title: Outlier detection for high dimensional data

abstract: The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimensional domains in which the data can contain hundreds of dimensions. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. However, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective of proximity-based definitions. Consequently, for high dimensional data, the notion of finding meaningful outliers becomes substantially more complex and non-obvious. In this paper, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.


title: Optimality principles in sensorimotor control

abstract: The sensorimotor system is a product of evolution, development, learning and adaptation-which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.


title: A Closer Look at Amphetamine-Induced Reverse Transport and Trafficking of the Dopamine and Norepinephrine Transporters

abstract: Amphetamine (AMPH) and its derivatives are regularly used in the treatment of a wide array of disorders such as attention-deficit hyperactivity disorder (ADHD), obesity, traumatic brain injury, and narcolepsy (Prog Neurobiol 75:406–433, 2005; J Am Med Assoc 105:2051–2054, 1935; J Am Acad Child Adolesc Psychiatry 41:514–521, 2002; Neuron 43:261–269, 2004; Annu Rev Pharmacol Toxicol 47:681–698, 2007; Drugs Aging 21:67–79, 2004). Despite the important medicinal role for AMPH, it is more widely known for its psychostimulant and addictive properties as a drug of abuse. The primary molecular targets of AMPH are both the vesicular monoamine transporters (VMATs) and plasma membrane monoamine—dopamine (DA), norepinephrine (NE), and serotonin (5-HT)—transporters. The rewarding and addicting properties of AMPH rely on its ability to act as a substrate for these transporters and ultimately increase extracellular levels of monoamines. AMPH achieves this elevation in extracellular levels of neurotransmitter by inducing synaptic vesicle depletion, which increases intracellular monoamine levels, and also by promoting reverse transport (efflux) through plasma membrane monoamine transporters (J Biol Chem 237:2311–2317, 1962; Med Exp Int J Exp Med 6:47–53, 1962; Neuron 19:1271–1283, 1997; J Physiol 144:314–336, 1958; J Neurosci 18:1979–1986, 1998; Science 237:1219–1223, 1987; J Neurosc 15:4102–4108, 1995). This review will focus on two important aspects of AMPH-induced regulation of the plasma membrane monoamine transporters—transporter mediated monoamine efflux and transporter trafficking.


Sentiment Analysis of Reviews


title: CHAPTER THREE A Reciprocal Influence Model of Social Power: Emerging Principles

abstract: Abstract In the present chapter, we advance a reciprocal influence model of social power. Our model is rooted in evolutionist analyses of primate hierarchies, and notions that the capacity for subordinates to form alliances imposes important demands upon those in power, and that power heuristically reduces the likelihood of conflicts within groups. Guided by these assumptions, we posit a set of propositions regarding the reciprocal nature of power, and review recent supporting data. With respect to the acquisition of social power, we show that power is afforded to those individuals and strategic behaviors related to advancing the interests of the group. With respect to constraints upon power, we detail how group‐based representations (a fellow group member's reputation), communication (gossip), and self‐assessments (an individual's modest sense of power) constrain the actions of those in power according to how they advance group interests. Finally, with respect to the notion that power acts as a social interaction heuristic, we examine how social power is readily and accurately perceived by group members and gives priority to the emotions, goals, and actions of high‐power individuals in shaping interdependent action. We conclude with a discussion of recent studies of the subjective sense of power and class‐based ideologies.


title: Smart grid cooperative communication with smart relay

abstract: Many studies have investigated the smart grid architecture and communication models in the past few years. However, the communication model and architecture for a smart grid still remain unclear. Today's electric power distribution is very complex and maladapted because of the lack of efficient and cost-effective energy generation, distribution, and consumption management systems. A wireless smart grid communication system can playan important role in achieving these goals. In thispaper, we describe a smart grid communication architecture in which we merge customers and distributors into a single domain. In the proposed architecture, all the home area networks, neighborhood area networks, and local electrical equipment form a local wireless mesh network (LWMN). Each device or meter can act as a source, router, or relay. The data generated in any node (device/meter) reaches the data collector via other nodes. The data collector transmits this data via the access point of a wide area network (WAN). Finally, data is transferred to the service provider or to the control center of the smart grid. We propose a wireless cooperative communication model for the LWMN. We deploy a limited number of smart relays to improve the performance of the network. A novel relay selection mechanism is also proposed to reduce the relay selection overhead. Simulation results show that our cooperative smart grid (coopSG) communication model improves the end-to-end packet delivery latency, throughput, and energy efficiency over both the Wang et al. and Niyato et al. models.


title: Optimal Power Flow Management for Grid Connected PV Systems With Batteries

abstract: This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.


title: Blockchain Router: A Cross-Chain Communication Protocol

abstract: Cross-chain communication is one of the major design considerations in current blockchain systems [4-7] such as Ethereum[8]. Currently, Blockchain operates like information isolated island, they cannot obtain external data or execute transactions on their own. Motivated by recent studies [1-3] on blockchain's multiChain framework, we investigate the cross-chain communication. We introduces blockchain router, which empowers blockchains to connect and communicate cross chains. By establishing an economic model, blockchain router enables different blockchains in the network communicate with each other same like Internet network. In the network of blockchain router, some blockchain plays the role of a router which, according to the communication protocol, analyzes and transmits communication requests, dynamically maintaining a topology structure of the blockchain network.


title: "Good job, you're so smart": The effects of inconsistency of praise type on young children's motivation.

abstract: Abstract Previous research has demonstrated that generic praise (“good drawer”) is related to children giving up after failure because failure implies the lack of a critical trait (e.g., drawing ability). Conversely, nongeneric praise (“good job drawing”) is related to mastery motivation because it implies that success is related to effort. Yet children may receive a mixture of these praise types (i.e., inconsistent praise), the effects of which are unclear. We tested how inconsistent praise influenced two components of motivation: self-evaluation and persistence. Kindergarteners (N = 135) were randomly assigned to one of five conditions in which consistency of praise type was varied. After two failure scenarios, children reported self-evaluations and persistence. Results indicated that more nongeneric praise related linearly to greater motivation, yet self-evaluation and persistence were impacted differently by inconsistent praise types. Hearing even a small amount of generic praise reduced persistence, whereas hearing a small amount of nongeneric praise preserved self-evaluation.


title: Lambda architecture for cost-effective batch and speed big data processing

abstract: Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis ‘on-the-fly’, as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.


title: Simrank++: Query rewriting through link analysis of the click graph

abstract: We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank [7] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits "evidence." We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries from Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.


title: Anticipation of monetary gain but not loss in healthy older adults

abstract: Although global declines in structure have been documented in the aging human brain, little is known about the functional integrity of the striatum and prefrontal cortex in older adults during incentive processing. We used event-related functional magnetic resonance imaging to determine whether younger and older adults differed in both self-reported and neural responsiveness to anticipated monetary gains and losses. The present study provides evidence for intact striatal and insular activation during gain anticipation with age, but shows a relative reduction in activation during loss anticipation. These findings suggest that there is an asymmetry in the processing of gains and losses in older adults that may have implications for decision-making.


title: A Monte-Carlo approach for ghost avoidance in the Ms. Pac-Man game

abstract: Ms. Pac-Man is a challenging, classic arcade game that provides an interesting platform for Artificial Intelligence (AI) research. This paper reports the first Monte-Carlo approach to develop a ghost avoidance module of an intelligent agent that plays the game. Our experimental results show that the look-ahead ability of Monte-Carlo simulation often prevents Ms. Pac-Man being trapped by ghosts and reduces the chance of losing Ms. Pac-Man's life significantly. Our intelligent agent has achieved a high score of around 21,000. It is sometimes capable of clearing the first three stages and playing at the level of a novice human player.


