the AVFI converter in 65nm bulk CMOS achieves a peak efficiency of 84.1% at a power density of 13.2mW/mm2 over a wide range of input (0.22 to 2.4V) and output (0.85 to 1.2V).


title: A survey of power and energy efficient techniques for high performance numerical linear algebra operations

abstract: A thorough survey of energy efficient techniques for linear algebra operations.A complete list of valuable research efforts.A deeply review of all aspects of feasible solutions.Detailed discussion and comprehensive comparison.Thought-provoking directions of future research are provided. Extreme scale supercomputers available before the end of this decade are expected to have 100 million to 1billion computing cores. The power and energy efficiency issue has become one of the primary concerns of extreme scale high performance scientific computing. This paper surveys the research on saving power and energy for numerical linear algebra algorithms in high performance scientific computing on supercomputers around the world. We first stress the significance of numerical linear algebra algorithms in high performance scientific computing nowadays, followed by a background introduction on widely used numerical linear algebra algorithms and software libraries and benchmarks. We summarize commonly deployed power management techniques for reducing power and energy consumption in high performance computing systems by presenting power and energy models and two fundamental types of power management techniques: static and dynamic. Further, we review the research on saving power and energy for high performance numerical linear algebra algorithms from four aspects: profiling, trading off performance, static saving, and dynamic saving, and summarize state-of-the-art techniques for achieving power and energy efficiency in each category individually. Finally, we discuss potential directions of future work and summarize the paper.


title: Lightweight security framework for IoTs using identity based cryptography

abstract: Internet of Things (IoTs) is gaining increasing significance due to real-time communication and decision making capabilities of sensors integrated into everyday objects. Securing IoTs is one of the foremost concerns due to the ubiquitous nature of the sensors coupled with the increasing sensitivity of user data. Further, power-constrained nature of the IoTs emphasizes the need for lightweight security that can tailor to the stringent resource requirements of the sensors. In this work, we propose a lighweight security framework for IoTs using Identity based Cryptography. In particular, we develop a hierarchical security architecture for IoTs and further develop protocols for secure communication in IoTs using identity based cryptography. Our proposed mechanism has been evaluated using simulations conducted using Contiki and RELIC. Evaluation shows that our proposed mechanism is lightweight incurring lesser overhead and thus can be applied in IoTs.


title: Sleep can reduce the testing effect: it enhances recall of restudied items but can leave recall of retrieved items unaffected.

abstract: The testing effect refers to the finding that retrieval practice in comparison to restudy of previously encoded contents can improve memory performance and reduce time-dependent forgetting. Naturally, long retention intervals include both wake and sleep delay, which can influence memory contents differently. In fact, sleep immediately after encoding can induce a mnemonic benefit, stabilizing and strengthening the encoded contents. We investigated in a series of 5 experiments whether sleep influences the testing effect. After initial study of categorized item material (Experiments 1, 2, and 4A), paired associates (Experiment 3), or educational text material (Experiment 4B), subjects were asked to restudy encoded contents or engage in active retrieval practice. A final recall test was conducted after a 12-hr delay that included diurnal wakefulness or nocturnal sleep. The results consistently showed typical testing effects after the wake delay. However, these testing effects were reduced or even eliminated after sleep, because sleep benefited recall of restudied items but left recall of retrieved items unaffected. The findings are consistent with the bifurcation model of the testing effect (Komell, Bjork, & Garcia, 2011), according to which the distribution of memory strengths across items is shifted differentially by retrieving and restudying, with retrieval strengthening items to a much higher degree than restudy does. On the basis of this model, most of the retrieved items already fall above recall threshold in the absence of sleep, so additional sleep-induced strengthening may not improve recall of retrieved items any further. [ABSTRACT FROM AUTHOR]


title: Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain

abstract: Arguably the most common cause of image degradation is compression. This papers presents a novel approach to restoring JPEG-compressed images. The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images. The restoration is a sparse coding process carried out jointy in the DCT and. pixel domains. The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem. Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.


title: MusicGlove: A Wearable Musical Controller for Massive Media Library

abstract: This research aim st o develop aw earablemusical interface which enables to control audio and video signals by using hand gestures and human body motions. We have been developing an audio-visual manipulation system that realizes tracks control, time-based operations and searching for tracks fro mm assive music library. It aim st o build an emotional and a! ecting musical interaction, and will provide ab ettermethod of music listening to people. A sophisticated glove-like device with an acceleration sensor and several strain sensors has been developed. A realtim es ignal processing and musical control are executed as a result of gesture recognition. We also developed a stand-alone device that perform sa s amusical controller and player at the sam et ime. In this paper, we describe the development of a compact and sophisticated sensor device, and demonstrate its performance of audio and video signals control.


An Original Classification of Rehabilitation Hand Exoskeletons


Classifying, Profiling and Predicting User Behavior in the Context of Location Based Services


title: The stable marriage problem

abstract: The original work of Gale and Shapley on an assignment method using the stable marriage criterion has been extended to find all the stable marriage assignments. The algorithm derived for finding all the stable marriage assignments is proved to satisfy all the conditions of the problem. Algorithm 411 applies to this paper.


title: Are Generative Classifiers More Robust to Adversarial Attacks?

abstract: There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers, which only model the conditional distribution of the labels given the inputs. In this paper we propose the deep Bayes classifier, which improves classical naive Bayes with conditional deep generative models. We further develop detection methods for adversarial examples, which reject inputs that have negative log-likelihood under the generative model exceeding a threshold pre-specified using training data. Experimental results suggest that deep Bayes classifiers are more robust than deep discriminative classifiers, and the proposed detection methods achieve high detection rates against many recently proposed attacks.


title: Deformable Model Fitting by Regularized Landmark Mean-Shift

abstract: Deformable model fitting has been actively pursued in the computer vision community for over a decade. As a result, numerous approaches have been proposed with varying degrees of success. A class of approaches that has shown substantial promise is one that makes independent predictions regarding locations of the model's landmarks, which are combined by enforcing a prior over their joint motion. A common theme in innovations to this approach is the replacement of the distribution of probable landmark locations, obtained from each local detector, with simpler parametric forms. In this work, a principled optimization strategy is proposed where nonparametric representations of these likelihoods are maximized within a hierarchy of smoothed estimates. The resulting update equations are reminiscent of mean-shift over the landmarks but with regularization imposed through a global prior over their joint motion. Extensions to handle partial occlusions and reduce computational complexity are also presented. Through numerical experiments, this approach is shown to outperform some common existing methods on the task of generic face fitting.


title: Advancing the state of mobile cloud computing

abstract: The capabilities of mobile devices have been improving very quickly in terms of computing power, storage, feature support, and developed applications. However, these mobile applications are still intrinsically limited by a relative lack of bandwidth, computing power, and energy compared to their tethered counterparts. Cloud computing offers abundant computing power that can be tapped easily. Apple iCloud and Amazon Silk browser are two recent mobile applications that leverage the cloud. In this paper, we systematically explore the fundamental research questions when combining mobile and cloud computing. We will highlight some of the challenges we face and some of the solutions we are pursuing.


