title: Authentication using graphical passwords: effects of tolerance and image choice

abstract: Graphical passwords are an alternative to alphanumeric passwords in which users click on images to authenticate themselves rather than type alphanumeric strings. We have developed one such system, called PassPoints, and evaluated it with human users. The results of the evaluation were promising with respect to rmemorability of the graphical password. In this study we expand our human factors testing by studying two issues: the effect of tolerance, or margin of error, in clicking on the password points and the effect of the image used in the password system. In our tolerance study, results show that accurate memory for the password is strongly reduced when using a small tolerance (10 x 10 pixels) around the user's password points. This may occur because users fail to encode the password points in memory in the precise manner that is necessary to remember the password over a lapse of time. In our image study we compared user performance on four everyday images. The results indicate that there were few significant differences in performance of the images. This preliminary result suggests that many images may support memorability in graphical password systems.


A System of Operative Surgery


title: Current Advances in the Cognitive Neuroscience of Music

abstract: The study of music perception and cognition is one of the oldest topics in experimental psychology. The last 20 years have seen an increased interest in understanding the functional neuroanatomy of music processing in humans, using a variety of technologies including fMRI, PET, ERP, MEG, and lesion studies. We review current findings in the context of a rich intellectual history of research, organized by the cognitive systems underlying different aspects of human musical behavior. We pay special attention to the perception of components of musical processing, musical structure, laterality effects, cultural issues, links between music and movement, emotional processing, expertise, and the amusias. Current trends are noted, such as the increased interest in evolutionary origins of music and comparisons of music and language. The review serves to demonstrate the important role that music can play in informing broad theories of higher order cognitive processes such as music in humans.


title: Elastic bands: connecting path planning and control

abstract: Elastic bands are proposed as the basis for a framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. The framework is outlined, and an efficient implementation based on bubbles is discussed. >


title: Cluster-based D2D architecture for safety services in vehicular ad hoc networks

abstract: With the expected introduction of autonomous vehicles in the next decade, it will be imperative for road traffic authorities to develop strategies to manage the co-existence of autonomous and legacy vehicles. In this scenario, the exchange of safety messages among vehicles in a timely manner will be critical for future advanced road traffic management systems. Most of the work in the area of vehicular ad hoc network (VANET) has concentrated on the IEEE 802.11p and the DSRC standards. With the emergence of the advanced LTE-based cellular network standard, researchers are now concentrating on the LTE-based VANET architecture. However, due to the use of the centralized architecture, an LTE-based system may require significant network resources to support vehicle-to-vehicle (V2V) communications. In this paper, we propose a new vehicle discovery technique and a LTE cluster-based device-to-device (D2D) architecture to achieve high packet delivery ratio for the safety services in a vehicular network. The performance of the proposed network architecture is analyzed by an OMNET++ based simulation model. Simulations results shows that the proposed algorithm reduces the end-to-end delay and signalling overhead as well as improve the data packet delivery ratio (DPDR) compared to the existing 3GPP ProSe technique for vehicular safety applications.


title: An Underactuated Multi-finger Grasping Device

abstract: In this paper, a mechanical model for an underactuated multi-finger grasping device is presented. The device has single-tendon, three-phalanx fingers, all moved by only one actuator. By means of the model, both the kinematic and dynamical behaviour of the finger itself can be studied. The finger is part of a more complex mechanical system that consists of a four-finger grasping device for robots or a five-finger human hand prosthesis. Some results of both the kinematic and dynamical behaviour are also presented.


title: Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble

abstract: This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbour-based anomaly detection method by isolation. Inne runs significantly faster than existing nearest neighbour-based methods such as Local Outlier Factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and constant space complexity. Compared with the existing tree-based isolation method iForest, the proposed isolation method overcomes three weaknesses of iForest that we have identified, i.e., Its inability to detect local anomalies, anomalies with a low number of relevant attributes, and anomalies that are surrounded by normal instances.


title: Crowdfunding: A Review and Research Agenda

abstract: Crowdfunding is an alternative model for project financing, where a large and dispersed audience participates through relatively small financial contributions, in exchange for physical, financial or social rewards. It is usually done via Internet-based platforms that act as a bridge between the crowd and the projects. Over the last few years, academic scholars have explored this topic, both empirically and theoretically. However, the mixed findings and array of theories used have come to warrant a critical review of past works. To this end, we perform a systematic literature review on crowdfunding and seek to extract (1) the key management theories that have been applied in the context of crowdfunding and how these have been extended, and (2) the principal factors contributing to success for the different crowdfunding models, where success entails accomplishment of both fundraising and timely repayment. In the process, we offer a comprehensive definition of crowdfunding and identify avenues for future research based on the gaps and conflicting results in the literature.


title: Optimizing the LLC–LC Resonant Converter Topology for Wide-Output-Voltage and Wide-Output-Load Applications

abstract: LLC-LC resonant converter is a suitable circuit topology to design switched-mode power supplies for wide-output-voltage and wide-output-load applications. In this paper, a design procedure is introduced to optimize this converter. Unlike soft switching techniques for pulse width modulated converters, which usually apply an active auxiliary circuit to reduce switching losses and EMI, in the proposed converter, not only such circuits are not used, but also all of the parasitic elements are merged to the converter's main components. Zero-voltage switching (ZVS) operation is realized for all power devices under all operating conditions. Thus, this converter is a suitable choice for high-switching-frequency operation. The converter parameters have been designed to minimize the components' current and voltage stresses. In addition, the frequency variation range has been reduced in order to use the magnetic components, optimally. Developed prototype of the converter has been tested for different output voltages (25-165 Vdc) under different loads (0-3 Adc) and input voltages (320-370 Vdc) for being used as ion implanter arc power supply. For regulating and adjusting the designed prototype converter output voltage, its switching frequency varies from 177 to 314 kHz. The converter's maximum efficiency is approximately equal to 96.6%.


title: Secure data sharing in multi-clouds

abstract: Security is the main concern in the cloud computing environment. The usage of the cloud computing in increasing gradually, where the security is still lagging. Traditionally, use of single cloud in cloud computing environment leads to security issues, malicious users, and also untrusted users. The recent research proved that Multi cloud computing provides all the services in a single architecture by combining many clouds. This research aims in providing the reliable source of dependency on multi clouds. The main purpose of this paper is to use multi-clouds for data security and reduce data risks for an efficient and reliable data accessibility. Different techniques are used to implement the multi cloud computing, which lacks in the providing efficiency, data security and privacy, low cost. The leverage of security in multi-clouds is enhanced through application of Data Sharing Algorithm.


title: Using Object Information for Spotting Text

abstract: Text spotting, also called text detection, is a challenging computer vision task because of cluttered backgrounds, diverse imaging environments, various text sizes and similarity between some objects and characters, e.g., tyre and ‘o’. However, text spotting is a vital step in numerous AI and computer vision systems, such as autonomous robots and systems for visually impaired. Due to its potential applications and commercial values, researchers have proposed various deep architectures and methods for text spotting. These methods and architectures concentrate only on text in images, but neglect other information related to text. There exists a strong relationship between certain objects and the presence of text, such as signboards or the absence of text, such as trees. In this paper, a text spotting algorithm based on text and object dependency is proposed. The proposed algorithm consists of two sub-convolutional neural networks and three training stages. For this study, a new NTU-UTOI dataset containing over 22k non-synthetic images with 277k bounding boxes for text and 42 text-related object classes is established. According to our best knowledge, it is the second largest non-synthetic text image database. Experimental results on three benchmark datasets with clutter backgrounds, COCO-Text, MSRA-TD500 and SVT show that the proposed algorithm provides comparable performance to state-of-the-art text spotting methods. Experiments are also performed on our newly established dataset to investigate the effectiveness of object information for text spotting. The experimental results indicate that the object information contributes significantly on the performance gain.