title: Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering

abstract: This paper presents algorithms and a prototype system for hand tracking and hand posture recognition. Hand postures are represented in terms of hierarchies of multi-scale colour image features at different scales, with qualitative inter-relations in terms of scale, position and orientation. In each image, detection of multi-scale colour features is performed. Hand states are then simultaneously detected and tracked using particle filtering, with an extension of layered sampling referred to as hierarchical layered sampling. Experiments are presented showing that the performance of the system is substantially improved by performing feature detection in colour space and including a prior with respect to skin colour. These components have been integrated into a real-time prototype system, applied to a test problem of controlling consumer electronics using hand gestures. In a simplified demo scenario, this system has been successfully tested by participants at two fairs during 2001.


title: An Extended Filtered Channel Framework for Pedestrian Detection

abstract: Pedestrian detection is an important example of object detection and has attracted much attention. Many works have shown that good image features provide high detection accuracy, and a few works have investigated enhancing low-level features (e.g., gradient and color features) using a filtered layer (i.e., convolutional layer) to obtain enhanced features or filtered channel features. To investigate whether these features are saturated, this paper adopts the concept of filtered channel features and strengthens them by adding more convolutional layers. Acting as convolution kernels, multilayer filters are applied to low-level features to obtain the extended filtered channel features, providing a powerful feature extractor with multilayer transformation for pedestrian detection. The proposed extended filtered channel framework (ExtFCF) achieves competitive performance on widely used benchmark datasets (Caltech, INRIA, and KITTI datasets), using only histogram of oriented gradient (HOG) and CIE-LUV [a color space composing of luminance (L) and two chrominance (UV) components by International Commission on Illumination] color features (HOG+LUV) as low-level features. One representative ExtFCF implementation achieves the best result compared with the current best traditional pedestrian detection methods on the Caltech dataset.


title: A Two Phase Deep Learning Model for Identifying Discrimination from Tweets

abstract: Discrimination discovery is the data mining problem of unveiling discriminatory practices by analyzing a dataset of historical decision records. In this paper, we focus on discovering discrimination from tweets using deep learning models. One challenge here is that it is dicult to obtain a large well-labeled dataset required by the training of deep learning models for the purpose of discrimination analysis. We develop a two-phase deep learning model to address this challenge. Our model rst learns text representations based on weakly-labeled tweets (containing some specic hashtags), then trains the classier


title: Automated detection of euro pallet loads by interpreting PMD camera depth images

abstract: In this study, a novel approach for the detection of parcel loading positions on a pallet is presented. This approach was realized as a substantial change in comparison with traditional system design of contour detection in de-palletizing processes. Complex 3D-vision systems, costly laser scanners or throughput decreasing local sensor solutions integrated in grippers are substituted by a low-cost photonic mixing device (PMD) camera. By combining PMD technology and a predetermined model of loading situations, stored during assembling the pallet, this approach can compensate for the drawbacks of each respective system. An essential part of the approach are computer-graphics methods specific to the given problem to both detect the deviation between the nominal and the actual loading position and if necessary an automated correction of the packaging scheme. From an economic point of view, this approach can decrease the costs of mandatory contour checking in automated de-palletizing processes.


title: Scalable algorithms for scholarly figure mining and semantics

abstract: Most scholarly papers contain one or multiple figures. Often these figures show experimental results, e.g, line graphs are used to compare various methods. Compared to the text of the paper, figures and their semantics have received relatively less attention. This has significantly limited semantic search capabilities in scholarly search engines. Here, we report scalable algorithms for generating semantic metadata for figures. Our system has four sequential modules: 1. Extraction of figure, caption and mention; 2. Binary classification of figures as compound (contains sub-figures) or not; 3. Three class classification of non compound figures as line graph, bar graph or others; and 4. Automatic processing of line graphs to generate a textual summary. In each step a metadata file is generated, each having richer information than the previous one. The algorithms are scalable yet each individual step has an accuracy greater than 80%.


Game-based Analysis of the Network Attack-Defense Interaction


title: Smart home research

abstract: This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.


title: Mapping Text with Phrase Nets

abstract: We present a new technique, the phrase net, for generating visual overviews of unstructured text. A phrase net displays a graph whose nodes are words and whose edges indicate that two words are linked by a user-specified relation. These relations may be defined either at the syntactic or lexical level; different relations often produce very different perspectives on the same text. Taken together, these perspectives often provide an illuminating visual overview of the key concepts and relations in a document or set of documents.


Maximum matching and a polyhedron with 0,1-vertices


title: Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation

abstract: Abstract Conventional neural networks have been demonstrated to be a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known Self-Organizing Background Subtraction (SOBS) method and its variants based on neural networks have long been the leading methods on the large-scale CDnet 2012 dataset during a long time. Convolutional neural networks, which are used in deep learning, have been recently and excessively employed for background initialization, foreground detection, and deep learned features. The top background subtraction methods currently used in CDnet 2014 are based on deep neural networks, and have demonstrated a large performance improvement in comparison to conventional unsupervised approaches based on multi-feature or multi-cue strategies. Furthermore, since the seminal work of Braham and Van Droogenbroeck in 2016, a large number of studies on convolutional neural networks applied to background subtraction have been published, and a continual gain of performance has been achieved. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. To do so, we first surveyed the background initialization and background subtraction methods based on deep neural networks concepts, and also deep learned features. We then discuss the adequacy of deep neural networks for the task of background subtraction. Finally, experimental results are presented for the CDnet 2014 dataset.


title: Children’s imitation of causal action sequences is influenced by statistical and pedagogical evidence

abstract: Abstract Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naive demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.


title: Manipulating Liquid Metal Droplets in Microfluidic Channels With Minimized Skin Residues Toward Tunable RF Applications

abstract: A nontoxic liquid metal, such as eutectic gallium-indium (EGaIn) alloy, has been used to develop tunable radio frequency (RF) components, such as antennas, inductors, or capacitors, for enabling large tunable range, better linearity, and low loss, using fluidic displacement of the liquid metal. However, EGaIn residue, due to its fast oxidation, limits multiple movement of the EGaIn in the reconfigurable RF components. This paper focuses on the use of surfactants, carrier liquids, and microchannel coating materials that minimize EGaIn fragmentation and EGaIn residues on poly(dimethylsiloxane) (PDMS)-based microfluidic channels during repeated actuation of an EGaIn plug. Using a combination of carrier liquids and microchannel coating materials to minimize EGaIn from leaving residues on the PDMS microfluidic channel, a microstrip transmission line switch as a proof-of-concept reconfigurable RF application using the EGaIn plug is demonstrated. It is switched ON<4 dB and OFF with a loss of <18 dB over the frequency range between 4 and 15 GHz. [2014-0228]


title: Comparison of single-phase T-type multilevel converters for grid-connected PV systems

abstract: Single-phase photovoltaic (PV) energy conversion systems are the main solution for small-scale rooftop PV applications. Some multilevel topologies have been commercialized for PV systems and an they are attractive alternative to implement small-scale rooftop PV applications. Efficiency, reliability, power quality and power losses are important concepts to consider in PV converters. For this reason this paper presents a comparison of four multilevel converter based in the T-type topology proposed by Conergy. The presented control scheme is based in single-phase voltage oriented control and simulation results are presented to provide a preliminary validation of each topology. Finally a summary table with the different features of the converters is provided.