title: An Analysis of the Bitcoin Electronic Cash System

abstract: In a world that relies heavily on technology, privacy is sought by many. Privacy, among other things, is especially desired when making an online payment. This motivates the use of electronic cash, a form of electronic payment system based on the paper cash system used daily. The most successful and widely used of these services is Bitcoin – a decentralized peer-to-peer electronic cash system. This paper provides a broad introduction to Bitcoin, while analyzing its construction and investigating some of its perks and flaws. It can be seen that, when compared to paper cash and electronic cash, Bitcoin is in a class of its own.


title: Ambiguity and Constraint in Mathematical Expression Recognition

abstract: The problem of recognizing mathematical expressions differs significantly from the recognition of standard prose. While in prose significant constraints can be put on the interpretation of a character by the characters immediately preceding and following it, few such simple constraints are present in a mathematical expression. In order to make the problem tractable, effective methods of recognizing mathematical expressions will need to put intelligent constraints on the possible interpretations. The authors present preliminary results on a system for the recognition of both handwritten and typeset mathematical expressions. While previous systems perform character recognition out of context, the current system maintains ambiguity of the characters until context can be used to disambiguate the interpretation. In addition, the system limits the number of potentially valid interpretations by decomposing the expressions into a sequence of compatible convex regions. The system uses A-star to search for the best possible interpretation of an expression. We provide a new lower bound estimate on the cost to goal that improves performance significantly.


title: A 60-GHz Dual-Mode Class AB Power Amplifier in 40-nm CMOS

abstract: A 60-GHz dual-mode power amplifier (PA) is implemented in 40-nm bulk CMOS technology. To boost the amplifier performance at millimeter-wave (mmWave) frequencies, a new transistor layout is proposed to minimize the device and interconnect parasitics while the neutralized amplifier stage is co-optimized with input transformer to improve the power gain and stability. The transformer-based power-combining PA consists of two unit amplifiers, operating in Class AB for better back-off efficiency. To further reduce the power consumption and hence extend battery lifetime, one unit PA is tuned off in low-power mode. A switch is used to short the output of this non-operating unit PA to reduce the combiner loss and improve the efficiency. The PA achieves a measured saturated output power (PSAT) of 17.0 dBm (12.1 dBm) and 1-dB compressed power (P1dB) of 13.8 dBm (9.1 dBm) in the high-power (low-power) mode. The power-added efficiencies (PAEs) at PSAT and P1dB are 30.3% and 21.6% respectively for the high-power mode. Compared to Class A, the PA operating in Class AB shows 5.3% improvement in measured PAE at P1dB with no compromise in linearity. The PA with the power combiner only occupies an active area of 0.074 mm 2. The reliability measurements are also conducted and the PA has an estimated lifetime of 80613 hours.


title: Perceived supervisor support: Contributions to perceived organizational support and employee retention.

abstract: Three studies investigated the relationships among employees' perception of supervisor support (PSS), perceived organizational support (POS), and employee turnover. Study I found, with 314 employees drawn from a variety of organizations, that PSS was positively related to temporal change in POS, suggesting that PSS leads to POS. Study 2 established, with 300 retail sales employees, that the PSS-POS relationship increased with perceived supervisor status in the organization. Study 3 found, with 493 retail sales employees, evidence consistent with the view that POS completely mediated a negative relationship between PSS and employee turnover. These studies suggest that supervisors, to the extent that they are identified with the organization, contribute to POS and, ultimately, to job retention.


Measuring treatment process beliefs among staff of specialized addiction treatment services.


title: Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation

abstract: In this work we present a novel method for the challenging problem of depth image up sampling. Modern depth cameras such as Kinect or Time-of-Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem using higher order regularization for depth image up sampling. In this optimization an an isotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the up sampling. We derive a numerical algorithm based on a primal-dual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel up sampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate ground truth, which, for the first time, enable to benchmark depth up sampling methods using real sensor data.


title: A World Championship Caliber Checkers Program

abstract: The checkers program Chinook has won the right to play a 40-game match for the World Checkers Championship against Dr. Marion Tinsley. This was earned by placing second, after Dr. Tinsley, at the 1990 U.S. National Open, the biennial event used to determine a challenger for the Championship. This is the first time a program has earned the right to contest for a human World Championship. In an exhibition match played in December 1990, Tinsley narrowly defeated Chinook 7.5 - 6.5. This paper describes the program, the research problems encountered and our solutions. Many of the techniques used for computer chess are directly applicable to computer checkers. However, the problems of building a world championship caliber program force us to address some issues that have, to date, been largely ignored by the computer chess community.


Active learning with feedback on both features and instances


title: Annoyed Users: Ads and Ad-Block Usage in the Wild

abstract: Content and services which are offered for free on the Internet are primarily monetized through online advertisement. This business model relies on the implicit agreement between content providers and users where viewing ads is the price for the "free" content. This status quo is not acceptable to all users, however, as manifested by the rise of ad-blocking plugins which are available for all popular Web browsers. Indeed, ad-blockers have the potential to substantially disrupt the widely established business model of "free" content, currently one of the core elements on which the Web is built. In this work, we shed light on how users interact with ads. We show how to leverage the functionality of AdBlock Plus, one of the most popular ad-blockers to identify ad traffic from passive network measurements. We complement previous work, which focuses on active measurements, by characterizing ad-traffic in the wild, i.e., as seen in a residential broadband network of a major European ISP. Finally, we assess the prevalence of ad-blockers in this particular network and discuss possible implications for content providers and ISPs.


title: The assessment of goal commitment: A measurement model meta-analysis

abstract: Goals are central to current treatments of work motivation, and goal commitment is a critical construct in understanding the relationship between goals and performance. Inconsistency in the measurement of goal commitment hindered early research in this area but the nine-item, self-report scale developed by Hollenbeck, Williams, and Klein (1989b), and derivatives of that scale, have become the most commonly used measures of goal commitment. Despite this convergence, a few authors, based on small sample studies, have raised questions about the dimensionality of this measure. To address the conflicting recommendations in the literature regarding what items to use in assessing goal commitment, the current study combines the results of 17 independent samples and 2918 subjects to provide a more conclusive assessment by combining meta-analytic and multisample confirmatory factor analytic techniques. This effort reflects the first combined use of these techniques to test a measurement model and allowed for the creation of a database substantially larger than that of previously factor analyzed samples containing these scale items. By mitigating sampling error, the results clarified a number of debated issues that have arisen out of previous small sample factor analyses and revealed a five-item scale that is unidimensional and equivalent across measurement timing, goal origin, and task complexity. It is recommended that this five-item scale be used in future research assessing goal commitment.