title: Perceived Parental Social Support and Academic Achievement: An Attachment Theory Perspective

abstract: Parents strive to cultivate personal characteristics in their offspring that will enable them to function successfully and independently once they leave the parental home. This cultivation must occur throughout the child's life if he or she is to develop the self-confidence and skills required to meet the challenges of adult life. Contrary to early theories of adolescent development (e.g., Erikson, 1956), a stormy rejection of parental ties is not required to attain competent adult status. Rather, research indicates that individuals who maintain a close relationship with parents throughout adolescence demonstrate more self-confidence and independence than those who report greater emotional distance from their parents (Maccoby & Martin, 1983). Theories of early childhood development also stress the importance of a high-quality relationship with parents for the acquisition of needed life skills. According to Ainsworth (1982), responsive and nurturant parents provide a secure base from which the child can explore the environment without anxiety about parental availability. A secure relationship with one or both parents facilitates exploration, discovery, skill acquisition, and the development of self-confidence (Ainsworth, 1982). It is hypothesized that a secure relationship with parents throughout childhood contributes to the development of positive schemata regarding oneself (i.e., high self-worth and self-efficacy) and what can be expected in relationships with others (i.e., a


title: The design and evaluation of prototype eco-feedback displays for fixture-level water usage data

abstract: Few means currently exist for home occupants to learn about their water consumption: e.g., where water use occurs, whether such use is excessive and what steps can be taken to conserve. Emerging water sensing systems, however, can provide detailed usage data at the level of individual water fixtures (i.e., disaggregated usage data). In this paper, we perform formative evaluations of two sets of novel eco-feedback displays that take advantage of this disaggregated data. The first display set isolates and examines specific elements of an eco-feedback design space such as data and time granularity. Displays in the second set act as design probes to elicit reactions about competition, privacy, and integration into domestic space. The displays were evaluated via an online survey of 651 North American respondents and in-home, semi-structured interviews with 10 families (20 adults). Our findings are relevant not only to the design of future water eco-feedback systems but also for other types of consumption (e.g., electricity and gas).


title: CNFET based ternary magnitude comparator

abstract: Ternary logic is a promising alternative to conventional binary logic, since it is possible to achieve simplicity and energy efficiency due to the reduced circuit overhead. In this paper, a ternary magnitude comparator design based on Carbon Nanotube Field Effect Transistors (CNFETs) is presented. This design eliminates the usage of complex ternary decoder which is a part of existing designs. Elimination of decoder results in reduction of delay and power. Simulations of proposed and existing designs are done on HSPICE and results proves that the proposed 1-bit comparator consumes 81% less power and shows delay advantage of 41.6% compared to existing design. Further a methodology to extend the 1-bit comparator design to n-bit comparator design is also presented.


title: Are Sex Drive and Hypersexuality Associated with Pedophilic Interest and Child Sexual Abuse in a Male Community Sample?

abstract: Although much is currently known about hypersexuality (in the form of excessive sexual behavior) among sexual offenders, the degree to which hypersexual behavior is linked to paraphilic and especially pedophilic interests in non-forensic populations has not been established. The purpose of the present study was to elucidate the associations between total sexual outlets (TSO) and other sex drive indicators, antisocial behavior, pedophilic interests, and sexual offending behavior in a large population-based community sample of males. The sample included 8,718 German men who participated in an online study. Hypersexual behavior as measured by self-reported TSO, self-reported sex drive, criminal history, and pedophilic interests were assessed. In moderated hierarchical logistic regression analyses self-reported contact sexual offending against children was linked to sexual fantasizing about children and antisociality. There was no association between aggregated sex drive, and sexual abusive behaviour in the multivariate analyses. In contrast, self-reported child pornography consumption was associated with sex drive, sexual fantasies involving children, and antisociality. Nevertheless, in clinical practice an assessment of criminal history and pedophilic interests in hypersexual individuals and vice versa hypersexuality in antisocial or pedophilic men should be considered as particularly antisociality and pedophilic interest are important predictors of sexual offending against prepubescent children.


title: Knowledge sharing framework for iKaaS platform

abstract: The iKaaS platform has been developed as a Horizon 2020 project to construct a multi-cloud environment in order to share data and knowledge. In this paper, we add a framework for knowledge sharing/reuse in the iKaaS platform. Furthermore, we design a revenue-sharing mechanism for knowledge sharing, which uses a blockchain for registration and use of data processing services.


title: FET 11-The European Future Technologies Conference and Exhibition Introducing the Human Brain Project

abstract: The Human Brain Project (HBP) is a candidate project in the European Union's FET Flagship Program, funded by the ICT Program in the Seventh Framework Program. The project will develop a new integrated strategy for understanding the human brain and a novel research platform that will integrate all the data and knowledge we can acquire about the structure and function of the brain and use it to build unifying models that can be validated by simulations running on supercomputers. The project will drive the development of supercomputing for the life sciences, generate new neuroscientific data as a benchmark for modeling, develop radically new tools for informatics, modeling and simulation, and build virtual laboratories for collaborative basic and clinical studies, drug simulation and virtual prototyping of neuroprosthetic, neuromorphic, and robotic devices. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.


title: Ultralow-Voltage Process-Variation-Tolerant Schmitt-Trigger-Based SRAM Design

abstract: We analyze Schmitt-Trigger (ST)-based differential-sensing static random access memory (SRAM) bitcells for ultralow-voltage operation. The ST-based SRAM bitcells address the fundamental conflicting design requirement of the read versus write operation of a conventional 6T bitcell. The ST operation gives better read-stability as well as better write-ability compared to the standard 6T bitcell. The proposed ST bitcells incorporate a built-in feedback mechanism, achieving process variation tolerance - a must for future nano-scaled technology nodes. A detailed comparison of different bitcells under iso-area condition shows that the ST-2 bitcell can operate at lower supply voltages. Measurement results on ten test-chips fabricated in 130-nm CMOS technology show that the proposed ST-2 bitcell gives 1.6× higher read static noise margin, 2× higher write-trip-point and 120-mV lower read-Vmin compared to the iso-area 6T bitcell.


title: Situated Resolution and Generation of Spatial Referring Expressions for Robotic Assistants

abstract: In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot- and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address this challenge with a method for context construction that can be used for both generating and resolving REs - two previously disjoint aspects. Our approach is embedded in a bi-directional framework for natural language processing for robots.


title: Combined Pulmonary Fibrosis and Emphysema Syndrome: A New Phenotype within the Spectrum of Smoking-Related Interstitial Lung Disease

abstract: Combined pulmonary fibrosis and emphysema (CPFE) is a recently defined syndrome, in which centrilobular and/or paraseptal emphysemas in upper lung zones coexist with pulmonary fibrosis in lower lobes in individuals. These patients have a characteristic lung function profile, with unexpected subnormal dynamic and static lung volumes, contrasting with a significant reduction of carbon monoxide transfer (DLco) and exercise hypoxemia. Pulmonary hypertension is highly prevalent in CPFE and is the leading determinant of death. Tobacco smoking has been proposed as the main factor in its etiology, though the pathophysiology and its natural history remain to be determined. High-resolution computed axial tomography is the mandatory tool to confirm the diagnosis. Currently, there is no consensus about its treatment since those published to date on this issue are limited to well-characterised series of cases; hence, a better understanding of this entity may help in the development of future therapeutic approaches.


title: Development of a helical climbing modular snake robot

abstract: This research aims to study the grasping torque profile of the helical climbing robot on the cylindrical pole with constant radius. The modular snake robot is formed into a helical shape which can be described by various parameters such as the helical pitch angle, the radius and the pitch distance. The torque in each axis of rotation is affected by the helical pitch angle parameter. Five grasping configurations with different helical pitch angle were tested on the 18 degree of freedoms, 7 modules wheeled snake robot. The experimental result showed that the torque around yaw axis transferred to the pitch axis when the helical pitch angle was increasing. The profile of torque magnitude along the robot's body resulted in parabolic shape due to the unbalanced grasping force of the discrete points of contact between the robot and the pole.


title: Why Is My Classifier Discriminatory?

abstract: Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.


title: Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

abstract: We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-decoder structure to preserve relative spatial coordinates between the input image and the output feature map. Based on the joint probabilities of each of the Gaussian mixture components, we introduce a sample energy based method to score the anomaly of image test patches. A two-stream network framework is employed to combine the appearance and motion anomalies, using RGB frames for the former and dynamic flow images, for the latter. We test our approach on two popular benchmarks (UCSD Dataset and Avenue Dataset). The experimental results verify the superiority of our method compared to the state of the arts.


title: Biomechanics of walking, running, and sprinting

abstract: A biomechanical study of 13 runners which consisted of 2 male sprinters, 5 experienced joggers, and 6 elite long-distance runners were studied. We obtained hip, knee, and ankle joints motions in the sagittal plane and electromyographic data from specific muscle groups. As the speed of gait increased, the length of stance phase progressively decreased from 62% for walking to 31% for running and to 22% for sprinting. The sagittal plane motion increased as the speed of gait increased. Generally speaking, the body lowers its center of gravity with the increased speed by increasing flexion of the hips and knees and magnifying dorsiflexion at the ankle joint. Electromyographic activity about the knee demonstrated increased activity in the quadricep muscle group and hamstring group with increased speed. Muscle function about the ankle joint demonstrated that the posterior calf musculature which normally functions during the midstance phase in walking became a late swing phase muscle and was active through the first 80% of stance phase, as compared to 15% in walking. Beside the changes in the electromyographic activity of the muscles, the anterior compartment muscles of the calf undergo a concentric contracture at the time of initial floor contact during running and sprinting but undergo an eccentric contraction during walking.


Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz


title: Improv Chat: Second Response Generation for Chatbot

abstract: Existing research on response generation for chatbot focuses on \textbf{First Response Generation} which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user's query). In this paper, we introduce a new task \textbf{Second Response Generation}, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users' query and the chatbot's first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.


title: Corporate reputation and sustained superior financial performance

abstract: Good corporate reputations are critical because of their potential for value creation, but also because their intangible character makes replication by competing firms considerably more difficult. Existing empirical research confirms that there is a positive relationship between reputation and financial performance. This paper complements these findings by showing that firms with relatively good reputations are better able to sustain superior profit outcomes over time. In particular, we undertake an analysis of the relationship between corporate reputation and the dynamics of financial performance using two complementary dynamic models. We also decompose overall reputation into a component that is predicted by previous financial performance, and that which is ‘left over’, and find that each (orthogonal) element supports the persistence of above-average profits over time. Copyright © 2002 John Wiley & Sons, Ltd.


title: Pre-production forecasting of movie revenues with a dynamic artificial neural network

abstract: We show DAN2 to be an effective tool for forecasting movie revenues.DAN2 improved upon benchmark ANN-based revenue forecasting models by 32.8%.We develop a new model to forecast movie revenues during the pre-production period.We offer new insights into the role of various movie attributes in revenue forecasts.DAN2 achieves an accuracy rate of 94.1% with this new model and variable-set. The production of a motion picture is an expensive, risky endeavor. During the five-year period from 2008 through 2012, approximately 90 films were released in the United States with production budgets in excess of $100 million. The majority of these films failed to recoup their production costs via gross domestic box office revenues. Existing decision support systems for pre-production analysis and green-lighting decisions lack sufficient accuracy to meaningfully assist decision makers in the film industry.Established models focus primarily upon post-release and post-production forecasts. These models often rely upon opening weekend data and are reasonably accurate but only if data up until the moment of release is included. A forecast made immediately prior to the debut of a film, however, is of limited value to stakeholders because it can only influence late-stage adjustments to advertising or distribution strategies and little else.In this paper we present the development of a model based upon a dynamic artificial neural network (DAN2) for the forecasting of movie revenues during the pre-production period. We first demonstrate the effectiveness of DAN2 and show that DAN2 improves box-office revenue forecasting accuracy by 32.8% over existing models. Subsequently, we offer an alternative modeling strategy by adding production budgets, pre-release advertising expenditures, runtime, and seasonality to the predictive variables. This alternative model produces excellent forecasting accuracy values of 94.1%.


Guiding the Herd: The Effect of Reference Groups in Crowdfunding Decision Making


title: Adaptive scheduling with parallelism feedback

abstract: Multiprocessor scheduling in a shared multiprogramming environment is often structured as two-level scheduling, where a kernel-level job scheduler allots processors to jobs and a user-level task scheduler schedules the work of a job on the allotted processors. In this context, the number of processors allotted to a particular job may vary during the job's execution, and the task scheduler must adapt to these changes in processor resources. For overall system efficiency, the task scheduler should also provide parallelism feedback to the job scheduler to avoid the situation where a job is allotted processors that it cannot use productively.We present an adaptive task scheduler for multitasked jobs with dependencies that provides continual parallelism feedback to the job scheduler in the form of requests for processors. Our scheduler guarantees that a job completes near optimally while utilizing at least a constant fraction of the allotted processor cycles. Our scheduler can be applied to schedule data-parallel programs, such as those written in High Performance Fortran (HPF), *Lisp, C*, NESL, and ZPL.Our analysis models the job scheduler as the task scheduler's adversary, challenging the task scheduler to be robust to the system environment and the job scheduler's administrative policies. For example, the job scheduler can make available a huge number of processors exactly when the job has little use for them. To analyze the performance of our adaptive task scheduler under this stringent adversarial assumption, we introduce a new technique called "trim analysis," which allows us to prove that our task scheduler performs poorly on at most a small number of time steps, exhibiting near-optimal behavior on the vast majority.To be precise, suppose that a job has work T 1 and critical-path length T ∞ and is running on a machine with P processors. Using trim analysis, we prove that our scheduler completes the job in O(T 1 /P + T ∞ + Llg P) time steps, where L is the length of a scheduling quantum and P denotes the O(T ∞ + L lg P)-trimmed availability. This quantity is the average of the processor availability over all time steps excluding the O(T ∞ + L lg P) time steps with the highest processor availability. When T 1 /T ∞ >> P (the job's parallelism dominates the O(T ∞ + L lg P)-trimmed availability), the job achieves nearly perfect linear speedup. Conversely, when T 1 /T ∞ << P, the asymptotic running time of the job is nearly the length of its critical path.


Low-Rank Linear Cold-Start Recommendation from Social Data.


title: Interplay of Inhibition and Excitation Shapes a Premotor Neural Sequence

abstract: In the zebra finch, singing behavior is driven by a sequence of bursts within premotor neurons located in the forebrain nucleus HVC (proper name). In addition to these excitatory projection neurons, HVC also contains inhibitory interneurons with a role in premotor patterning that is unclear. Here, we used a range of electrophysiological and behavioral observations to test previously described models suggesting discrete functional roles for inhibitory interneurons in song production. We show that single HVC premotor neuron bursts are sufficient to drive structured activity within the interneuron network because of pervasive and facilitating synaptic connections. We characterize interneuron activity during singing and describe reliable pauses in the firing of those neurons. We then demonstrate that these gaps in inhibition are likely to be necessary for driving normal bursting behavior in HVC premotor neurons and suggest that structured inhibition and excitation may be a general mechanism enabling sequence generation in other circuits.


title: Clique-graph matching by preserving global & local structure

abstract: This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise clique correspondence constrained by the one-to-one matching. Since the objective function is not jointly convex to both latent variables, we decompose it into two consecutive steps for optimization: 1) clique-to-clique similarity measure by preserving local unary and pairwise correspondences; 2) graph-to-graph similarity measure by preserving global clique-to-clique correspondence. Extensive experiments on the synthetic data and real images show that the proposed method can outperform representative methods especially when both noise and outliers exist.


title: A process of knowledge discovery from web log data: Systematization and critical review

abstract: This paper presents a comprehensive survey of web log/usage mining based on over 100 research papers. This is the first survey dedicated exclusively to web log/usage mining. The paper identifies several web log mining sub-topics including specific ones such as data cleaning, user and session identification. Each sub-topic is explained, weaknesses and strong points are discussed and possible solutions are presented. The paper describes examples of web log mining and lists some major web log mining software packages.


title: Semi-supervised Aspect Based Sentiment Analysis for Movies Using Review Filtering

abstract: Abstract Aspect based Sentiment Analysis (ABSA) is a subarea of opinion mining which enables one to gain deeper insights into the features of items which interest the users by mining reviews. In this paper we attempt to perform ABSA on movie review data. Unlike other domains such as camera, laptops restaurants etc, a major chunk of movie reviews is devoted to describing the plot and contains no information about user interests. The presence of such narrative content may potentially mislead the review analysis. The contribution of this paper is two-fold: a two class classification scheme for plots and reviews without the need for labeled data is proposed. The overhead of constructing manually labeled data to build the classifier is avoided and the resulting classifier is shown to be effective using a small manually built test set. Secondly we propose a scheme to detect aspects and the corresponding opinions using a set of hand crafted rules and aspect clue words. Three schemes for selection of aspect clue words are explored - manual labeling (M), clustering(C) and review guided clustering (RC). The aspect and sentiment detection using all the three schemes is empirically evaluated against a manually constructed test set. The experiments establish the effectiveness of manual labeling over cluster based approaches but among the cluster based approaches, the ones utilizing the review guided clue words performed better.