A Turkish-German Code-Switching Corpus.


title: Evolving Signal Processing for Brain–Computer Interfaces

abstract: Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.


title: Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

abstract: ©2016 Association for Computational Linguistics. Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.


title: Neural systems for recognizing emotion

abstract: Abstract Recognition of emotion draws on a distributed set of structures that include the occipitotemporal neocortex, amygdala, orbitofrontal cortex and right frontoparietal cortices. Recognition of fear may draw especially on the amygdala and the detection of disgust may rely on the insula and basal ganglia. Two important mechanisms for recognition of emotions are the construction of a simulation of the observed emotion in the perceiver, and the modulation of sensory cortices via top-down influences.


title: DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing

abstract: In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving. Despite great success achieved in various human intelligence tasks, similar to traditional software, DNNs could also exhibit incorrect behaviors caused by hidden defects causing severe accidents and losses. In this paper, we propose DeepHunter, an automated fuzz testing framework for hunting potential defects of general-purpose DNNs. DeepHunter performs metamorphic mutation to generate new semantically preserved tests, and leverages multiple plugable coverage criteria as feedback to guide the test generation from different perspectives. To be scalable towards practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and prioritizes the tests selection based on active feedback. The effectiveness of DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10, ImageNet) and 7 DNNs with diverse complexities, under a large set of 6 coverage criteria as feedback. The large-scale experiments demonstrate that DeepHunter can (1) significantly boost the coverage with guidance; (2) generate useful tests to detect erroneous behaviors and facilitate the DNN model quality evaluation; (3) accurately capture potential defects during DNN quantization for platform migration.


title: Wideband in-phase power divider using cascaded parallel coupled microstrip lines

abstract: A compact wideband in-phase power divider is presented. The proposed device utilizes parallel coupled microstrip lines, connected in cascaded configurations, and a stepped impedance T-junction of microstrip line. To achieve tight coupling for wideband performance, a slotted ground is utilized under each of the parallel coupled lines. Moreover, chip capacitors are connected between the coupled lines to enhance the coupling. To improve isolation between the output ports, an isolation resistor is added between the microstrip lines that are connected to the output ports just before the coupled lines. The proposed structure provides 100% fractional bandwidth based on 15 dB isolation as the reference. The simulated results of the proposed divider show equal power division with less than 0.5 dB additional insertion loss and more than 13 dB return loss at all the ports with less than 1° phase imbalance across the band 1–3 GHz.


title: Towards cloud based big data analytics for smart future cities

abstract: A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.


title: Computationally Efficient Approaches for Image Style Transfer

abstract: In this work, our focus is on developing fast image style transfer architectures for practical applications. We have proposed three modifications to the architecture of a recent, real-time, artistic style transfer technique to make it computationally more efficient. We have proposed the use of depth-wise separable convolution (DepSep) in place of convolution and nearest neighbor (NN) interpolation in place of transposed convolution. We have also explored the concatenation of nearest neighbour and bilinear (Bil) interpolations in place of transposed convolution. The stylized images from the modified architectures are perceptually similar in quality to those from the original architecture. The decrease in the computational complexity of our architectures is validated by the decrease in the testing time by 26.1%, 39.1%, and 57.1%, respectively, for DepSep, DepSep-NN-Bil and DepSep-NN modifications. Working with another architecture, we have examined how the quality of the stylized reconstruction changes with the change of the loss function to be minimized.


title: Substrate-Integrated-Waveguide Circulators Suitable for Millimeter-Wave Integration

abstract: Over the years, many millimeter-wave circulator techniques have been presented, such as nonradiative dielectric and fin-line circulators. Although excellent results have been demonstrated in the literature, their proliferation in commercial devices has been hindered by complex assembly cost. This paper presents a study of substrate-integrated millimeter-wave degree-2 circulators. Although the substrate integrated-circuits technique may be applied to virtually any planar transmission medium, the one adopted in this paper is the substrate integrated waveguide (SIW). Two design configurations are possible: a planar one that is suitable for thin substrate materials and a turnstile one for thicker substrate materials. The turnstile circulator is ideal for systems where the conductor losses associated with the thin SIW cannot be tolerated. The design methodology adopted in this paper is to characterize the complex gyrator circuit as a preamble to design. This is done via a commercial finite-element package


title: Mobile-health : A review of current state in 2015

abstract: Display Omitted An extensive review of the state of the art on m-Health and related approaches.Study of the scientific developments/break-through on m-Health.Analysis of the top mobile health applications in the top mobile markets.Discussion about current and open issues on m-Health services and technologies. Health telematics is a growing up issue that is becoming a major improvement on patient lives, especially in elderly, disabled, and chronically ill. In recent years, information and communication technologies improvements, along with mobile Internet, offering anywhere and anytime connectivity, play a key role on modern healthcare solutions. In this context, mobile health (m-Health) delivers healthcare services, overcoming geographical, temporal, and even organizational barriers. M-Health solutions address emerging problems on health services, including, the increasing number of chronic diseases related to lifestyle, high costs of existing national health services, the need to empower patients and families to self-care and handle their own healthcare, and the need to provide direct access to health services, regardless of time and place. Then, this paper presents a comprehensive review of the state of the art on m-Health services and applications. It surveys the most significant research work and presents a deep analysis of the top and novel m-Health services and applications proposed by industry. A discussion considering the European Union and United States approaches addressing the m-Health paradigm and directives already published is also considered. Open and challenging issues on emerging m-Health solutions are proposed for further works.


title: A survey on novelty detection using level set methods

abstract: The process of mining includes various methodologies and data classification is one of the advantageous methods involved in it. It not only eases the process of machine learning but also gives a platform for proper functioning of the process. There are cases wherein the data which is important or unidentified is missed during the process of classification. The process of mining is highly affected by the absence of such kind of data. The process of extraction of unknown and new data from the huge dataset that has been left during the classification process is known as novelty detection. This paper reviews the identification of novelty data that is used during the process of classification. Novelty detection generally focuses on the identification of shapes or patterns, space and learning along with density taxonomy models. This paper aims to consider the most typically used ideas in modeling the novelty data to classify and compare the schemes discussed in existing literature as well as to provide useful material for existing state and challenges for the same.


title: Training Neural Nets with the Reactive Tabu Search

abstract: In this paper the task of training subsymbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the reactive tabu search (RTS). An iterative optimization process based on a "modified local search" component is complemented with a meta-strategy to realize a discrete dynamical system that discourages limit cycles and the confinement of the search trajectory in a limited portion of the search space. The possible cycles are discouraged by prohibiting (i.e., making tabu) the execution of moves that reverse the ones applied in the most recent part of the search. The prohibition period is adapted in an automated way. The confinement is avoided and a proper exploration is obtained by activating a diversification strategy when too many configurations are repeated excessively often. The RTS method is applicable to nondifferentiable functions, is robust with respect to the random initialization, and effective in continuing the search after local minima. Three tests of the technique on feedforward and feedback systems are presented. >


title: Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization

abstract: This paper proposes a new approach using improved particle swarm optimization (PSO) to optimize the path of a mobile robot through an environment containing static obstacles. Relative to many optimization methods that produce nonsmooth paths, the PSO method developed in this paper can generate smooth paths, which are more preferable for designing continuous control technologies to realize path following using mobile robots. To reduce computational cost of optimization, the stochastic PSO (S-PSO) with high exploration ability is developed, so that a swarm with small size can accomplish path planning. Simulation results validate the proposed algorithm in a mobile robot path planning.


title: Blind image quality assessment based on aesthetic and statistical quality-aware features

abstract: The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.