The affect heuristic


title: Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization

abstract: The Matrix-Factorization (MF) based models have become popular when building Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability. However, most of the current MF based models are batch models that are incapable of being incrementally updated; while in real world applications users always enjoy receiving quick responses from the system once they have made feedbacks. In this work, we aim to design an incremental CF recommender based on the Regularized Matrix Factorization (RMF). To achieve this objective, we first simplify the training rule of RMF to propose the SI-RMF, which provides a simple mathematic form for further investigation; whereby we design two Incremental RMF models, respectively are the Incremental RMF (IRMF) and the Incremental RMF with linear biases (IRMF-B). The experiments on two large, real datasets suggest positive results, which prove the efficiency of our strategy.


title: A characterization of the single-peaked single-crossing domain

abstract: We investigate elections that are simultaneously single-peaked and single-crossing (SPSC). We show that the domain of 1-dimensional Euclidean elections (where voters and candidates are points on the real line, and each voter prefers the candidates that are close to her to the ones that are further away) is a proper subdomain of the SPSC domain, by constructing an election that is single-peaked and single-crossing, but not 1-Euclidean. We then establish a connection between narcissistic elections (where each candidate is ranked first by at least one voter), single-peaked elections and single-crossing elections, by showing that an election is SPSC if and only if it can be obtained from a narcissistic single-crossing election by deleting voters. We show two applications of our characterization.


title: Theoretical reflections on agile development methodologies

abstract: The traditional goal of optimization and control is making way for learning and innovation.


title: Simulation of fuzzy logic control for DC servo motor using Arduino based on MATLAB/Simulink

abstract: The most widely used control strategy in industry is proportional integral derivative (PID) controller. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity. One of the application is to control arm robot manipulator model by using DC servo motor as actuator. This paper presents design of PID controller of DC servo motor using automated PID tuning by sisotool for higher order system and implement to the Arduino Mega 2560 via potentiometer by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink. A better design of controller using fuzzy logic controller (FLC) is proposed. Simulation results are demonstrated. Performance analysis shows the effectiveness of the proposed Fuzzy logic controller as compared to the PID controller.


title: Hierarchical power management for asymmetric multi-core in dark silicon era

abstract: Asymmetric multi-core architectures integrating cores with diverse power-performance characteristics is emerging as a promising alternative in the dark silicon era where only a fraction of the cores on chip can be powered on due to thermal limits. We introduce a hierarchical power management framework for asymmetric multi-cores that builds on control theory and coordinates multiple controllers in a synergistic manner to achieve optimal power-performance efficiency while respecting the thermal design power budget. We integrate our framework within Linux and implement/evaluate it on real ARM big.LITTLE asymmetric multi-core platform.


title: Universal Principles of Depicting Oneself across the Centuries: From Renaissance Self-Portraits to Selfie-Photographs

abstract: Selfie-photography is generally thought of as a cultural mass phenomenon of the early 21st century, inseparably related to the development and triumph of the smartphone with integrated camera. Western culture, however, has been highly familiar with self-depictions since the Renaissance days. Putting the contemporary selfie into this historic context covering more than five centuries of cultural development from Durer’s (1500) famous “Self-Portrait at 28” (also known as “Selbstbildnis im Pelzrock”) to today’s Instagram galleries allows for identifying central parallels concerning the technical and social antecedents as well as common underlying psychological factors and shared properties of different kinds of self-depiction. The article provides an overview of the types of contemporary photographic selfies and compares them with painted self-portraits. Finally, this historic perspective leads us to the insight that self-portraits as well as selfies are both referring to nothing less than the “conditio humana”.


title: Dynamic path selection in 5G multi-RAT wireless networks

abstract: Emerging 5G networks will not only offer higher link rates, but also integrate a variety of Radio Access Technologies (RATs) in order to provide ultra-reliable broadband access to a wide range of applications with high throughput and low latency requirements. SDN-enabled dynamic path selection is of critical importance in exploiting the collective transmission resources in such heterogeneous multi-RAT environments and delivering excellent user performance. In the present paper we propose the ‘best-rate’ path selection algorithm for multi-RAT networks with various types of traffic flows. The best-rate algorithm accounts for the radio conditions and performance requirements of individual flows as well as the load conditions at the various access points. We analytically establish that the rates received by the various flows under the best-rate path selection, in conjunction with local fair resource sharing at the individual access points, are close to globally Proportional Fair. Detailed simulation experiments demonstrate that the best-rate algorithm achieves significant gains in terms of user-perceived throughput performance over various baseline policies.


title: On the Theory of Dynamic Programming

abstract: Abstract : The paper is the text of an invited address before the annual summer meeting of the American Mathematical Society at Laramie, Wyoming, September 2, 1954. The contents are chiefly of an expository nature on the theory of dynamic programming.


title: The Impact of Organizational Culture on Agile Method Use

abstract: Agile method proponents believe that organizational culture has an effect on the extent to which an agile method is used. Research into the relationship between organizational culture and information systems development methodology deployment has been explored by others using the Competing Values Framework (CVF). However this relationship has not been explored with respect to the agile development methodologies. Based on a multi-case study of nine projects we show that specific organizational culture factors correlate with effective use of an agile method. Our results contribute to the literature on organizational culture and system development methodology use.


title: Low-rate LDPC codes with simple protograph structure

abstract: This paper provides a construction method for low-rate low density parity check codes. Inspired by recently proposed accumulate-repeat-accumulate (ARA) codes, and hybrid concatenated codes, in this paper we extend the construction to low rates. Such codes can be viewed as hybrid concatenations of simple modules such as accumulators, repetition codes, differentiators, and punctured single parity check codes. These codes constitute a subclass of LDPC codes with very fast encoder structure. They also have a projected graph or protograph representation that allows for high-speed decoder implementation. Based on density evolution, we show through some examples that low iterative decoding thresholds close to the channel capacity limits can be achieved, as the block size goes to infinity. Iterative decoding simulation results for short blocks are provided for a few examples that show near-capacity performance and very low error floor


Requirements for Elliptic Curves for High-Assurance Applications


title: Classification of Age Groups Based on Facial Features

abstract: An age group classification system for gray-scale facial images is proposed in this paper. Four age groups, including babies, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into three phases: location, feature extraction, and age classification. Based on the symmetry of human faces and the variation of gray levels, the positions of eyes, noses, and mouths could be located by applying the Sobel edge operator and region labeling. Two geometric features and three wrinkle features from a facial image are then obtained. Finally, two back-propagation neural networks are constructed for classification. The first one employs the geometric features to distinguish whether the facial image is a baby. If it is not, then the second network uses the wrinkle features to classify the image into one of three adult groups. The proposed system is experimented with 230 facial images on a Pentium II 350 processor with 128 MB RAM. One half of the images are used for training and the other half for test. It takes 0.235 second to classify an image on an average. The identification rate achieves 90.52% for the training images and 81.58% for the test images, which is roughly close to human’s subjective justification.