title: Methodology of an exercise intervention program using social incentives and gamification for obese children

abstract: Background ::: Traditional exercise [supervised exercise (SE)] intervention has been proved to be one of the most effective ways to improve metabolic health. However, most exercise interventions were on a high-cost and small scale, moreover lacking of the long-term effect due to low engagement. On the other hand, it was noteworthy that gamification and social incentives were promising strategies to increase engagement and sustain exercise interventions effects; as well as mobile technologies such as WeChat also can provide an appropriate platform to deploy interventions on a broader, low-cost scale. Thus, we aim to develop a novel exercise intervention (‘S&G exercise intervention’) that combines SE intervention with gamification and social incentives design through WeChat, with the aim of improving metabolic health and poor behaviors among overweight and obesity children.


title: A Machine Learning Approach to Identifying Sections in Legal Briefs

abstract: With an abundance of legal documents now available in electronic format, legal scholars and practitioners are in need of systems able to search and quantify semantic details of these documents. A key challenge facing designers of such systems, however, is that the majority of these documents are natural language streams lacking formal structure or other explicit semantic information. In this research, we describe a two-stage supervised learning approach for automatically identifying section boundaries and types in appellee briefs. Our approach uses learned classifiers in a two-stage process to categorize white-space separated blocks of text. First, we use a binary classifier to predict whether or not a text block is a section header. Next, we classify those blocks predicted to be section headers in the first stage into one of 19 section types. A cross-validation experiment shows our approach has over 90% accuracy on both tasks, and is significantly more accurate than baseline methods.


title: TASTY: tool for automating secure two-party computations

abstract: Secure two-party computation allows two untrusting parties to jointly compute an arbitrary function on their respective private inputs while revealing no information beyond the outcome. Existing cryptographic compilers can automatically generate secure computation protocols from high-level specifications, but are often limited in their use and efficiency of generated protocols as they are based on either garbled circuits or (additively) homomorphic encryption only. In this paper we present TASTY, a novel tool for automating, i.e., describing, generating, executing, benchmarking, and comparing, efficient secure two-party computation protocols. TASTY is a new compiler that can generate protocols based on homomorphic encryption and efficient garbled circuits as well as combinations of both, which often yields the most efficient protocols available today. The user provides a high-level description of the computations to be performed on encrypted data in a domain-specific language. This is automatically transformed into a protocol. TASTY provides most recent techniques and optimizations for practical secure two-party computation with low online latency. Moreover, it allows to efficiently evaluate circuits generated by the well-known Fairplay compiler. We use TASTY to compare protocols for secure multiplication based on homomorphic encryption with those based on garbled circuits and highly efficient Karatsuba multiplication. Further, we show how TASTY improves the online latency for securely evaluating the AES functionality by an order of magnitude compared to previous software implementations. TASTY allows to automatically generate efficient secure protocols for many privacy-preserving applications where we consider the use cases for private set intersection and face recognition protocols.


title: All-Metal Transmit-Array for Circular Polarization Design Using Rotated Cross-Slot Elements for High-Power Microwave Applications

abstract: Transmit-array (T-A) design for circular polarization made by all metal is presented in this communication. The proposed T-A consists of numerous cross-slot elements. The simulation results indicate that rotating the cross slot on the element, a full transmission phase range of 360° can be achieved for a transmission magnitude better than −0.6 dB at 9.375 GHz. A T-A has been designed, simulated, and measured in x-band. The measurement results show that the T-A can radiate the wave in the desired direction with an enhanced gain. They are in agreement with the theoretical and simulation results. Furthermore, the all-metal T-A presented in this communication has the application potentials for high-power microwave applications.


title: Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture

abstract: Face recognition applications for airport security and surveillance can benefit from the collaborative coupling of mobile and cloud computing as they become widely available today. This paper discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The challenge lies with how to perform task partitioning from mobile devices to cloud and distribute compute load among cloud servers (cloudlet) to minimize the response time given diverse communication latencies and server compute powers. Our preliminary simulation results show that optimal task partitioning algorithms significantly affect response time with heterogeneous latencies and compute powers. Motivated by these results, we design, implement, and validate the basic functionalities of MOCHA as a proof-of-concept, and develop algorithms that minimize the overall response time for face recognition. Our experimental results demonstrate that high-powered cloudlets are technically feasible and indeed help reduce overall processing time when face recognition applications run on mobile devices using the cloud as the backend servers.


title: A Fully-Integrated 3-Level DC-DC Converter for Nanosecond-Scale DVFS

abstract: On-chip DC-DC converters have the potential to offer fine-grain power management in modern chip-multiprocessors. This paper presents a fully integrated 3-level DC-DC converter, a hybrid of buck and switched-capacitor converters, implemented in 130 nm CMOS technology. The 3-level converter enables smaller inductors (1 nH) than a buck, while generating a wide range of output voltages compared to a 1/2 mode switched-capacitor converter. The test-chip prototype delivers up to 0.85 A load current while generating output voltages from 0.4 to 1.4 V from a 2.4 V input supply. It achieves 77% peak efficiency at power density of 0.1 W/mm2 and 63% efficiency at maximum power density of 0.3 W/mm2. The converter scales output voltage from 0.4 V to 1.4 V (or vice-versa) within 20 ns at a constant 450 mA load current. A shunt regulator reduces peak-to-peak voltage noise from 0.27 V to 0.19 V under pseudo-randomly fluctuating load currents. Using simulations across a wide range of design parameters, the paper compares conversion efficiencies of the 3-level, buck and switched-capacitor converters.


title: Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches

abstract: Machine-to-machine communication, a promising technology for the smart city concept, enables ubiquitous connectivity between one or more autonomous devices without or with minimal human interaction. M2M communication is the key technology to support data transfer among sensors and actuators to facilitate various smart city applications (e.g., smart metering, surveillance and security, infrastructure management, city automation, and eHealth). To support massive numbers of machine type communication (MTC) devices, one of the challenging issues is to provide an efficient way for multiple access in the network and to minimize network overload. In this article, we review the M2M communication techniques in Long Term Evolution- Advanced cellular networks and outline the major research issues. Also, we review the different random access overload control mechanisms to avoid congestion caused by random channel access of MTC devices. To this end, we propose a reinforcement learning-based eNB selection algorithm that allows the MTC devices to choose the eNBs (or base stations) to transmit packets in a self-organizing fashion.


A Sub-linear, Massive-scale Look-alike Audience Extension System A Massive-scale Look-alike Audience Extension


title: Concentrated Differential Privacy

abstract: We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations.


title: Learning Discriminative and Shareable Features for Scene Classification

abstract: In this paper, we propose to learn a discriminative and shareable feature transformation filter bank to transform local image patches (represented as raw pixel values) into features for scene image classification. The learned filters are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative and class-specific information. For each category, a subset of the filters are activated in a data-adaptive manner, meanwhile sharing of filters among different categories is also allowed. Discriminative power of the filter bank is further enhanced by enforcing the features from the same category to be close to each other in the feature space, while features from different categories to be far away from each other. The experimental results on three challenging scene image classification datasets indicate that our features can achieve very promising performance. Furthermore, our features also show great complementary effect to the state-of-the-art ConvNets feature.


title: for an updated version if you want to cite this. Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System

abstract: One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance, though eBay's net feedback statistic is far from the best predictor available. Fourth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.