ESP: Labeling Images with a Computer Game


title: Information Dynamics based Self-Adaptive Reservoir for Delay Temporal Memory Tasks

abstract: Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory. Specifically for delayed response tasks involving the transient memorization of information (temporal memory), self-adaptation in RC is crucial for generalization to varying delays. In this work using information theory, we combine a generalized intrinsic plasticity rule with a local information dynamics based schema of reservoir neuron leak adaptation. This allows the RC network to be optimized in a self-adaptive manner with minimal parameter tuning. Local active information storage, measured as the degree of influence of previous activity on the next time step activity of a neuron, is used to modify its leak-rate. This results in RC network with non-uniform leak rate which depends on the time scales of the incoming input. Intrinsic plasticity (IP) is aimed at maximizing the mutual information between each neuron’s input and output while maintaining a mean level of activity (homeostasis). Experimental results on two standard benchmark tasks confirm the extended performance of this system as compared to the static RC (fixed leak and no IP) and RC with only IP. In addition, using both a simulated wheeled robot and a more complex physical hexapod robot, we demonstrate the ability of the system to achieve long temporal memory for solving a basic T-shaped maze navigation task with varying delay time scale.


title: Architecture Complexity and Energy Efficiency of Small Wind Turbines

abstract: The power characteristics of wind turbines are nonlinear. It is particularly true for vertical-axis turbines whose provided power is very sensitive to the load. Thus, controlling the operating point is essential to optimize the energetic behavior. Several control strategies (maximum power point tracking) can be used for the energy conversion. If the wind-turbine characteristic Cp(lambda) is supposed to be a priori known, it can be used for optimal control of the torque, speed, or system output power. On the contrary, if this characteristic is unknown, an operational seeking algorithm such as fuzzy logic has to be implemented. Several structures with different associated complexity degrees can be used, in particular, the structure of the ac-dc conversion, which can be either a pulsewidth-modulation voltage-source rectifier or a simple diode bridge. A comparative study of the corresponding control strategies and architectures is proposed in this paper regarding the tradeoffs between structure complexity and energy efficiency. The analysis is based on simulations and experiments


title: Multi-level Attention Networks for Visual Question Answering

abstract: Inspired by the recent success of text-based question answering, visual question answering (VQA) is proposed to automatically answer natural language questions with the reference to a given image. Compared with text-based QA, VQA is more challenging because the reasoning process on visual domain needs both effective semantic embedding and fine-grained visual understanding. Existing approaches predominantly infer answers from the abstract low-level visual features, while neglecting the modeling of high-level image semantics and the rich spatial context of regions. To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention. First, we generate semantic concepts from high-level semantics in convolutional neural networks (CNN) and select those question-related concepts as semantic attention. Second, we encode region-based middle-level outputs from CNN into spatially-embedded representation by a bidirectional recurrent neural network, and further pinpoint the answer-related regions by multiple layer perceptron as visual attention. Third, we jointly optimize semantic attention, visual attention and question embedding by a softmax classifier to infer the final answer. Extensive experiments show the proposed approach outperforms the-state-of-arts on two challenging VQA datasets.


title: MiBench: A free, commercially representative embedded benchmark suite

abstract: This paper examines a set of commercially representative embedded programs and compares them to an existing benchmark suite, SPEC2000. A new version of SimpleScalar that has been adapted to the ARM instruction set is used to characterize the performance of the benchmarks using configurations similar to current and next generation embedded processors. Several characteristics distinguish the representative embedded programs from the existing SPEC benchmarks including instruction distribution, memory behavior, and available parallelism. The embedded benchmarks, called MiBench, are freely available to all researchers.


title: Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning

abstract: Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets.


title: Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review

abstract: Wireless Body Area Networks (WBANs) have emerged as a promising technology that has shown enormous potential in improving the quality of healthcare, and has thus found a broad range of medical applications from ubiquitous health monitoring to emergency medical response systems. The huge amount of highly sensitive data collected and generated by WBAN nodes requires an ascendable and secure storage and processing infrastructure. Given the limited resources of WBAN nodes for storage and processing, the integration of WBANs and cloud computing may provide a powerful solution. However, despite the benefits of cloud-assisted WBAN, several security issues and challenges remain. Among these, data availability is the most nagging security issue. The most serious threat to data availability is a distributed denial of service (DDoS) attack that directly affects the all-time availability of a patient's data. The existing solutions for standalone WBANs and sensor networks are not applicable in the cloud. The purpose of this review paper is to identify the most threatening types of DDoS attacks affecting the availability of a cloud-assisted WBAN and review the state-of-the-art detection mechanisms for the identified DDoS attacks.


title: Current Sensorless Equalization Strategy for a Single-Switch Voltage Equalizer Using Multistacked Buck–Boost Converters for Photovoltaic Modules Under Partial Shading

abstract: Differential power processing converters and voltage equalizers have been proposed and used for photovoltaic strings comprising multiple modules/substrings connected in series in order to preclude negative influences of partial shading. The single-switch voltage equalizer using multistacked buck–boost converters can significantly reduce the necessary switch count compared to that of conventional topologies, achieving simplified circuitry. However, multiple current sensors are necessary for this single-switch equalizer to effectively perform equalization. In this paper, a current sensorless equalization technique ( ΔV -controlled equalization) is presented. The equalization strategy using the ΔV -controlled equalization is explained and discussed on the basis of comparison with current-controlled equalization strategies. Experimental equalization tests emulating partial-shading conditions were performed using the single-switch equalizer employing the ΔV -controlled equalization. Negative impacts of partial shading were successfully precluded, demonstrating the efficacy of the proposed ΔV -controlled equalization strategy.


Face Recognition Based on the Combination Method of Multiple Classifier


title: Identification of Consumer Adverse Drug Reaction Messages on Social Media

abstract: The prevalence of social media has resulted in spikes of data on the Internet which can have potential use to assist in many aspects of human life. One prospective use of the data is in the development of an early warning system to monitor consumer Adverse Drug Reactions (ADRs). The direct reporting of ADRs by consumers is playing an increasingly important role in the world of pharmacovigilance. Social media provides patients a platform to exchange their experiences regarding the use of certain drugs. However, the messages posted on those social media networks contain both ADR related messages (positive examples) and non-ADR related messages (negative examples). In this paper, we integrate text mining and partially supervised learning methods to automatically extract and classify messages posted on social media networks into positive and negative examples. Our findings can provide managerial insights into how social media analytics can improve not only postmarketing surveillance, but also other problem domains where large quantity of user-generated content is available.


title: Some challenges in forensic fingerprint classification and interpretation

abstract: The usage of fingerprints is no longer limited to crime investigation. And automated fingerprint identification systems have been developed for a variety of applications. However, in law enforcement agency manual categorization and analysis are still an indispensible part of fingerprint operation mainly for the following two reasons: (1) Automated fingerprint classification and identification systems are far from perfection. They can help to expedite the identification process, but cannot completely replace human fingerprint experts in terms of identification accuracy; (2) Fingerprint classification can help speed up the automated searching process as fingerprint databases become overwhelmingly large. In this paper, we review existing classification systems, including Henry, Vucetich, Battley, and NCIC classification systems, and look into the challenges of classifying certain ambiguous fingerprints and interpreting latent prints. By reviewing literature and a representative case of fingerprint misidentification, we analyze the objective and subjective factors contributing to erroneous and inconsistent identification.


title: That Smarts!: Smartphones and Child Injuries

abstract: From 2005 to 2012, injuries to children under ve increased by 10%. Using the expansion of ATT’s 3G network, I nd that smartphone adoption has a causal impact on child injuries. This eect is strongest amongst children ages 0-5, but not children ages 6-10, and in activities where parental supervision matters. I put this forward as indirect evidence that this increase is due to parents being distracted while supervising children, and not due to increased participation in accident-prone activities.


title: Cooperative and Competitive Dynamics Model for Information Propagation in Online Social Networks

abstract: Traditional empirical models of propagation consider individual contagion as an independent process, thus spreading in isolation manner. In this paper, we study how different contagions interact with each other as they spread through the network in order to propose an alternative dynamics model for information propagation. The proposed model is a novel combination of Lotka-Volterra cooperative model and competitive model. It is assumed that the interaction of one message on another is flexible instead of always negative. We prove that the impact of competition depends on the critical speed of the messages. By analyzing the differential equations, one or two stable equilibrium points can be found under certain conditions. Simulation results not only show the correctness of our theoretical analyses but also provide a more attractive conclusion. Different types of messages could coexist in the condition of high critical speed and intense competitive environment, or vice versa. The messages will benefit from the high critical speed when they are both competitive, and adopting a Tit-for-Tat strategy is necessary during the process of information propagation.