title: Direct Current Control of a Synchronous Machine in Field Coordinates

abstract: This paper presents an implementation of a modified method for direct current control (DCC) in a permanent-magnet synchronous motor (SM). The control is performed using a predictive algorithm in rotor field coordinates due to its simpler implementation. Its task is to minimize the final current error at the end of the sampling interval. Depending on the desired performance, two variants are proposed, yielding either lower switching frequency or lower current ripple. Both variants of DCC, named DCC I and DCC II, have been rearranged for applications in SMs with either surface-mounted permanent magnets or interior (buried) permanent magnets. A comparison with field-oriented control using space-vector modulation and synchronized on/off modulation shows the advantages and drawbacks of the proposed method. Simulations and measurements on a laboratory model with SM having low inductances (thus expecting high ripple) and very high number of poles (high stator supply frequency) confirm the validity of the approach. Additional considerations regarding practical problems, some of which can be found in similar predictive methods, are also presented.


title: High-fidelity joint drive system by torque feedback control using high precision linear encoder

abstract: When robots cooperate with humans it is necessary for robots to move safely on sudden impact. Joint torque sensing is vital for robots to realize safe behavior and enhance physical performance. Firstly, this paper describes a new torque sensor with linear encoders which demonstrates electro magnetic noise immunity and is unaffected temperature changes. Secondly, we propose a friction compensation method using a disturbance observer to improve the positioning accuracy. In addition, we describe a torque feedback control method which scales down the motor inertia and enhances the joint flexibility. Experimental results of the proposed controller are presented.


title: Verbal working memory and sentence comprehension

abstract: This target article discusses the verbal working memory system used in sentence comprehension. We review the concept of working memory as a short-duration system in which small amounts of information are simultaneously stored and manipulated in the service of accomplishing a task. We summarize the argument that syntactic processing in sentence comprehension requires such a stor- age and computational system. We then ask whether the working memory system used in syntactic processing is the same as that used in verbally mediated tasks that involve conscious controlled processing. Evidence is brought to bear from various sources: the relation- ship between individual differences in working memory and individual differences in the efficiency of syntactic processing; the effect of concurrent verbal memory load on syntactic processing; and syntactic processing in patients with poor short-term memory, patients with poor working memory, and patients with aphasia. Experimental results from these normal subjects and patients with various brain le- sions converge on the conclusion that there is a specialization in the verbal working memory system for assigning the syntactic structure of a sentence and using that structure in determining sentence meaning that is separate from the working memory system underlying the use of sentence meaning to accomplish other functions. We present a theory of the divisions of the verbal working memory system and suggestions regarding its neural basis.


title: Insulation Failure Mechanisms of Power Generators

abstract: Several statistics have been published dealing with failure causes of high voltage rotating machines in general and power generators in particular [1 4]. Some of these statistics only specify the part of the machine which failed without giving any deeper insight in the failure mechanism. Other publications distinguish between the damage which caused the machine to fail and the root cause which effected the damage. The survey of 1199 hydrogenerators carried out by the CIGRE study committee SC11, EG11.02 provides an example of such an investigation [5]. It gives detailed results of 69 incidents. 56% of the failed machines showed an insulation damage, other major types being mechanical, thermal and bearing damages (Figure 1a). Root causes which led to these damages are subdivided into 7 different groups (Figure 1b).


title: Background Subtraction Based on a Robust Consensus Method

abstract: Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance


title: Academic and Educational Outcomes of Children With ADHD

abstract: Attention-deficit/hyperactivity disorder (ADHD) is associated with poor grades, poor reading and math standardized test scores, and increased grade retention. ADHD is also associated with increased use of school-based services, increased rates of detention and expulsion, and ultimately with relatively low rates of high school graduation and postsecondary education. Children in community samples who show symptoms of inattention, hyperactivity, and impulsivity with or without formal diagnoses of ADHD also show poor academic and educational outcomes. Pharmacologic treatment and behavior management are associated with reduction of the core symptoms of ADHD and increased academic productivity, but not with improved standardized test scores or ultimate educational attainment. Future research must use conceptually based outcome measures in prospective, longitudinal, and community-based studies to determine which pharmacologic, behavioral, and educational interventions can improve academic and educational outcomes of children with ADHD.


title: Building an Encrypted and Searchable Audit Log

abstract: Audit logs are an important part of any secure system, and they need to be carefully designed in order to give a faithful representation of past system activity. This is especially true in the presence of adversaries who might want to tamper with the audit logs. While it is important that auditors can inspect audit logs to assess past system activity, the content of an audit log may contain sensitive information, and should therefore be protected from unauthorized


Selection , Evaluation , Testing , Integration and Implementation of Commercial-Off-The-Shelf ( COTS ) components in Software


title: Basic Erosion Processes of Oxidized and Clean Metal Cathodes by Electric Arcs

abstract: This paper reviews the considerable advances which have been made in recent years in understanding the basic processes involved in erosion of metal cathodes of electric arcs. These advances have been due to the use of scanning electron microscopy (SEM) which has revealed craters of 0.1-?m diameter, the use of arc durations down to a few nanoseconds, and recognition of the importance of oxide film on the cathode surface and control of this in experiments. The paper covers atmospheric pressure and vacuum arcs and clears up most of the confusion about such parameters as cathode emitting site current densities and lifetimes, at least for copper cathodes and to an extent for some others.


title: Grasping Objects: The Relationship Between the Cage and the Form-Closure Grasp

abstract: In various industries, there is a need for low-cost, flexible robotic grasping systems that handle and classify different items, which usually involve low-cost grippers and reliable manipulations. One-degree-offreedom (1-DoF) two-pin or three-pin grippers are widely used in the automation of manufacturing processes because of their low cost and high reliability. However, some uncertainties introduced by the object and the gripper may affect the robustness of the grasp. The principle of caging enables us to deal with these uncertainties, and it is therefore important to study caging as a practical solution to improve the grasps of 1-DoF industrial grippers.