Object Classification and Localization Using SURF Descriptors


title: Style: Language Variation and Identity

abstract: Style refers to ways of speaking - how speakers use the resource of language variation to make meaning in social encounters. This 2007 book develops a coherent theoretical approach to style in sociolinguistics, illustrated with copious examples. It explains how speakers project different social identities and create different social relationships through their style choices, and how speech-style and social context inter-relate. Style therefore refers to the wide range of strategic actions and performances that speakers engage in, to construct themselves and their social lives. Coupland draws on and integrates a wide variety of contemporary sociolinguistic research as well as his own extensive research in this field. The emphasis is on how social meanings are made locally, in specific relationships, genres, groups and cultures, and on studying language variation as part of the analysis of spoken discourse.


abstract: Poster: "ECR 2014 / C-0879 / Paediatric breast ultrasound - what, why and when?" by: "E.-L. Gerety1, P. Britton1, M. G. Wallis1, A. Gomez2; 1Cambridge/UK, 2Cambridge /UK"


title: Jasplakinolide Induces Apoptosis in Various Transformed Cell Lines by a Caspase-3-Like Protease-Dependent Pathway

abstract: To clarify the mechanisms underlying the antiproliferative effects of jasplakinolide, a cyclic depsipeptide from marine sponges, we examined whether jasplakinolide induces apoptosis in a variety of transformed and nontransformed cells. Jasplakinolide inhibited proliferation of human Jurkat T cells, resulting in cell death. This was accompanied by chromatin condensation and DNA cleavage at the linker regions between the nucleosomes. When caspase-3-like activity in the cytosolic extracts of Jurkat T cells was examined with a fluorescent substrate, DEVD-MAC (N-acetyl-Asp-Glu-Val-Asp-4-methyl-coumaryl-7-amide), the activity in the cells treated with jasplakinolide was remarkably increased in a time-dependent manner. Pretreatment of Jurkat T cells with the caspase inhibitor zVAD [benzyloxycarbonyl(Cbz)-Val-Ala-β-Asp(OMe)-fluoromethylketone] or DEVD-CHO (N-acetyl-Asp-Glu-Val-Asp-1-aldehyde) prevented the induction of apoptosis by jasplakinolide. Moreover, exposure of various murine transformed cell lines to jasplakinolide resulted in cell death, which was inhibited by zVAD. Although it has been well established that murine immature thymocytes are sensitive to apoptosis when exposed to various apoptotic stimuli, these cells as well as mature T lymphocytes were resistant to jasplakinolide-induced apoptosis. The results suggest that jasplakinolide induces apoptotic cell death through a caspase-3-like protease-dependent pathway. Another important outcome is that transformed cell lines were more susceptible to jasplakinolide-induced apoptosis than normal nontransformed cells.


title: SEED: Hands-On Lab Exercises for Computer Security Education

abstract: This paper presents the development of a set of hands-on exercises(labs) that covered a spectrum of security topics and could be shared with other instructors. The author developed SEED labs covering many security topics: vulnerabilities, attacks, software security, system security, network security, Web security, access control, authentication, cryptography, and so on. Most SEED labs have gone through multiple development trial cycles in university courses. Many universities have requested the instructor's manual and adopted SEED labs for their courses. Most SEED labs use Ubuntu Linux. A few labs require significant effort for kernel-level coding; for them, the author chose Minix, an instructional OS. SEED labs don't require a dedi cated physical laboratory. The author created a prebuilt VM image of Ubuntu and installed the necessary software. Various lab environment setups were tried, including using students' PCs, a general computing lab, and a cloud-computing infrastructure. And found that for convenience, students prefer to use their own computers.


title: Virtual synchronous-machine control of voltage-source converters in a low-voltage microgrid

abstract: Irish Research Council (Enterprise Partnership scheme in conjunction with PERL, UCC and UTRC-Ireland)


title: High Resolution Software Defined Radar System for Target Detection.

abstract: The Universal Software Radio Peripheral USRP NI2920, a software defined transceiver so far mainly used in Software Defined Radio applications, is adopted in this work to design a high resolution L-Band Software Defined Radar system. The enhanced available bandwidth, due to the Gigabit Ethernet interface, is exploited to obtain a higher slant-range resolution with respect to the existing Software Defined Radar implementations. A specific Lab VIEW application, performing radar operations, is discussed, and successful validations are presented to demonstrate the accurate target detection capability of the proposed software radar architecture. In particular, outdoor and indoor test are performed by adopting a metal plate as reference structure located at different distances from the designed radar system, and results obtained from the measured echo are successfully processed to accurately reveal the correct target position, with the predicted slant-range resolution equal to 6m.


title: Deep CNNs Along the Time Axis With Intermap Pooling for Robustness to Spectral Variations

abstract: Convolutional neural networks (CNNs) with convolutional and pooling operations along the frequency axis have been proposed to attain invariance to frequency shifts of features. However, this is inappropriate with regard to the fact that acoustic features vary in frequency. In this paper, we contend that convolution along the time axis is more effective. We also propose the addition of an intermap pooling (IMP) layer to deep CNNs. In this layer, filters in each group extract common but spectrally variant features, then the layer pools the feature maps of each group. As a result, the proposed IMP CNN can achieve insensitivity to spectral variations characteristic of different speakers and utterances. The effectiveness of the IMP CNN architecture is demonstrated on several LVCSR tasks. Even without speaker adaptation techniques, the architecture achieved a WER of 12.7% on the SWB part of the Hub5'2000 evaluation test set, which is competitive with other state-of-the-art methods.


Semi-supervised Learning of Naive Bayes Classifier with


title: Criteria-Based Requirements Prioritization for Software Product Management.

abstract: Meeting stakeholders requirements and expectations becomes one of the critical aspects on which any software organization in market-driven environment focus on, and pays a lot of efforts and expenses to maximize the satisfaction of their stakeholders. Therefore identifying the software product release contents becomes one of the critical decisions for software product success. Requirements prioritization refers to that activity through which product releases contents that maximize stakeholder satisfaction can be identified. This makes it one of the most important component of software requirement decision support in incremental software development [8]. This paper illustrates the Criteria-Based requirement prioritization approach for software product management. The technique proposed on this paper designed based on the Hierarchical Cumulative Voting (HCV) and Value-Oriented Prioritization (VOP) techniques. The proposed technique, Value-Oriented HCV (VOHCV) can select the best candidate requirements for each release based on the stakeholders input values for each requirement. These values reflect the importance of each requirement in terms of associated anticipated cost, technical risk, relative impact, market-related aspects and perceived value to the stakeholder. The VOHCV inherits the strengths of the HCV and VOP techniques. It also provides a mechanism that takes different stakeholders’ aspects into account while selecting the best candidate release requirement, to maximize stakeholders’ value and satisfaction [11].


A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages


title: Social media research: Theories, constructs, and conceptual frameworks

abstract: In just one decade, social media have revolutionized the life of many people and thus attracted much attention, not only from industry, but also academia. To understand how researchers have adopted theories, used research constructs, and developed conceptual frameworks in their studies, a systematic and structured literature review based on five leading online academic databases was conducted. A total of 46 articles on social media research were consolidated and analyzed, including empirical studies spanning from 2002 to 2011. A collection of theories/models and constructs/attributes adopted in these articles is summarized and tabulated for easy reference and comprehension of extant research results. A causal-chain framework was developed based on the input-moderator-mediator-output model to illustrate the causality between the research constructs used and the conceptualization of theoretical models/theories proposed by previous researchers. Because social media cover a wide range of research topics, the literature review may not be exhaustive. However, the proposed causal-chain framework and suggested research directions may be regarded as representative references for future research in the subject area. This is believed to be the first comprehensive literature review of social media research, and it contributes to a better understanding of the causes and effects of the adoption and usage of social media.


title: An Evaluation of Popular Copy-Move Forgery Detection Approaches

abstract: A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.


title: An Efficient Pseudonymous Authentication Scheme With Strong Privacy Preservation for Vehicular Communications

abstract: In this paper, we propose an efficient pseudonymous authentication scheme with strong privacy preservation (PASS), for vehicular communications. Unlike traditional pseudonymous authentication schemes, the size of the certificate revocation list (CRL) in PASS is linear with the number of revoked vehicles and unrelated to how many pseudonymous certificates are held by the revoked vehicles. PASS supports the roadside unit (RSU)-aided distributed certificate service that allows the vehicles to update certificates on road, but the service overhead is almost unrelated to the number of updated certificates. Furthermore, PASS provides strong privacy preservation to the vehicles so that the adversaries cannot trace any vehicle, even though all RSUs have been compromised. Extensive simulations demonstrate that PASS outperforms previously reported schemes in terms of the revocation cost and the certificate updating overhead.


title: A Complex Event Processing Toolkit for Detecting Technical Chart Patterns

abstract: With the advent of large high volume data, we have seen need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series data, and they are used by experts in time series data analysis domains such as stock market and currency exchange rates. Yet the automated identification of these patterns is challenging due to the high volatility and noise of data. The paper focuses on identifying suitable technique to filter out volatility and a set of algorithms to query the data streams continuously and to identify patterns. The resulting solution is a toolkit for chart pattern recognition which is a composition of a set of complex CEP queries and a Kernel regression smoother applied on moving windows. Same toolkit can be used to detect chart patterns in other domains like Gold and Oil prices etc as well.


title: Divergent Transcription: A Driving Force for New Gene Origination?

abstract: The mammalian genome is extensively transcribed, a large fraction of which is divergent transcription from promoters and enhancers that is tightly coupled with active gene transcription. Here, we propose that divergent transcription may shape the evolution of the genome by new gene origination.