Kalman filtering in R


title: Pressure Ulcer Prevalence, Incidence, Risk Factors, and Impact

abstract: Pressure ulcers are a common problem among older adults in all health care settings. Prevalence and incidence estimates vary by setting, ulcer stage, and length of follow-up. Risk factors associated with increased pressure ulcer incidence have been identified. Activity or mobility limitation, incontinence, abnormalities in nutritional status, and altered consciousness are the most consistently reported risk factors for pressure ulcers. Pain, infectious complications, prolonged and expensive hospitalizations, persistent open ulcers, and increased risk of death are all associated with the development of pressure ulcers. The tremendous variability in pressure ulcer prevalence and incidence in health care settings suggests that opportunities exist to improve outcomes for persons at risk for and with pressure ulcers.


title: Kea: A Computation Offloading System for Smartphone Sensor Data

abstract: Nowadays smartphones are equipped with many sensors which applications can continuously invoke to acquire real-time sensor information, such as GPS tracking. Due to the resource-constrained nature of the smartphones, it is often beneficial if the processing of the sensor data is offloaded to a remote resource. However, the decision to offload the computation depends on a multitude of factors such as the hardware capabilities of the phone, the communication energy and latency and the characteristics of the stream computations, e.g., window size, sensor frequency and operational complexity.In this paper we introduce Kea, a profiling-based computation offloading system that automatically decides whether offloading is beneficial for smartphones. The decision making is based on two criteria: the power consumption of the application and the elapsed time for processing the sensor data. Our evaluation results show that unexpected factors such as CPU frequency scaling and the network state also influence the decision-making process. In addition, we show that Kea's profiling overhead is negligible.


title: Double Pole Four Throw switch design with CMOS inverter

abstract: To avoid the uses of multiple RF chain associated with the multiple antennas, RF switch is most essential component. Multiple antennas systems are used to replace traditional single antennas circuitry in the radio transceiver system in order to improve the transmission capability and reliability. The desired switching system must have low cost and simple structure, yet still can capture all the advantage of Multiple Inputs Multiple Output (MIMO) systems. In this paper, we have proposed a Double Pole Four Throw (DP4T) RF switch using Complementary Metal Oxide Semiconductor (CMOS) technology with inverter property. This switch is low in cost, and capable of selecting data streams to or from the two antennas for transmitting or receiving processes, respectively.


title: Design and kinematic modeling of a concentric wire-driven mechanism targeted for minimally invasive surgery

abstract: In this paper, a concentric wire-driven mechanism (CWM) is presented. The CWM comprises of two nested wire-driven mechanisms (WDM). Each WDM contains a flexible backbone and a set of wires. The backbone bending is controlled by pulling the wires. The stiffness of the outer WDM is controllable and dominants that of the overlapped section. Therefore, in the overlapped section the inner WDM conforms to the shape of the outer WDM and the tip of the outer WDM serves as the base of the distal separate section of the inner WDM. Compared with conventional flexible mechanisms, i.e., tendon/wire/cable-driven mechanism and concentric tube mechanism, the CWM has a much wider workspace. This advantage is demonstrated by simulations based on kinematic modeling. A prototype is developed and the preliminary experimental results validate the design concept.


title: Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results

abstract: Clustering of data in a large dimension space is of a great interest in many data mining appli cations In this paper we propose a method for clustering of data in a high dimensional space based on a hypergraph model In this method the relationship present in the original data in high dimensional space are mapped into a hypergraph A hyperedge represents a relationship a n ity among subsets of data and the weight of the hyperedge re ects the strength of this a nity A hypergraph partitioning algorithm is used to nd a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized We present results of experiments on two di erent data sets S P stock data for the period of and protein coding data These experiments demonstrate that our approach is applicable and e ective in high dimensional datasets


title: Emotion regulation and memory: The cognitive costs of keeping one's cool.

abstract: An emerging literature has begun to document the affective consequences of emotion regulation. Little is known, however, about whether emotion regulation also has cognitive consequences. A process model of emotion suggests that expressive suppression should reduce memory for emotional events but that reappraisal should not. Three studies tested this hypothesis. Study 1 experimentally manipulated expressive suppression during film viewing, showing that suppression led to poorer memory for the details of the film. Study 2 manipulated expressive suppression and reappraisal during slide viewing. Only suppression led to pooler slide memory. Study 3 examined individual differences in typical expressive suppression and reappraisal and found that suppression was associated with poorer selfreported and objective memory but that reappraisal was not. Together, these studies suggest that the cognitive costs of keeping one's cool may vary according to how this is done.


title: Role of self-care in management of diabetes mellitus

abstract: Diabetes mellitus (DM) is a chronic progressive metabolic disorder characterized by hyperglycemia mainly due to absolute (Type 1 DM) or relative (Type 2 DM) deficiency of insulin hormone. World Health Organization estimates that more than 346 million people worldwide have DM. This number is likely to more than double by 2030 without any intervention. The needs of diabetic patients are not only limited to adequate glycemic control but also correspond with preventing complications; disability limitation and rehabilitation. There are seven essential self-care behaviors in people with diabetes which predict good outcomes namely healthy eating, being physically active, monitoring of blood sugar, compliant with medications, good problem-solving skills, healthy coping skills and risk-reduction behaviors. All these seven behaviors have been found to be positively correlated with good glycemic control, reduction of complications and improvement in quality of life. Individuals with diabetes have been shown to make a dramatic impact on the progression and development of their disease by participating in their own care. Despite this fact, compliance or adherence to these activities has been found to be low, especially when looking at long-term changes. Though multiple demographic, socio-economic and social support factors can be considered as positive contributors in facilitating self-care activities in diabetic patients, role of clinicians in promoting self-care is vital and has to be emphasized. Realizing the multi-faceted nature of the problem, a systematic, multi-pronged and an integrated approach is required for promoting self-care practices among diabetic patients to avert any long-term complications.


title: Smart Massive MIMO: An Infrastructure toward 5th Generation Smart Cities Network

abstract: On the Optimizing of Wireless Networks and toward improving the future 5th Generation mobile Network Infrastructure, we propose a novel infrastructure that can be the next Smart City Network. Our proposed Infrastructure takes into consideration most future demands and challenges, includes Capacity, Reliability, Scalability, and Flexibility. To deal with this issues we propose a wireless network infrastructure that is based on latest technologies of Massive MIMO systems. We further extend our infrastructure with many smart features, to be capable of coping with Cloud Computing, Smartphones, IoT and other intelligence-based services. The proposed infrastructure uses Network Functions Virtualization (NFV), Software-Defined Networking ::: (SDN), Virtual Antenna Arrays (VAA) and Joint Beamforming to afford flexibility. We further propose a Terminal-centric rather than a Cell-centric based Infrastructure, which optimize interference aware environment and lead to higher capacity and reliability. The new infrastructure includes multi-purpose nodes that run a Network Operating System (NOS). This node will afford a scalable and flexible cost effective and semi-distributed network resources. Other propositions that meet Power-Effective, Cost-Effective, and Scenery aware design are discussed. Keywords - Wireless Network Infrastructure, Massive MIMO, Joint Beamforming, Cloud-based Networks, NFV, SDN, Cloud Computing, Grid Computing, and Distributed Systems.


title: Eternal black holes in Anti-de-Sitter

abstract: We propose a dual non-perturbative description for maximally extended Schwarzschild Anti-de-Sitter spacetimes. The description involves two copies of the conformal field theory associated to the AdS spacetime and an initial entangled state. In this context we also discuss a version of the information loss paradox and its resolution.