title: What is biological motion?: Definition, stimuli and paradigms

abstract: The study of biological motion perception was introduced into vision research some 40 years ago by Swedish psychologist Gunnar Johansson. Since that time, the questions and approaches have changed considerably. While early work on biological motion perception was primarily concerned with mechanism of perceptual organization, the more recent research emphasizes on the social significance of human and animal motion. In tracing the evolution of the term “biological motion” and its use, this chapter attempts to characterize and classify the different questions and experimental paradigms that have dominated research on biological motion perception over the last few decades. Gunnar Johansson's biological motion: structure from non-rigid motion The term “biological motion” is closely associated with the work of Gunnar Johansson (1911 – 1998), an experimental psychologist who received his PhD from the University of Stockholm and then taught and conducted research at the University of Uppsala, Sweden, for most of his career. Hardly any study on biological motion is published without a reference to at least one of the two papers which Johansson published on his observation that observers can effortlessly make out a human figure behind a degraded visual stimulus that consists of nothing but a few dots moving along with the major joints of a human body (Johansson, 1973; 1976). Gunnar Johansson was an extremely prolific researcher, was well connected, and had strong ties to many of the personalities that had shaped European post-war experimental psychology. In addition to the two papers on the perception of human motion, he published almost 100 papers over the course of his career. He was strongly influenced by the school of Gestalt Psychology. Wolfgang Kohler had visited him several times in Uppsala and the two had planned collaborative work shortly before Kohler's unexpected death in 1967. Johansson also maintained a long academic relationship with James J. Gibson. With Gibson he shared an interest in the question how the visual system samples and then extracts information about discrete objects and events from the ambient optic array (i.e., the everchanging, continuous stream of light irradiating the location of the observer from all directions). Particularly, the two friends discussed (and sometimes argued about) the way the visual system distinguishes between the visual motion induced by the observer himself while moving through the environment, and the motion of objects, animals, and people relative to the visual environment. Johansson's approach to this question was his “vector analysis” theory which he had first laid out in his doctoral thesis (Johansson, 1950). The theory was rooted in Gestalt theory, specifically in Duncker's work on induced motion (Duncker, 1928), and it was based on Johansson's own experimental work for his doctoral thesis under the supervision of Gestalt psychologist David Katz at Stockholm University. Johansson's experiments had set out to demonstrate how the human visual system would break down the motion of simple objects into “common” motion encoded in a global reference system, and “relative” motion represented within an object-based reference system. Common motion of the entire visual field with respect to the observer would be indicative of the observer’s motion, while motion relative to the global, allocentric reference system has the potential to characterize objects – both in terms of motion parallax induced by the observer and in terms of the object's own motion. On the object level itself, the same distinction seemed to make sense, too. The motion common to all the parts of an object describes its trajectory through space, while the relative motion between the parts bears information about changes of orientation or the configuration of the internal structure of a deformable


title: Treatment effects and short-term relapse of maxillomandibular expansion during the early to mid mixed dentition.

abstract: INTRODUCTION ::: The treatment effects and the short-term (0.9 +/- 0.45 years) relapse potential of phase I slow maxillary expansion, with a bonded palatal expander or a quad-helix appliance combined with a mandibular banded Crozat/lip bumper and followed by 12 to 15 months of retention, were examined. ::: ::: ::: METHODS ::: Pretreatment (8.8 +/- 1.7 years) and posttreatment (11.1 +/- 1.7 years) models of 54 patients were used to evaluate treatment effects. Posttreatment (11.0 +/- 1.3 years) and follow-up (11.9 +/- 1.4 years) models of 23 patients who returned for phase II treatment were used to evaluate relapse over the 11 months, during which no retention was used. The models were digitized, and 15 measures were computed. ::: ::: ::: RESULTS ::: Significant treatment increases were observed for all measurements in both arches. Treatment gains in arch perimeter (6%-8%) were due more to increases in intermolar width (11%-15%) than to increases in arch depth (5%). Posttreatment relapse was significant (P <.05) for all measures except mandibular intercanine width and maxillary molar arch depth. After accounting for normal growth, net changes (pretreatment to follow-up) indicated significant increases for all measures except maxillary molar arch depth. In addition to maintaining leeway space, the maxilla and the mandible showed net perimeter gains of 2.9 and 1.0 mm, respectively. ::: ::: ::: CONCLUSIONS ::: Slow maxillary expansion combined with a mandibular banded Crozat/lip bumper during the early mixed dentition produced clinically useful increases in arch dimensions that subsequently underwent mild-to-moderate amounts of relapse after removal of all retention appliances.


title: Bayesian Model Averaging: A Tutorial

abstract: Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident in- ferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of- sample predictive performance. We also provide a catalogue of currently available BMA software.


title: Robust heart rate estimation using wrist-based PPG signals in the presence of intense physical activities

abstract: Heart rate tracking from a wrist-type photoplethysmogram (PPG) signal during intensive physical activities is a challenge that is attracting more attention thanks to the introduction of wrist-worn wearable computers. Commonly-used motion artifact rejection methods coupled with simple periodicity-based heart rate estimation techniques are incapable of achieving satisfactory heart rate tracking performance during intense activities. In this paper, we propose a two-stage solution. Firstly, we introduce an improved spectral subtraction method to reject the spectral components of motion artifacts. Secondly, instead of using heuristic mechanisms, we formalize the spectral peaks selection process as the shortest path search problem and validate its effectiveness. Analysis on the experimental results based on a published database shows that: (1) Our proposed method outperforms three other comparable methods with regards to heart rate estimation error. (2) The proposed method is a promising candidate for both offline cardiac health analysis and online heart rate tracking in daily life, even during intensive physical motions.


title: Temporal difference models describe higher-order learning in humans

abstract: The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning1. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning2, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.


title: A Demonstration of Sterling: A Privacy-Preserving Data Marketplace

abstract: In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using privacy-preserving smart contracts which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned. ::: ::: For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.


title: D-PAGE: Diverse Paraphrase Generation

abstract: In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.


title: MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors

abstract: Radar imaging is a competitive option for smart city applications over optical approaches, as it raises no privacy concerns. The inherent difficulty of interpreting radar signals can be overcome using deep learning techniques to leverage the capabilities of monitoring sensors with a minimum of human intervention. In this paper, we use a modified convolutional neural network (CNN) for classifying radar images in order to detect vacant parking spaces with a 77-GHz imaging radar. Although training CNNs for radar-image classification is challenging due to poor generalization performance caused by the lack of labeled training data, the modified architecture takes into account the properties of the radar image in order to introduce prior information into the model and improve performance. A MIMO-FMCW radar is utilized to render a slant-range image of a parking scenario, and the image patches corresponding to each parking location are classified independently in the CNN. Since the radiation pattern of a MIMO array varies as a function of the scanning angle, the corresponding spatial coordinate of each patch is included as an additional feature in the upper layers of the network. This allows the model to combine local features from each patch with global scenario information in order to learn robust features that generalize properly to new scenarios. Several models are trained end to end with data from four different parking scenarios and evaluated in a 4-fold cross-validation scheme, and performance is improved when spatial prior information is included.


title: BP: Profiling Vulnerabilities on the Attack Surface

abstract: Security practitioners use the attack surface of software systems to prioritize areas of systems to test and analyze. To date, approaches for predicting which code artifacts are vulnerable have utilized a binary classification of code as vulnerable or not vulnerable. To better understand the strengths and weaknesses of vulnerability prediction approaches, vulnerability datasets with classification and severity data are needed. The goal of this paper is to help researchers and practitioners make security effort prioritization decisions by evaluating which classifications and severities of vulnerabilities are on an attack surface approximated using crash dump stack traces. In this work, we use crash dump stack traces to approximate the attack surface of Mozilla Firefox. We then generate a dataset of 271 vulnerable files in Firefox, classified using the Common Weakness Enumeration (CWE) system. We use these files as an oracle for the evaluation of the attack surface generated using crash data. In the Firefox vulnerability dataset, 14 different classifications of vulnerabilities appeared at least once. In our study, 85.3% of vulnerable files were on the attack surface generated using crash data. We found no difference between the severity of vulnerabilities found on the attack surface generated using crash data and vulnerabilities not occurring on the attack surface. Additionally, we discuss lessons learned during the development of this vulnerability dataset.