title: A Lightweight Simulator for Autonomous Driving Motion Planning Development

abstract: A good simulation environment will facilitate motion planning algorithm development for urban autonomous driving. The first requirement of such a simulator is to be able to replicate a complex urban environment, including road network, curb, general objects, etc. The second requirement is to simulate a realistic host vehicle, which includes perception, control and vehicle dynamics, to recreate imperfect inputs and non-accurate execution of the planner. The third requirement is to model traffic participants (other on-road vehicles) for microscopic traffic simulation. Intelligent-agent-based techniques are used to allow the traffic participants to interact with the environment and each other. In this paper, we present an open-source lightweight simulation environment, FastSim, which is designed to meet the three requirements above. Keywords–Traffic simulator; Intelligent agent; Motion Planning; Autonomous driving


title: Decoupled Parallel Backpropagation with Convergence Guarantee

abstract: Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources. Recently, several algorithms have been proposed for breaking the backward locking. However, their performances degrade seriously when networks are deep. In this paper, we propose decoupled parallel backpropagation algorithm for deep learning optimization with convergence guarantee. Firstly, we decouple the backpropagation algorithm using delayed gradients, and show that the backward locking is removed when we split the networks into multiple modules. Then, we utilize decoupled parallel backpropagation in two stochastic methods and prove that our method guarantees convergence to critical points for the non-convex problem. Finally, we perform experiments for training deep convolutional neural networks on benchmark datasets. The experimental results not only confirm our theoretical analysis, but also demonstrate that the proposed method can achieve significant speedup without loss of accuracy.


title: Why Total Quality Management Programs Do Not Persist: The Role of Management Quality and Implications for Leading a TQM Transformation

abstract: Top-down total quality management (TQM) programs often fail to create deep and sustained change in organizations. They become a fad soon replaced by another fad. Failure to institutionalize TQM can be attributed to a gap between top management's rhetoric about their intentions for TQM and the reality of implementation in various subunits of the organization. The gap varies from subunit to subunit due to the quality of management in each. By quality of management is meant the capacity of senior team to (1) develop commitment to the new TQM direction and behave and make decisions that are consistent with it, (2) develop the cross-functional mechanisms, leadership skills, and team culture needed for TQM implementation, and (3) create a climate of open dialogues about progress in the TQM transformation that will enable learning and further change. The TQM transformations will persist only if top management requires and ultimately institutionalizes an honest organizational-wide conversation that surfaces valid data about the quality of management in each subunit of the firm and leads to changes in management quality or replacement of managers.


title: Development of prosthetic arm with pneumatic prosthetic hand and tendon-driven wrist

abstract: Recently, various prosthetic arms have been developed, but few are both attractive and functional. Considering human coexistence, prosthetic arms must be both safe and flexible. In this research, we developed a novel prosthetic arm with a five-fingered prosthetic hand using our original pneumatic actuators and a slender tendon-driven wrist using a wire drive and two small motors. Because the prosthetic hand’s driving source is comprised of small pneumatic actuators, the prosthetic hand is safe when it makes contact with people; it can also operate flexibly. In addition, the arm has a tendon-driven wrist to expand its motion space and to perform many operations. First, we explain the pneumatic hand’s drive mechanism and its tendon-driven wrist. Next, we identify the characteristics of the hand and the wrist and construct a control system for this arm and verify its control performance.


title: Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses.

abstract: Studying communities impacted by traumatic events is often costly, requires swift action to enter the field when disaster strikes, and may be invasive for some traumatized respondents. Typically, individuals are studied after the traumatic event with no baseline data against which to compare their postdisaster responses. Given these challenges, we used longitudinal Twitter data across 3 case studies to examine the impact of violence near or on college campuses in the communities of Isla Vista, CA, Flagstaff, AZ, and Roseburg, OR, compared with control communities, between 2014 and 2015. To identify users likely to live in each community, we sought Twitter accounts local to those communities and downloaded tweets of their respective followers. Tweets were then coded for the presence of event-related negative emotion words using a computerized text analysis method (Linguistic Inquiry and Word Count, LIWC). In Case Study 1, we observed an increase in postevent negative emotion expression among sampled followers after mass violence, and show how patterns of response appear differently based on the timeframe under scrutiny. In Case Study 2, we replicate the pattern of results among users in the control group from Case Study 1 after a campus shooting in that community killed 1 student. In Case Study 3, we replicate this pattern in another group of Twitter users likely to live in a community affected by a mass shooting. We discuss conducting trauma-related research using Twitter data and provide guidance to researchers interested in using Twitter to answer their own research questions in this domain. (PsycINFO Database Record


title: A Man-in-the-Middle attack against OpenDayLight SDN controller

abstract: In this paper, we investigated the vulnerabilities surrounding software-defined networking (SDN). Specifically, we examined the vulnerabilities of OpenDayLight SDN controller. Among all the possible attack vectors, a man-in-the-middle attack using ARP poisoning was successfully launched to intercept the traffic between a client and the OpenDayLight controller. Details of the experiment method, procedures and results were described in this manuscript. To the best of our knowledge, this is the first successful practical attempt to penetrate an SDN controller and be able to capture login credentials of the controller. The significance of this attack should not be taken lightly; once the SDN controller is under the control of the adversary, there will be no security at all for the entire network governed by this controller.


title: Automatic Summarization of News Articles in Mobile Devices

abstract: Smartphones and tablets provide access to the Web anywhere and anytime. Automatic Text Summarization techniques aim to extract the fundamental information in documents. Making automatic summarization work in portable devices is a challenge, in several aspects. This paper presents an automatic summarization application for Android devices. The proposed solution is a multi-feature language independent summarization application targeted at news articles. Several evaluation assessments were conducted and indicate that the proposed solution provides good results.


title: Migration and health: a global public health research priority

abstract: With 244 million international migrants, and significantly more people moving within their country of birth, there is an urgent need to engage with migration at all levels in order to support progress towards global health and development targets. In response to this, the 2nd Global Consultation on Migration and Health– held in Colombo, Sri Lanka in February 2017 – facilitated discussions concerning the role of research in supporting evidence-informed health responses that engage with migration. Drawing on discussions with policy makers, research scholars, civil society, and United Nations agencies held in Colombo, we emphasize the urgent need for quality research on international and domestic (in-country) migration and health to support efforts to achieve the Sustainable Development Goals (SDGs). The SDGs aim to ‘leave no-one behind’ irrespective of their legal status. An ethically sound human rights approach to research that involves engagement across multiple disciplines is required. Researchers need to be sensitive when designing and disseminating research findings as data on migration and health may be misused, both at an individual and population level. We emphasize the importance of creating an ‘enabling environment’ for migration and health research at national, regional and global levels, and call for the development of meaningful linkages – such as through research reference groups – to support evidence-informed inter-sectoral policy and priority setting processes.


title: Efficient rendering of heterogeneous polydisperse granular media

abstract: We address the challenge of efficiently rendering massive assemblies of grains within a forward path-tracing framework. Previous approaches exist for accelerating high-order scattering for a limited, and static, set of granular materials, often requiring scene-dependent precomputation. We significantly expand the admissible regime of granular materials by considering heterogeneous and dynamic granular mixtures with spatially varying grain concentrations, pack rates, and sizes. Our method supports both procedurally generated grain assemblies and dynamic assemblies authored in off-the-shelf particle simulation tools. The key to our speedup lies in two complementary aggregate scattering approximations which we introduced to jointly accelerate construction of short and long light paths. For low-order scattering, we accelerate path construction using novel grain scattering distribution functions (GSDF) which aggregate intra-grain light transport while retaining important grain-level structure. For high-order scattering, we extend prior work on shell transport functions (STF) to support dynamic, heterogeneous mixtures of grains with varying sizes. We do this without a scene-dependent precomputation and show how this can also be used to accelerate light transport in arbitrary continuous heterogeneous media. Our multi-scale rendering automatically minimizes the usage of explicit path tracing to only the first grain along a light path, or can avoid it completely, when appropriate, by switching to our aggregate transport approximations. We demonstrate our technique on animated scenes containing heterogeneous mixtures of various types of grains that could not previously be rendered efficiently. We also compare to previous work on a simpler class of granular assemblies, reporting significant computation savings, often yielding higher accuracy results.