Robust Multimodal Sequence-Based Loop Closure Detection via Structured Sparsity


title: Comparing effective and ineffective behaviors of student programmers

abstract: This paper reports on a quantitative evaluation of five years of data collected in the first three programming courses at Virginia Tech. The dataset involves a total of 89,879 assignment submissions by 1,101 different students. Assignment results were partitioned into two groups: scores above 80% (A/B) and scores below 80% (C/D/F). To investigate student behaviors that result in differing levels of achievement, all students who consistently received A/B scores and all students who consistently received C/D/F scores were removed from the dataset. A within-subjects comparison of the scores received by the remaining individuals was performed. Further, time and code-size data that is difficult to compare directly between different courses was normalized. This study revealed several significant results. When students received A/B scores, they started earlier and finished earlier than when the same students received C/D/F scores. They also wrote slightly more program code. They did not appear to spend any more time on their work, however. Approximately two-thirds of the A/B scores were received by individuals who started more than a day in advance of the deadline, while approximately two-thirds of the C/D/F scores were received by individuals who started on the last day or later. One possible explanation is that students who start earlier simply have more time to seek assistance when they get stuck.


title: Modeling and attitude control of a tilt tri-rotor UAV

abstract: The tilt tri-rotor UAV which combines the advantages of both rotary wing and fixed wing vehicle, offers the capability of hover, vertical take-off and landing and fast forward movement. Because of the complex structure and aerodynamics, the modeling and control of tilt tri-rotor UAV reamians a problem. In this paper, precise model of a tilt tri-rotor UAV is developed by combing system identification and physical measurement. An attitude controller is designed based on control allocation of attitude and throttle on the powerplant. The detailed mathematical model is obtained by the equations of Newton-Euler, which parameters are acquired from designed experiments. To improve the stability of helicopter fight, a new control allocation strategy is developed in the framework of PID control.


title: Assembling virtual pots from 3D measurements of their fragments

abstract: A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. The overall approach is formulated and described and some detail is provided on the elements of the procedure. The end result is a representation suitable for comparisons, geometric feature extraction, visualization and digital archiving. Matching of fragments and aligning them geometrically is based on matching break-curves (curves on a pot surface separating fragments), estimated axes and profile curves for individual fragments and groups of matched fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. In our case, associated with subassemblies of fragments is a loglikelihood which is a sum of energy functions. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The addressed problem and solution can be considered as problems in "geometric learning" and in "perceptual grouping," where subgroups of pot fragments at a site location are to be assembled into individual virtual pots and other fragments are to be discarded as clutter.


title: EVALUATION OF PENALTY FUNCTIONS FOR SEMI-GLOBAL MATCHING COST AGGREGATION

abstract: Abstract. The stereo matching method semi-global matching (SGM) relies on consistency constraints during the cost aggregation which are enforced by so-called penalty terms. This paper proposes new and evaluates four penalty functions for SGM. Due to mutual dependencies, two types of matching cost calculation, census and rank transform, are considered. Performance is measured using original and degenerated images exhibiting radiometric changes and noise from the Middlebury benchmark. The two best performing penalty functions are inversely proportional and negatively linear to the intensity gradient and perform equally with 6.05% and 5.91% average error, respectively. The experiments also show that adaptive penalty terms are mandatory when dealing with difficult imaging conditions. Consequently, for highest algorithmic performance in real-world systems, selection of a suitable penalty function and thorough parametrization with respect to the expected image quality is essential.


title: Extending SPARQL with regular expression patterns (for querying RDF)

abstract: RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for ''Path RDF'') which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.


title: Routing Protocols Based on Ant Colony Optimization in Wireless Sensor Networks: A Survey

abstract: Routing is one of the important topics in wireless sensor networks (WSNs), and has been attracting the research community in the last decade. Routing based on ant colony optimization (ACO) is a type of transmission method rich in characteristics. There are several survey papers that present and compare routing protocols from various perspectives, but the survey on ACO-based routing is still missing. This paper makes a first attempt to provide a comprehensive review on ACO-based routing protocols in WSNs. First, we offer a classification of these routing algorithms. Second, the most representative ACO-based routing protocols are described, discussed, and qualitatively compared. Finally, we put forward some open issues concerning the design of WSNs. This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate routing schemes for specific applications.


title: Policy maker and provider knowledge and attitudes regarding the provision of emergency contraceptive pills within Lao PDR

abstract: BackgroundThe Ministry of Health (MOH) launched the National Reproductive Health Policy in 2005, which included recommendations regarding the use of emergency contraceptive pills (ECP). However, ECP have not yet been introduced officially in the public sector of the Lao PDR. Thus, their availability is limited. Understanding the knowledge of ECP and attitudes about their provision, barriers to use, and availability among health providers and policy makers is essential to successfully incorporate ECP into reproductive health services.MethodsQualitative research methods using in-depth interviews were employed to collect data from policy makers and health providers (auxiliary medical staff, nurses, and medical doctors). Altogether, 10 policy makers, 22 public providers, and 10 providers at private clinics were interviewed. Content analysis was applied to analyze the transcribed data.ResultsThe majority of policy makers and health care providers had heard about ECP and supported their introduction in the public sector. However, their knowledge was poor, many expressed inconsistent attitudes, and their ability to meet the demand of potential users is limited.ConclusionsThere is a need to train health providers and policy makers on emergency contraception and improve their knowledge about ECP, especially regarding the correct timing of use and the availability of methods. In addition, the general public must be informed of the attributes, side effects, and availability of ECP, and policy makers must facilitate the approval of ECP by the Lao Food and Drug Administration. These interventions could lead to increased access to and demand for ECP.


title: Design and Locomotion Control of a Biomimetic Underwater Vehicle With Fin Propulsion

abstract: As a novel biologically inspired underwater vehicle, a robotic manta ray (RoMan-II) has been developed for potential marine applications. Manta ray can perform diversified locomotion patterns in water by manipulating two wide tins. These motion patterns have been implemented on the developed fish robot, including swimming by flapping fins, turning by modulating phase relations of fins, and online transition of different motion patterns. The movements are achieved by using a model of artificial central pattern generators (CPGs) constructed with coupled nonlinear oscillators. This paper focuses on the analytical formulation of coupling terms in the CPG model and the implementation issues of the CPG-based control on the fish robot. The control method demonstrated on the manta ray robot is expected to be a frame- work that can tackle locomotion control problems in other types of multifin-actuated fish robots or more general robots with rhythmic movement patterns.


Women Exploitation in Indian modern society


title: Optimization of the SVM Kernels using an Empirical Error Minimization Scheme.

abstract: We address the problem of optimizing kernel parameters in Support Vector Machine modelling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. for optimizing SVM kernels using an analytic upper bound of the error. However, our optimization scheme minimizes an empirical error estimate using a Quasi-Newton technique. The method has shown to reduce the number of support vectors along the optimization process. In order to assess our contribution, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIST digit image database. The method has shown satisfactory results with much faster convergence in comparison with the simple gradient descent method.Furthermore, we also experimented two more optimization schemes based respectively on the maximization of the margin and on the minimization of an approximated VC dimension estimate. While both of the objective functions are minimized, the error is not. The corresponding experimental results we carried out show this shortcoming.


title: On the Robustness of Mobile Device Fingerprinting: Can Mobile Users Escape Modern Web-Tracking Mechanisms?