title: Comparing Human and Automatic Face Recognition Performance

abstract: Face recognition technologies have seen dramatic improvements in performance over the past decade, and such systems are now widely used for security and commercial applications. Since recognizing faces is a task that humans are understood to be very good at, it is common to want to compare automatic face recognition (AFR) and human face recognition (HFR) in terms of biometric performance. This paper addresses this question by: 1) conducting verification tests on volunteers (HFR) and commercial AFR systems and 2) developing statistical methods to support comparison of the performance of different biometric systems. HFR was tested by presenting face-image pairs and asking subjects to classify them on a scale of ldquoSame,rdquo ldquoProbably Same,rdquo ldquoNot sure,rdquo ldquoProbably Different,rdquo and ldquoDifferentrdquo; the same image pairs were presented to AFR systems, and the biometric match score was measured. To evaluate these results, two new statistical evaluation techniques are developed. The first is a new way to normalize match-score distributions, where a normalized match score is calculated as a function of the angle from a representation of [false match rate, false nonmatch rate] values in polar coordinates from some center. Using this normalization, we develop a second methodology to calculate an average detection error tradeoff (DET) curve and show that this method is equivalent to direct averaging of DET data along each angle from the center. This procedure is then applied to compare the performance of the best AFR algorithms available to us in the years 1999, 2001, 2003, 2005, and 2006, in comparison to human scores. Results show that algorithms have dramatically improved in performance over that time. In comparison to the performance of the best AFR system of 2006, 29.2% of human subjects performed better, while 37.5% performed worse.


title: A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

abstract: Tying suture knots is a time-consuming task performed frequently during minimally invasive surgery (MIS). Automating this task could greatly reduce total surgery time for patients. Current solutions to this problem replay manually programmed trajectories, but a more general and robust approach is to use supervised machine learning to smooth surgeon-given training trajectories and generalize from them. Since knottying generally requires a controller with internal memory to distinguish between identical inputs that require different actions at different points along a trajectory, it would be impossible to teach the system using traditional feedforward neural nets or support vector machines. Instead we exploit more powerful, recurrent neural networks (RNNs) with adaptive internal states. Results obtained using LSTM RNNs trained by the recent Evolino algorithm show that this approach can significantly increase the efficiency of suture knot tying in MIS over preprogrammed control


title: Urdu noun phrase chunking: HMM based approach

abstract: Extraction of noun phrase (NP) from text is useful for many natural language processing applications, such as name entity recognition, indexing, searching, parsing etc. We present a noun phrase chunker for Urdu which is based on a statistical approach. A 100,000 words Urdu corpus is manually tagged with NP chunk tags. The corpus is used to develop a statistical approach. Initially, a statistical approach based on standard HMM model is developed for automatics NP chunking. In Urdu phrases, the case marker (CM) indicates the end of a noun phrase and is appended at its end. Thus, if one scans the sentence in reverse order, one may be able to better predict phrase endings. So, the technique is enhanced by changing scanning direction. The technique is further enhanced by merging chunk and POS tags to achieve maximum accuracy. The results of all experiments are reported with maximum overall accuracy of 97.61% achieved using HMM based approach with extended tagset and right to left (RTL) scanning.


title: To see, or not to see— is That the query?

abstract: An automatic transmission having a torque converter comprising an impeller connected to the crankshaft of an internal combustion engine and a turbine driven by the impeller by oil, an automatic transmission apparatus comprising a planetary gear device and a fluid operated brake and clutch devices connected to an output shaft of the turbine, and a final reduction gear device connected to an output shaft of the automatic transmission apparatus. An overdrive device comprising an overdrive clutch and overdrive gears is connected to the output shaft of the turbine. An intermediate shaft is connected between an output shaft of the overdrive gears and the final reduction gear device, and a valve is provided for applying pressure oil to the overdrive clutch for engagement thereof.


title: Deep predictive policy training using reinforcement learning

abstract: Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.


SCAN: Structure Correcting Adversarial Network for Chest X-rays Organ Segmentation


title: Review of constraints-based motion retargeting techniques in 3D animations

abstract: Constraints-based motion retargeting was one of the motion retargeting techniques. With it, a collection of constraints were set when the 3D motion data were reused to object models during animating. The existing main techniques of constraints-based motion retargeting are analyzed and compared first, and then the problems are proposed to provide reference for further researches.


title: T-DesP: Destination Prediction Based on Big Trajectory Data

abstract: Destination prediction is very important in location-based services such as recommendation of targeted advertising location. Most current approaches always predict destination according to existing trip based on history trajectories. However, no existing work has considered the difference between the effects of passing-by locations and the destination in history trajectories, which seriously impacts the accuracy of predicted results as the destination can indicate the purpose of traveling. Meanwhile, the temporal information of history trajectories in destination prediction plays an important role. On one hand, the history trajectories in different periods also differ in the influence, e.g., the history trajectories from last week can reflect the status quo more accurately than the history trajectories two years ago. On the other hand, the history trajectories in different time slots reflect different facts of traffic and moving habits of people, e.g., visiting a restaurant in the daytime and visiting a bar at night. Although a huge amount of history trajectories can be achieved in the era of big data, it is still far from covering all the query trajectories since a road network is widely distributed and trajectory data is sparse. The temporal sensitivity of history trajectories highlights the sparsity problem even more. Therefore, we propose a novel model $\text{T-DesP}$ to solve the aforementioned problems. The model is comprised of two modules: trajectory learning and destination prediction. In the module of trajectory learning, a novel method called the mirror absorbing Markov chain model is proposed for modeling the trajectories for isolating the destination. We build a transition tensor to deduce the transition probability between each location pair in a particular time slot. To address the data sparsity problem, we fill the missing values in transition tensor through a context-aware tensor decomposition approach. In the module of destination prediction, an absorbing tensor is derived from the filled transition tensor, and the theoretical model is established for destination prediction. The experiments prove the effectiveness and efficiency of $\text{T-DesP}$ .


title: Ground Reflection Removal in Compressive Sensing Ground Penetrating Radars

abstract: Recent results in compressive sensing (CS)-based subsurface imaging showed that, if the target space is sparse, it can be reconstructed with many fewer number of measurements from a stepped frequency ground penetrating radar (GPR). One of the problems in this CS subsurface imaging is the surface reflections. Previous work dealed with surface reflections using a model dictionary generated from the target space excluding specifically the near surface region. While this works fine for some applications, it might lack the imaging of near surface targets. Removing the surface reflections with standard methods is not directly applicable since only very few and random measurements in the frequency domain are taken. This letter provides a simple surface reflection method using compressive measurements, that can be used for nonplanar surfaces. It is observed in both simulated and experimental GPR data that the CS-based imaging method is more robust and can find shallow targets using the surface-reflection-removed data.