title: PPC: Popularity Prediction Caching in ICN

abstract: Thriving future network conceives embedding the ubiquitous in-network caching. The fine-grained cache behavior reveals an intimate relationship among contents in the same stream—sequenced contents have similar cache behavior. This letter presents a novel cache replacement method named popularity prediction caching (PPC) for chunk-level cache by discovering the relevance among video chunks in information centric network from the perspective of user watching behavior. PPC predicts and caches the future most popular chunks and evicts those with least future popularity in a linear complexity. Simulations in a GEANT model show that PPC outperforms cache policy based on content popularity, least recently used, least frequently used, and first in first out.


title: Aspect-Based Sentiment Analysis of Vietnamese Texts with Deep Learning

abstract: Aspect-based sentiment analysis (ABSA) is one of the most challenging problems in opinion mining especially for the language with a complex structure like Vietnamese. Many studies tackle this problem by separating it into two subtasks: opinion target extraction and sentiment polarity detection. These subtasks generally addressed by rule-based approaches or conventional machine learning approaches with hand-designed features. By contrast, we propose a sequence-labeling scheme associated with bidirectional recurrent neural networks (BRNN) and conditional random field (CRF) to extract opinion target and detect its sentiment simultaneously. Furthermore, we collect and construct a Vietnamese ABSA dataset specifically for smartphone domain. Experiments on this dataset show that BRNN-CRF architecture achieves a satisfied performance, outperforms CRF with hand-designed features. In addition, our framework requires no feature engineering efforts as well as linguistic resources, allows us to adapt to other languages easily.


title: A game theoretic approach to provide incentive and service differentiation in P2P networks

abstract: Traditional peer-to-peer (P2P) networks do not provide service differentiation and incentive for users. Consequently, users can obtain services without themselves contributing any information or service to a P2P community. This leads to the "free-riding" and "tragedy of the commons" problems, in which the majority of information requests are directed towards a small number of P2P nodes willing to share their resources. The objective of this work is to enable service differentiation in a P2P network based on the amount of services each node has provided to its community, thereby encouraging all network nodes to share resources. We first introduce a resource distribution mechanism between all information sharing nodes. The mechanism is driven by a distributed algorithm which has linear time complexity and guarantees Pareto-optimal resource allocation. Besides giving incentive, the mechanism distributes resources in a way that increases the aggregate utility of the whole network. Second, we model the whole resource request and distribution process as a competition game between the competing nodes. We show that this game has a Nash equilibrium and is collusion-proof. To realize the game, we propose a protocol in which all competing nodes interact with the information providing node to reach Nash equilibrium in a dynamic and efficient manner. Experimental results are reported to illustrate that the protocol achieves its service differentiation objective and can induce productive information sharing by rational network nodes. Finally, we show that our protocol can properly adapt to different node arrival and departure events, and to different forms of network congestion.


title: DoA Estimation and Capacity Analysis for 3-D Millimeter Wave Massive-MIMO/FD-MIMO OFDM Systems

abstract: With the promise of meeting future capacity demands, 3-D massive-MIMO/full dimension multiple-input-multiple-output (FD-MIMO) systems have gained much interest in recent years. Apart from the huge spectral efficiency gain, 3-D massive-MIMO/FD-MIMO systems can also lead to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of the system, accurate channel state information is critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for 3-D millimeter wave massive-MIMO orthogonal frequency division multiplexing (OFDM) systems. To be specific, the DoA is estimated using estimation of signal parameter via rotational invariance technique method, and the root mean square error of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system. An ergodic capacity analysis of the system in the presence of DoA estimation error is also conducted, and an optimum power allocation algorithm is derived. Furthermore, it is shown that the DoA-based channel estimation achieves a better performance than the traditional linear minimum mean squared error estimation in terms of ergodic throughput and minimum chordal distance between the subspaces of the downlink precoders obtained from the underlying channel and the estimated channel.


title: An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

abstract: Remarkable research activities and major advances have been occurred over the past decade in multiuser multiple-input multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.


title: Directive Coupled Sectorial Loops Antenna for Ultrawideband Applications

abstract: This letter presents a modified coupled sectorial loops antenna (CSLA) architecture with electrically small dimensions capable of unidirectional radiation over a very wide bandwidth. The proposed antenna is composed of two nonplanar sectorial loops in conjunction with a tapered slot fin in front that bisects the angle between the loops, all backed by a metallic reflector. The metallic reflector produces the required front-to-back ratio, and the tapered slot facilitates forward radiation of high-frequency components. The antenna can fit in a box having dimensions 0.43lambda times 0.32lambda times 0.25lambda, where lambda is the wavelength at the lowest operating frequency. The antenna shows excellent impedance matching over the frequency band 0.5-6 GHz and a stable radiation pattern in the band 0.5-2.25 GHz, with front-to-back ratio better than 10 dB and gain higher than 5 dBi. The antenna was designed, fabricated, and measured. The measured results show good agreement with the simulation results and validate the design accuracy and performance of the antenna. The antenna is proposed for usage in low-frequency, ultrawideband (UWB) radars requiring a compact antenna with relatively high directivity.


title: Satisfaction of Parents of Children with Autism Spectrum Disorder toward Physical Education Teachers

abstract: This study explored the satisfaction of parents of children with autism spectrum disorder (ASD) toward physical education/adapted physical education (PE/APE) teachers using the Parent Perceptions Toward Adapted Physical Education Teachers (PPTAPET) survey (Columna, Cook, Foley, & Bailey, 2014) and examined the influence of related variables. Forty-one parents completed the PPTAPET. Most parents were satisfied or strongly satisfied regarding items associated with communication (n = 23, 56%), qualification (n = 23, 56%), and rapport (n = 25, 61%) subtests. Instructional placement of the child was correlated with communication ( p = .012 ), qualification ( p = .015 ), and rapport ( p = .009 ). In addition, parents reported lower satisfaction in communication ( p = .009 ), qualification ( p = .017 ), and rapport ( p = .011 ) when their child was educated in fully inclusive PE/APE compared to non-fully inclusive PE/APE. These results suggest that teachers should account for parent satisfaction when making decisions for instructional placements for children with ASD. More research is suggested to further analyze the satisfaction of parents of children with ASD. Subscribe to TPE


title: SinFP, unification of active and passive operating system fingerprinting

abstract: The ubiquity of firewalls using Network Address Translation and Port Address Translation (NAT/PAT), stateful inspection, and packet normalization technologies is taking its toll on today’s approaches to operating system fingerprinting. Hence, SinFP was developed attempting to address the limitations of current tools. SinFP implements new methods, like the usage of signatures acquired by active fingerprinting when performing passive fingerprinting. Furthermore, SinFP is the first tool to perform operating system fingerprinting on IPv6 (both active and passive modes). Thanks to its signature matching algorithm, it is almost superfluous to add new signatures to its current database. In addition, its heuristic matching algorithm makes it highly resilient against signatures that have been modified by intermediate routing and/or filtering devices in-between, and against TCP/IP customization methods. This document presents an in-depth explanation of techniques implemented by SinFP tool.


title: The Immediate and Chronic Influence of Spatio-Temporal Metaphors on the Mental Representations of Time in English, Mandarin, and Mandarin-English Speakers

abstract: In this paper we examine whether experience with spatial metaphors for time has an influence on people’s representation of time. In particular we ask whether spatio-temporal metaphors can have both chronic and immediate effects on temporal thinking. In Study 1, we examine the prevalence of ego-moving representations for time in Mandarin speakers, English speakers, and Mandarin-English (ME) bilinguals. As predicted by observations in linguistic analyses, we find that Mandarin speakers are less likely to take an ego-moving perspective than are English speakers. Further, we find that ME bilinguals tested in English are less likely to take an ego-moving perspective than are English monolinguals (an effect of L1 on meaning-making in L2), and also that ME bilinguals tested in Mandarin are more likely to take an ego-moving perspective than are Mandarin monolinguals (an effect of L2 on meaning-making in L1). These findings demonstrate that habits of metaphor use in one language can influence temporal reasoning in another language, suggesting the metaphors can have a chronic effect on patterns in thought. In Study 2 we test Mandarin speakers using either horizontal or vertical metaphors in the immediate context of the task. We find that Mandarin speakers are more likely to construct front-back representations of time when understanding front-back metaphors, and more likely to construct up-down representations of time when understanding up-down metaphors. These findings demonstrate that spatio-temporal metaphors can also have an immediate influence on temporal reasoning. Taken together, these findings demonstrate that the metaphors we use to talk about time have both immediate and long-term consequences for how we conceptualize and reason about this fundamental domain of experience.


title: A critical review of Bitcoins usage by cybercriminals

abstract: Bitcoin is a new form of global digital currency based on peer-to-peer network, enabling a new payment system, and also a completely decentralised cryptocurrency. The P2P network consists of a digital file listing transactions like a ledger, a copy of which is also maintained on every computer on the network, and the transactions are also broadcasted in the public ledger of the Bitcoin network. Anonymity is the core feature that makes Bitcoin popular among people around the world. This feature is attained through the Bitcoin addresses in the public ledger, which represents the users in the Bitcoin network. Because of the illicit use of Bitcoins, the level of anonymity has reduced even though the users are still using the anonymizers like TOR to keep the anonymity stronger to connect to the Bitcoin network. In this paper, we analyse the complete process of transaction of Bitcoins and the anonymity thatlies in that process. The study also focuses on finding the forensic artefacts and the investigative way of approach towards the Bitcoin using forensic tools. The forensic tools are used to analyse the web browser activities, local drive, hard disk image, cookies, downloads and session data related to Bitcoins. The attacker can relate the transaction of the users and can control the Bitcoin blocks by even delaying the transactions. This research will focus on the methods in which the memory and even mobile devices involved in the transaction could be captured and analysed.


Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning


title: The Impact and Challenges of Cloud Computing Adoption on Public Universities in Southwestern Nigeria

abstract: This study investigates the impact and challenges of the adoption of cloud computing by public universities in the Southwestern part of Nigeria. A sample size of 100 IT staff, 50 para-IT staff and 50 students were selected in each university using stratified sampling techniques with the aid of well-structured questionnaires. Microsoft excel was used to capture the data while frequency and percentage distributions were used to analyze it. In all, 2, 000 copies of the questionnaire were administered to the ten (10) public universities in the southwestern part of Nigeria while 1742 copies were returned which represents a respondent rate of 87.1%. The result of the findings revealed that the adoption of cloud computing has a significant impact on cost effectiveness, enhanced availability, low environmental impact, reduced IT complexities, mobility, scalability, increased operability and reduced investment in physical asset However, the major challenges confronting the adoption of cloud are data insecurity, regulatory compliance concerns, lock-in and privacy concerns. This paper concludes by recommending strategies to manage the identified challenges in the study area.


title: A piezoelectric vibration based generator for wireless electronics

abstract: Enabling technologies for wireless sensor networks have gained considerable attention in research communities over the past few years. It is highly desirable, even necessary in certain situations, for wireless sensor nodes to be self-powered. With this goal in mind, a vibration based piezoelectric generator has been developed as an enabling technology for wireless sensor networks. The focus of this paper is to discuss the modeling, design, and optimization of a piezoelectric generator based on a two-layer bending element. An analytical model of the generator has been developed and validated. In addition to providing intuitive design insight, the model has been used as the basis for design optimization. Designs of 1 cm3 in size generated using the model have demonstrated a power output of 375 µW from a vibration source of 2.5 m s−2 at 120 Hz. Furthermore, a 1 cm3 generator has been used to power a custom designed 1.9 GHz radio transmitter from the same vibration source.


title: More than Bag-of-Words: Sentence-based Document Representation for Sentiment Analysis

abstract: Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document representation as their basis. In this paper, we examine whether a more fine-grained representation of documents as sequences of emotionally-annotated sentences can increase document classification accuracy. Experiments conducted on a sentence and document level annotated corpus show that the proposed solution, combined with BoW features, offers an increase in classification accuracy.


title: What Makes Live Events Engaging on Facebook Live, Periscope, and Snapchat

abstract: Live streaming platforms bring events from all around the world to people's computing devices. We conducted a mixed methods study including interviews (N = 42) and a survey (N = 223) to understand how people currently experience events using Facebook Live, Periscope, and Snapchat Live Stories. We identified four dimensions that make remote event viewing engaging: immersion, immediacy, interaction, and sociality. We find that both live streams and the more curated event content found on Snapchat are immersive and immediate, yet Snapchat Live Stories enable quickly switching among different views of the event. Live streams, on the other hand, offer real time interaction and sociality in a way that Snapchat Live Stories do not. However, the interaction's impact depends on comment volume, comment content, and relationship between viewer and broadcaster. We describe how people experience events remotely using these social media, and identify design opportunities around detecting exciting content, leveraging multiple viewpoints, and enabling interactivity to create engaging user experiences for remotely participating in events.


title: Closed-form solution of absolute orientation using unit quaternions

abstract: Finding the relationship between two coordinate systems using pairs of measurements of the coordinates of a number of points in both systems is a classic photogrammetric task . It finds applications i n stereoph and in robotics . I present here a closed-form solution to the least-squares problem for three or more paints . Currently various empirical, graphical, and numerical iterative methods are in use . Derivation of the solution i s simplified by use of unit quaternions to represent rotation . I emphasize a symmetry property that a solution to thi s problem ought to possess . The best translational offset is the difference between the centroid of the coordinates i n one system and the rotated and scaled centroid of the coordinates in the other system . The best scale is equal to th e ratio of the root-mean-square deviations of the coordinates in the two systems from their respective centroids . These exact results are to be preferred to approximate methods based on measurements of a few selected points . The unit quaternion representing the best rotation is the eigenvector associated with the most positive eigenvalue o f a symmetric 4 X 4 matrix . The elements of this matrix are combinations of sums of products of correspondin g coordinates of the points .


title: Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

abstract: Reference genomes are required to understand the diverse roles of microorganisms in ecology, evolution, human and animal health, but most species remain uncultured. Here we present a sequence composition-independent approach to recover high-quality microbial genomes from deeply sequenced metagenomes. Multiple metagenomes of the same community, which differ in relative population abundances, were used to assemble 31 bacterial genomes, including rare (<1% relative abundance) species, from an activated sludge bioreactor. Twelve genomes were assembled into complete or near-complete chromosomes. Four belong to the candidate bacterial phylum TM7 and represent the most complete genomes for this phylum to date (relative abundances, 0.06-1.58%). Reanalysis of published metagenomes reveals that differential coverage binning facilitates recovery of more complete and higher fidelity genome bins than other currently used methods, which are primarily based on sequence composition. This approach will be an important addition to the standard metagenome toolbox and greatly improve access to genomes of uncultured microorganisms.


title: Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system

abstract: The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multi-variable control system but the NNPID controller that uses the conventional gradient descent-learning algorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, includeing a controller structure improvement and a modified extended Kalman filter (EKF) learning algorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.


title: VISIONS OF SOCIAL MEDIA: SURFACING SCHEMAS FROM FIRMS' INFORMATIONAL ENGAGEMENTS

abstract: Prior research has noted the salience of informational engagements that culminate in an organizing vision to the diffusion of technology and the value firms garner from the technology. In contrast to sociological theories that view meaning as inherently pluralistic, information technology research has espoused a single community-level organizing vision. Drawing upon schema theory, we posit that different schemas or organizing visions underlie firms’ informational engagements. We consider the constitutive cognitive elements of these schemas in terms of six evaluative principles derived from six prototypical “orders of worth.” We apply relational class analysis to surface the schemas reflected in informational engagements about 675 social media initiatives by 26 of the top 30 Fortune firms. Contrary to extant perspectives on organizing vision, we identified four disparate schemas or visions of social media and observed firms’ informational engagements typically to reflect multiple visions.


title: Distributed Class-J Power Amplifiers

abstract: This paper presents the design and implementation of a distributed class-J power amplifier (DJPA) in a 0.25- $\mu \text{m}$ AlGaAs–InGaAs pHEMT technology. Class-J mode of operation is introduced in design of distributed power amplifiers (DPAs) to achieve high power added efficiencies (PAEs) over wide frequency ranges. Extensive load–pull (LP) and source–pull (SP) simulations are performed to show that class-J PAs are less sensitive to proper termination of higher order harmonics, and high PAE and output power can be obtained even if the second, third, fourth, and fifth harmonics comprise a real impedance. This is essential in DPAs as the higher order harmonics of the frequencies at lower side of the band may fall in the desired bandwidth. Based on LP and SP observations, a proof-of-concept 1.5–10-GHz monolithic microwave integrated circuit DJPA is designed and fabricated. Chip dimensions are $2.15\times 2.15$ mm2, and 29–30.7-dBm output power with 33–44% PAE is achieved over the frequency range. The large-signal power gain is greater than 10 dB while the PA maintains an average small-signal gain of 13 dB over the bandwidth.