abstract: Client fingerprinting techniques enhance classical cookie-based user tracking to increase the robustness of tracking techniques. A unique identifier is created based on characteristic attributes of the client device, and then used for deployment of personalized advertisements or similar use cases. Whereas fingerprinting performs well for highly customized devices (especially desktop computers), these methods often lack in precision for highly standardized devices like mobile phones. In this paper, we show that widely used techniques do not perform well for mobile devices yet, but that it is possible to build a fingerprinting system for precise recognition and identification. We evaluate our proposed system in an online study and verify its robustness against misclassification. Fingerprinting of web clients is often seen as an offence to web users' privacy as it usually takes place without the users' knowledge, awareness, and consent. Thus, we also analyze whether it is possible to outrun fingerprinting of mobile devices. We investigate different scenarios in which users are able to circumvent a fingerprinting system and evade our newly created methods.


title: Accurate Vision-based Vehicle Localization using Satellite Imagery

abstract: We propose a method for accurately localizing ground vehicles with the aid of satellite imagery. Our approach takes a ground image as input, and outputs the location from which it was taken on a georeferenced satellite image. We perform visual localization by estimating the co-occurrence probabilities between the ground and satellite images based on a ground-satellite feature dictionary. The method is able to estimate likelihoods over arbitrary locations without the need for a dense ground image database. We present a ranking-loss based algorithm that learns location-discriminative feature projection matrices that result in further improvements in accuracy. We evaluate our method on the Malaga and KITTI public datasets and demonstrate significant improvements over a baseline that performs exhaustive search.


title: RF-Echo: A Non-Line-of-Sight Indoor Localization System Using a Low-Power Active RF Reflector ASIC Tag

abstract: Long-range low-power localization is a key technology that enables a host of new applications of wireless sensor nodes. We present RF-Echo, a new low-power RF localization solution that achieves decimeter accuracy in long range indoor non-line-of-sight (NLOS) scenarios. RF-Echo introduces a custom-designed active RF reflector ASIC (application specific integrated circuit) fabricated in a 180nm CMOS process which echoes a frequency-shifted orthogonal frequency division multiplexing (OFDM) signal originally generated from an anchor. The proposed technique is based on time-of-flight (ToF) estimation in the frequency domain that effectively eliminates inter-carrier and inter-symbol interference in multipath-rich indoor NLOS channels. RF-Echo uses a relatively narrow bandwidth of $\leq$80 MHz which does not require an expensive very high sampling rate analog-to-digital converter (ADC). Unlike ultra-wideband (UWB) systems, the active reflection scheme is designed to operate at a relatively low carrier frequency that can penetrate building walls and other blocking objects for challenging NLOS scenarios. Since the bandwidth at lower frequencies (2.4 GHz and sub-1 GHz) is severely limited, we propose novel signal processing algorithms as well as machine learning techniques to significantly enhance the localization resolution given the bandwidth constraint of the proposed system. The newly fabricated tag IC consumes 62.8 mW active power. The software defined radio (SDR) based anchor prototype is rapidly deployable without the need for accurate synchronization among anchors and tags. Field trials conducted in a university building confirm up to 85 m operation with decimeter accuracy for robust 2D localization.


title: Query optimization of distributed pattern matching

abstract: Greedy algorithms for subgraph pattern matching operations are often sufficient when the graph data set can be held in memory on a single machine. However, as graph data sets increasingly expand and require external storage and partitioning across a cluster of machines, more sophisticated query optimization techniques become critical to avoid explosions in query latency. In this paper, we introduce several query optimization techniques for distributed graph pattern matching. These techniques include (1) a System-R style dynamic programming-based optimization algorithm that considers both linear and bushy plans, (2) a cycle detection-based algorithm that leverages cycles to reduce intermediate result set sizes, and (3) a computation reusing technique that eliminates redundant query execution and data transfer over the network. Experimental results show that these algorithms can lead to an order of magnitude improvement in query performance.


title: Information network or social network?: the structure of the twitter follow graph

abstract: In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.


title: Shear strength of concrete members without transverse reinforcement: A mechanical approach to consistently account for size and strain effects

abstract: Many theories and empirical formulae have been proposed to estimate the shear strength of reinforced concrete members without transverse reinforcement. It can be noted that these approaches differ not only in the resulting design expressions, but also on the governing parameters and on the interpretation of the failure mechanisms and governing shear-transfer actions. Also, no general consensus is yet available on the role that size and strain effects exhibit on the shear strength and how should they be accounted. This paper reviews the various potential shear-transfer actions in reinforced concrete beams with rectangular cross-section and discusses on their role, governing parameters and the influences that the size and level of deformation may exhibit on them. This is performed by means of an analytical integration of the stresses developed at the critical shear crack and accounting for the member kinematics. The results according to this analysis are discussed, leading to a number of conclusions. Finally, the resulting shear strength criteria are compared and related to the Critical Shear Crack Theory. This comparison shows the latter to be physically consistent, accounting for the governing mechanical parameters and leading to a smooth transition between limit analysis and Linear Elastic Fracture Mechanics in agreement to the size-effect law provided by Bažant et al.


title: A very brief measure of the Big-Five personality domains

abstract: When time is limited, researchers may be faced with the choice of using an extremely brief measure of the Big-Five personality dimensions or using no measure at all. To meet the need for a very brief measure, 5 and 10-item inventories were developed and evaluated. Although somewhat inferior to standard multi-item instruments, the instruments reached adequate levels in terms of: (a) convergence with widely used Big-Five measures in self, observer, and peer reports, (b) test–retest reliability, (c) patterns of predicted external correlates, and (d) convergence between self and observer ratings. On the basis of these tests, a 10-item measure of the Big-Five dimensions is offered for situations where very short measures are needed, personality is not the primary topic of interest, or researchers can tolerate the somewhat diminished psychometric properties associated with very brief measures.


title: Virtual Hadoop: MapReduce over Docker Containers with an Auto-Scaling Mechanism for Heterogeneous Environments

abstract: Hadoop is a widely used software framework for handling massive data. As heterogeneous computing gains its momentum, variants of Hadoop have been developed to offload the computation of the Hadoop applications onto the heterogeneous processors, such as GPUs, DSPs, and FPGA. Unfortunately, these variants do not support on-demand resource scaling for the deadline-aware applications in a sophisticated heterogeneous computing environment. In this work, we developed a framework called Virtual Hadoop, which scales out the required computing resources for the applications automatically to meet the given real-time requirements. We extended the methods of resource inference and allocation for the heterogeneous computing environments. On top of these methods, an auto-scaling mechanism was developed to dynamically allocate resources on-demand based on profile data and performance models for the application execution to meet the given time requirements. In addition, Virtual Hadoop can utilize Docker containers to facilitate the auto-scaling mechanism, where a container encapsulates a Hadoop node with the capability to leverage heterogeneous computing engines. Our experimental results reveal the efficiency of Virtual Hadoop, and hopefully the experiences and discussion presented in this paper will ease the adoption of heterogeneous computing for efficient big data processing.


title: Rescuing LoRaWAN 1.0

abstract: LoRaWAN is a worldwide deployed IoT security protocol. We provide an extensive analysis of the version 1.0, which is the currently deployed version, and we show that it suffers from several weaknesses. We introduce several attacks, including practical ones, that breach the network availability, data integrity, and data confidentiality, and target either an end-device or the backend system.


title: Semi-Supervised Learning with the Deep Rendering Mixture Model

abstract: Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a probabilistic generative model that models latent nuisance variation, and whose inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to learn from both labeled and unlabeled data. Guided by the theory of the DRMM, we introduce a novel non-negativity constraint and a variational inference term. We report state-of-the-art performance on MNIST and SVHN and competitive results on CIFAR10. We also probe deeper into how a DRMM trained in a semi-supervised setting represents latent nuisance variation using synthetically rendered images. Taken together, our work provides a unified framework for supervised, unsupervised, and semi-supervised learning.