title: Two-Stage Method for Large-Scale Acquisition of Contradiction Pattern Pairs using Entailment

abstract: In this paper we propose a two-stage method to acquire contradiction relations between typed lexico-syntactic patterns such as Xdrug prevents Ydisease and Ydisease caused by Xdrug. In the first stage, we train an SVM classifier to detect contradiction pattern pairs in a large web archive by exploiting the excitation polarity (Hashimoto et al., 2012) of the patterns. In the second stage, we enlarge the first stage classifier’s training data with new contradiction pairs obtained by combining the output of the first stage’s classifier and that of an entailment classifier. We acquired this way 750,000 typed Japanese contradiction pattern pairs with an estimated precision of 80%. We plan to release this resource to the NLP community.


title: Feature binding , attention and object perception

abstract: The seemingly effortless ability to perceive meaningful objects in an integrated scene actually depends on complex visual processes. The 'binding problem' concerns the way in which we select and integrate the separate features of objects in the correct combinations. Experiments suggest that attention plays a central role in solving this problem. Some neurological patients show a dramatic breakdown in the ability to see several objects; their deficits suggest a role for the parietal cortex in the binding process. However, indirect measures of priming and interference suggest that more information may be implicitly available than we can consciously access.


title: Providing Research Graph Data in JSON-LD Using Schema.org

abstract: In this position paper, we describe a pilot project that provides Research Graph records to external web services using JSON-LD. The Research Graph database contains a large-scale graph that links research datasets (i.e., data used to support research) to funding records (i.e. grants), publications and researcher records such as ORCID profiles. This database was derived from the work of the Research Data Alliance Working Group on Data Description Registry Interoperability (DDRI), and curated using the Research Data Switchboard open source software. By being available in Linked Data format, the Research Graph database is more accessible to third-party web services over the Internet, which thus opens the opportunity to connect to the rest of the world in the semantic format. ::: ::: The primary purpose of this pilot project is to evaluate the feasibility of converting registry objects in Research Graph to JSON-LD by accessing widely used vocabularies published at Schema.org. In this paper, we provide examples of publications, datasets and grants from international research institutions such as CERN INSPIREHEP, National Computational Infrastructure (NCI) in Australia, and Australian Research Council (ARC). Furthermore, we show how these Research Graph records are made semantically available as Linked Data through using Schema.org. The mapping between Research Graph schema and Schema.org is available on GitHub repository. We also discuss the potential need for an extension to Schema.org vocabulary for scholarly communication.


title: Triplet Probabilistic Embedding for Face Verification and Clustering

abstract: Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding step, learned using triplet probability constraints to address the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering. Experiments on the challenging IJB-A dataset show that the proposed algorithm performs close to the state of the art methods in verification and identification metrics, while requiring much less training data and training/test time. The superior performance of the proposed method on the CFP dataset shows that the representation learned by our deep CNN is robust to large pose variation. Furthermore, we demonstrate the robustness of deep features to challenges including age, pose, blur and clutter by performing simple clustering experiments on both IJB-A and LFW datasets.


title: Design, development, dynamic analysis and control of a 2-DOF spherical parallel mechanism

abstract: This paper investigates the design, dynamics, and control of a two Degrees-of-Freedom (DOFs) spherical Parallel Mechanism (PM) that was first built at Laval university. The geometric and inertial parameters of the model are obtained via a CAD software in order to reduce the weight and also to avoid the interference between the two limbs movements. The dynamic analysis used in this paper is based on a judicious concept in detaching the whole mechanism into several subsystems and consecutive synergies between kinematic analysis, Lagrangian and Newtonian approaches. The proposed approach performance for the dynamics of this mechanism is analyzed by means of several test trajectories for the end-effector and the results are put into contrast by the ones obtained with a dynamic analyser software. The results obtained by both approaches are coherent which affirms the correctness of the proposed algorithm. Finally, a co-simulation between MATLAB and MD-Adams has been accomplished in order to control this mechanism via computed torque method. The latter method has led the end-effector to follow the desired trajectory.


title: Unsupervised learning of quaternion features for image classification

abstract: Unsupervised feature learning is a very popular trend in image classification. Most of the methods for unsupervised feature learning produces filters which operate either on intensity or color information. In this paper we propose a quaternion-based approach for unsupervised feature learning which makes possible joint encoding of the intensity and color information. The image representation is computed using quaternion principal component analysis and k-means clustering. We experimentally show that our approach outperforms the existing approach for unsupervised feature learning from color images, achieving classification accuracy of 91% on a dataset of remote sensing images.


title: Compressed sensing : theory and applications

abstract: Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann and Guoshen Yu; 3. Xampling: compressed sensing of analog signals Moshe Mishali and Yonina C. Eldar; 4. Sampling at the rate of innovation: theory and applications Jose Antonia Uriguen, Yonina C. Eldar, Pier Luigi Dragotta and Zvika Ben-Haim; 5. Introduction to the non-asymptotic analysis of random matrices Roman Vershynin; 6. Adaptive sensing for sparse recovery Jarvis Haupt and Robert Nowak; 7. Fundamental thresholds in compressed sensing: a high-dimensional geometry approach Weiyu Xu and Babak Hassibi; 8. Greedy algorithms for compressed sensing Thomas Blumensath, Michael E. Davies and Gabriel Rilling; 9. Graphical models concepts in compressed sensing Andrea Montanari; 10. Finding needles in compressed haystacks Robert Calderbank, Sina Jafarpour and Jeremy Kent; 11. Data separation by sparse representations Gitta Kutyniok; 12. Face recognition by sparse representation Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma and John Wright.


title: Interpretable Multimodal Retrieval for Fashion Products

abstract: Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of our work originates from the development of an EI (Exclusive & Independent) tree that can cooperate with deep models for end-to-end multimodal learning. EI tree organizes the fashion concepts into multiple semantic levels and augments the tree structure with exclusive as well as independent constraints. It describes the different relationships among sibling concepts and guides the end-to-end learning of multi-level fashion semantics. From EI tree, we learn an explicit hierarchical similarity function to characterize the semantic similarities among fashion products. It facilitates the interpretable retrieval scheme that can integrate the concept-level feedback. Experiment results on two large fashion datasets show that the proposed approach can characterize the semantic similarities among fashion items accurately and capture user's search intent precisely, leading to more accurate search results as compared to the state-of-the-art methods.