title: Fault creep along the southern San Andreas from interferometric synthetic aperture radar, permanent scatterers, and stacking

abstract: [1] Interferometric synthetic aperture radar (InSAR) provides a practical means of mapping creep along major strike-slip faults. The small amplitude of the creep signal (<10 mm/yr), combined with its short wavelength, makes it difficult to extract from long time span interferograms, especially in agricultural or heavily vegetated areas. We utilize two approaches to extract the fault creep signal from 37 ERS SAR images along the southern San Andreas Fault. First, amplitude stacking is utilized to identify permanent scatterers, which are then used to weight the interferogram prior to spatial filtering. This weighting improves correlation and also provides a mask for poorly correlated areas. Second, the unwrapped phase is stacked to reduce tropospheric and other short-wavelength noise. This combined processing enables us to recover the near-field (∼200 m) slip signal across the fault due to shallow creep. Displacement maps from 60 interferograms reveal a diffuse secular strain buildup, punctuated by localized interseismic creep of 4–6 mm/yr line of sight (LOS, 12–18 mm/yr horizontal). With the exception of Durmid Hill, this entire segment of the southern San Andreas experienced right-lateral triggered slip of up to 10 cm during the 3.5-year period spanning the 1992 Landers earthquake. The deformation change following the 1999 Hector Mine earthquake was much smaller (<1 cm) and broader than for the Landers event. Profiles across the fault during the interseismic phase show peak-to-trough amplitude ranging from 15 to 25 mm/yr (horizontal component) and the minimum misfit models show a range of creeping/locking depth values that fit the data.


title: Path Planning for Visual servoing and Navigation using Convex Optimization

abstract: Exploiting visual cues to control systems for robotic applications is a promising idea. Practically, they are usable only if the end result accedes to the restrictions of the specific environment. These restrictions are like the limited field of view of the camera and physical constraints of the workspace of the robot. Hence, there is a need for a general framework that can be used adaptively across various environments. We develop such an algorithmic framework that is flexible to accommodate various kinds of constraints and generate a solution that is optimal in the sense of the considered error measure. We perform a constrained optimization on the error in a convex domain considering all the necessary constraints using convex optimization techniques and further extend it to nonconvex domains. We utilize branch-and-bound algorithm to divide the problem of optimizing over a range of rotations into simpler problems and solve for the optimal rotation. We demonstrate the performance of the algorithm by generating control signals in a simulated framework for visual servoing and in a real-world for robot


title: How Current Android Malware Seeks to Evade Automated Code Analysis

abstract: First we report on a new threat campaign, underway in Korea, which infected around 20,000 Android users within two months. The campaign attacked mobile users with malicious applications spread via different channels, such as email attachments or SMS spam. A detailed investigation of the Android malware resulted in the identification of a new Android malware family Android/BadAccents. The family represents current state-of-the-art in mobile malware development for banking trojans. ::: ::: Second, we describe in detail the techniques this malware family uses and confront them with current state-of-the-art static and dynamic code-analysis techniques for Android applications. We highlight various challenges for automatic malware analysis frameworks that significantly hinder the fully automatic detection of malicious components in current Android malware. Furthermore, the malware exploits a previously unknown tapjacking vulnerability in the Android operating system, which we describe. As a result of this work, the vulnerability, affecting all Android versions, will be patched in one of the next releases of the Android Open Source Project.


title: An architecture for integrating plan-based behavior generation with interactive game environments

abstract: R. Michael Young Department of Computer Science, Box 7535, North Carolina State University, Raleigh, NC, 27695, young@csc.ncsu.edu Mark O. Riedl Department of Computer Science, Box 7535, North Carolina State University, Raleigh, NC, 27695, young@csc.ncsu.edu Mark Branly Department of Computer Science, Box 7535, North Carolina State University, Raleigh, NC, 27695, young@csc.ncsu.edu Arnav Jhala Department of Computer Science, Box 7535, North Carolina State University, Raleigh, NC, 27695, young@csc.ncsu.edu R. J. Martin Knowwonder Digital Mediaworks, 12421 Willows Road NE #200 Kirkland, WA 98034, rockdeity@mindspring.com C. J. Saretto Microsoft Corporation, Box 1 Microsoft Way Redmond, WA 98052, cjsar@microsoft.com


title: Humor Styles as Mediators Between Self-Evaluative Standards and Psychological Well-Being

abstract: The authors examined how certain humor styles mediate the relations between self-evaluative standards (which form the primary evaluative component of the self-schema) and psychological well-being. As predicted, greater endorsement of positive self-evaluative standards led to the use of more affiliative humor, which, in turn, led to higher levels of social self-esteem and lower levels of depression. Also, as predicted, greater endorsement of negative self-evaluative standards led to the use of more self-defeating humor, which resulted in lower levels of social self-esteem and higher levels of depression. Further, affiliative humor also mediated the relation between negative self-evaluative standards and well-being. In this study, the greater endorsement of negative self-evaluative standards led to the use of less affiliative humor, which led to a decrease in social self-esteem. These results suggest that specific features associated with these 2 humor styles may contribute in a differential manner to an in...


title: T-junction: Experiments, trajectory collection, and analysis

abstract: For the proper understanding and modelling of pedestrian dynamics, reliable empirical data is necessary for analysis and verification. This paper discusses laboratory experiments at T-junctions: the setup, the extraction of the trajectories of the pedestrians and the analysis of the resulting data. Such experiments give us the opportunity to selectively analyse parameters independent of undesired influences and adjust them to high densities seldom seen in field studies. For the T-junction the density inside the junction has been varied and analysed. Two strategies for the time-efficient automatic extraction of accurate pedestrian trajectories from stereo recordings are presented. One strategy uses marker for detection and the other one based on a perspective depth field. From these trajectories the fundamental diagram of T-junction flow is analysed, the density, velocity and specific flow profiles are obtained using the Voronoi method.


title: Verifying remote data integrity in peer-to-peer data storage: A comprehensive survey of protocols

abstract: This paper surveys protocols that verify remote data possession. These protocols have been proposed as a primitive for ensuring the long-term integrity and availability of data stored at remote untrusted hosts. Externalizing data storage to multiple network hosts is becoming widely used in several distributed storage and P2P systems, which urges the need for new solutions that provide security properties for the remote data. Replication techniques cannot ensure on their own data integrity and availability, since they only offer probabilistic guarantees. Moreover, peer dynamics (i.e., peers join and leave at any time) and their potential misbehavior (e.g., free-riding) exacerbate the difficult challenge of securing remote data. To this end, remote data integrity verification protocols have been proposed with the aim to detect faulty and misbehaving storage hosts, in a dynamic and open setting as P2P networks. In this survey, we analyze several of these protocols, compare them with respect to expected security guarantees and discuss their limitations.


title: An improved eye feature extraction algorithm based on deformable templates

abstract: In this paper, an improved method for eye extraction using deformable templates is proposed. This new method overcomes the shortcomings of traditional deformable template techniques for eye extraction, such as unexpected shrinking of the template and complexity of the updating procedure, while offering higher flexibility and accuracy A new size term and eye corner finder are introduced to prevent over-shrinking and improve speed and accuracy of fitting. The eye features are fitted in a pre-set order to reduce the complexity of the updating procedure and increase the flexibility. We demonstrate the success of this new method by extracting eye features in real images.


title: Energy-efficient surveillance system using wireless sensor networks

abstract: The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We can trade off energy-awareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system.


title: Coordination in software development

abstract: Since its inception, the software industry has been in crisis. As Blazer noted 20 years ago, “[Software] is unreliable, delivered late, unresponsive to change, inefficient, and expensive … and has been for the past 20 years” [4]. In a survey of software contractors and government contract officers, over half of the respondents believed that calendar overruns, cost overruns, code that required in-house modifications before being usable, and code that was difficult to modify were common problems in the software projects they supervised [22]. Even today, problems with software systems are common and highly-publicized occurrences.


Depthmap: A program to perform visibility graph analysis. Paper presented at