title: Wavelet Network Model and Its Application to the Prediction of Hydrology

abstract: Based on the multi-time scale and the nonlinear characteristics of the observed time series, a new hybrid model between wavelet analysis and artificial neural network (ANN): wavelet network model, has been suggested. The present model absorbs some merits of wavelet transform and artificial neural network. Case studies, the short and long term prediction of hydrological time series, have been researched. The comparison results revealed that the suggested model could increase the forecasted accuracy and prolong the length time of prediction. The wavelet network model is satisfied. (Nature and Science 2003;1(1):67-71).


title: Financial distress model prediction using SVM+

abstract: Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost.


title: Book recommendation system using opinion mining technique

abstract: With the changing trends in technologies, and brisk growth in Internet, daily life of an individual has also changed at a very fast pace. The impacts of these technologies are so diverse that it has affected almost every sphere of the life. People start using applications of the Internet in their daily life; they prefer online shopping for their needs more and more. For academician, researchers and students, purchasing the desired book from huge collections of books on the Internet is very tedious work. In this paper, we presented a recommendation technique based on opinion mining to propose top ranked books on different discipline of the computer science. Based on the need of the customers and the reviews collected from them, we have categorized features for the books. We analyze the features on the basis of several characteristics that we have categorized and reviews of the users. Weights are assigned to categorized features according to their importance and usage, and accordingly the ranks are given. Finally, top ten ranked books are listed. This method is expected to be helpful for millions of the users who seek for desired books.


title: An Overview of the Security Concerns in Enterprise Cloud Computing

abstract: Deploying cloud computing in an enterprise infrastructure bring significant security concerns. Successful implementation of cloud computing in an enterprise requires proper planning and understanding of emerging risks, threats, vulnerabilities, and possible countermeasures. We believe enterprise should analyze the company/organization security risks, threats, and available countermeasures before adopting this technology. In this paper, we have discussed security risks and concerns in cloud computing and enlightened steps that an enterprise can take to reduce security risks and protect their resources. We have also explained cloud computing strengths/benefits, weaknesses, and applicable areas in information risk management.


title: Dietary Energy Intake at the Age of 4 Months Predicts Postnatal Weight Gain and Childhood Body Mass Index

abstract: OBJECTIVE. Rapid infant weight gain has been shown to predict later obesity risk; however, it is unclear which factors influence infant diet and weight gain. The objective of this study was to determine whether different feeding patterns and energy intakes that are provided to infants affect body weight and BMI later in childhood. METHODS. This representative birth cohort study was conducted in the United Kingdom. Energy intake at age 4 months was estimated from 1-day unweighed dietary records in 881 infants and related to their childhood weight gain and BMI. RESULTS. Among formula- or mixed-fed infants ( N = 582), energy intake was higher in first-born infants (mean ± SE: 2730 ± 29.4 kJ/day; n = 263) than in subsequent-born infants (2620.8 ± 25.2 kJ/day; n = 296). Energy intake at 4 months was also higher in infants who were given solid foods earlier (1–2 months: 2805.6 ± 50.4 kJ/day, n = 89; 2–3 months: 2658.6 ± 25.2 kJ/day, n = 339; 4+ months: 2587.2 ± 46.2 kJ/day, n = 111). Higher energy intake at 4 months predicted greater weight gain between birth to age 1, 2, or 3 years and larger body weight and BMI at ages 1 to 5 years. No significant associations were seen in breastfed infants ( N = 299). CONCLUSIONS. Among formula- or mixed-fed infants, dietary energy intake at age 4 months predicted postnatal weight gain and childhood obesity risk. Both prenatal and postnatal factors may influence infant energy intake and postnatal weight gain.


title: Conditional Graphical Lasso for Multi-label Image Classification

abstract: Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.


IIT (BHU): System Description for LSDSem'17 Shared Task


title: The dependent personality : developmental, social, and clinical perspectives

abstract: Developmental, social, and clinical studies of dependency have produced remarkably consistent results. A review and integration of these findings allow strong conclusions to be drawn regarding the etiology and dynamics of dependency


title: Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

abstract: Machine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Hence, there is much scope for the application of MLAs to the rapidly increasing volumes of remotely sensed geophysical data for geological mapping problems. We carry out a rigorous comparison of five MLAs: Naive Bayes, k-Nearest Neighbors, Random Forests, Support Vector Machines, and Artificial Neural Networks, in the context of a supervised lithology classification task using widely available and spatially constrained remotely sensed geophysical data. We make a further comparison of MLAs based on their sensitivity to variations in the degree of spatial clustering of training data, and their response to the inclusion of explicit spatial information (spatial coordinates). Our work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data. Random Forests is straightforward to train, computationally efficient, highly stable with respect to variations in classification model parameter values, and as accurate as, or substantially more accurate than the other MLAs trialed. The results of our study indicate that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically. The use of explicit spatial information generates accurate lithology predictions but should be used in conjunction with geophysical data in order to generate geologically plausible predictions. MLAs, such as Random Forests, are valuable tools for generating reliable first-pass predictions for practical geological mapping applications that combine widely available geophysical data.


Virtual Reality Exposure Therapy for Posttraumatic Stress Disorder


title: Narrow bandpass filters using dual-behavior resonators based on stepped-impedance stubs and different-length stubs

abstract: Dual-behavior resonators (DBRs) are based on the parallel association of two different bandstop structures, which implies a constructive recombination. Two kinds of stubs, stepped-impedance and different-length stubs, are examined in this paper. The first section concerns stepped-impedance stubs. We have previously reported some simplifications of the fundamental equations. Here, we propose a new simplification together with synthesis; both allow an independent control of the two attenuated bands. In a second section, a DBR composed of two uniform stubs of different lengths is studied instead of two stepped-impedance open-circuited stubs. This implies a greatly simplified synthesis and implementation. Some other structures combining short- and open-circuited stubs are also introduced. Throughout the paper, simulations are proposed to illustrate the possibilities offered by such topologies. Experimental results in microstrip technology are also presented in order to validate our idea. Finally, benefits and drawbacks of the different structures are discussed.


title: The biological impact of mass-spectrometry-based proteomics

abstract: In the past decade, there have been remarkable advances in proteomic technologies. Mass spectrometry has emerged as the preferred method for in-depth characterization of the protein components of biological systems. Using mass spectrometry, key insights into the composition, regulation and function of molecular complexes and pathways have been gained. From these studies, it is clear that mass-spectrometry-based proteomics is now a powerful 'hypothesis-generating engine' that, when combined with complementary molecular, cellular and pharmacological techniques, provides a framework for translating large data sets into an understanding of complex biological processes.


title: A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

abstract: The risks and perils of overfitting in machine learning are well known. However most of the treatment of this, including diagnostic tools and remedies, was developed for the supervised learning case. In this work, we aim to offer new perspectives on the characterization and prevention of overfitting in deep Reinforcement Learning (RL) methods, with a particular focus on continuous domains. We examine several aspects, such as how to define and diagnose overfitting in MDPs, and how to reduce risks by injecting sufficient training diversity. This work complements recent findings on the brittleness of deep RL methods and offers practical observations for RL researchers and practitioners.