title: Social Media: A New Frontier for Retailers?

abstract: During the last two decades the retailing industry is finding itself in a state of constant evolution and transformation. Globalization, mergers and acquisitions, and technological developments have drastically changed the retailing landscape. The explosive growth of the Internet has been one of the main catalysts in this process. The effects of the Internet have been mostly felt in retail sectors dealing mainly with intangibles or information products. But these are not likely to be limited to these sectors; increasingly retailers of physical products realize that the empowered, sophisticated, critical and well-informed consumer of today is essentially different to the consumer they have always known. The web, and particularly what is known as Social Media or Web 2.0, have given consumers much more control, information and power over the market process, posing retailers with a number of important dilemmas and challenges. This article explains what the new face of the Internet, widely referred to as Web 2.0 or Social Media, is, identifies its importance as a strategic marketing tool and proposes a number of alternative strategies for retailers. Implementing such strategies will allow retailers not only to survive, but also create competitive advantages and thrive in the new environment.


title: Determining water quality parameters of aquifers in the Vientiane Basin, Laos, using geophysical and water chemistry data

abstract: The aim of this study is to test the possibility of using magnetic resonance sounding (MRS) and vertical electrical sounding (VES) together with groundwater chemistry data from shallow and deep wells to distinguish freshwater aquifers from salt affected groundwater and moreover determine water quality parameters directly from geophysical data. Three study areas within the Vientiane Basin, including 46 sites and 38 sampled wells, have been chosen for the study. The main water quality ::: parameters affecting the water conductivity in the Vientiane Basin are total dissolved solids (TDS) and hardness and to a minor extent chloride, which all have shown to have fairly high correlation to the electrical conductivity ( EC ) of groundwater. The resistivity of the aquifers is highly variable but is most often above 4 Ωm, suggesting fresh water. No wells in the investigated areas have water quality ::: parameters exceeding the limit for unacceptable drinking or irrigation water. There is a fairly strong relationship between the conductivity of water from deep wells and the conductivity of the main water bearing layer determined by VES, where the best fit is obtained using: ::: EC [μS/cm] = 0.4694VES[μS/cm], R 2 = 0.69. However, due to equivalence in the VES interpretation and the difficulty to estimate the resistivity contribution from finer sediments, the equation may be seen and used as guidance to good water quality only within the Vientiane Basin area or in other areas with similar geology. The MRS results have been used to identify water layers in the VES inversion results to decrease the uncertainty in the interpretation The combination of MRS and VES techniques has been successful in distinguishing high conductive clay and mudstone from water bearing layers and moreover freshwater aquifers from salt affected water. Altogether, this makes MRS and VES very promising tools for guidance of future drilling and water quality estimation.


title: FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

abstract: In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google’s Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.


title: Generative Adversarial Networks Conditioned by Brain Signals

abstract: Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have supported the development of image generation techniques able to reach satisfactory levels of realism. Further improvements have been proposed to condition GANs to generate images matching a specific object category or a short text description. In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images. To accomplish this task, we first demonstrate that brain activity EEG signals encode visually-related information that allows us to accurately discriminate between visual object categories and, accordingly, we extract a more compact class-dependent representation of EEG data using recurrent neural networks. Afterwards, we use the learned EEG manifold to condition image generation employing GANs, which, during inference, will read EEG signals and convert them into images. We tested our generative approach using EEG signals recorded from six subjects while looking at images of the aforementioned 40 visual classes. The results show that for classes represented by well-defined visual patterns (e.g., pandas, airplane, etc.), the generated images are realistic and highly resemble those evoking the EEG signals used for conditioning GANs, resulting in an actual reading-the-mind process.


title: A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input

abstract: We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision. We combine discrete reasoning with uncertain predictions by a multi-world approach that represents uncertainty about the perceived world in a bayesian framework. Our approach can handle human questions of high complexity about realistic scenes and replies with range of answer like counts, object classes, instances and lists of them. The system is directly trained from question-answer pairs. We establish a first benchmark for this task that can be seen as a modern attempt at a visual turing test.


title: Strategy pattern: payment pattern for Internet banking

abstract: This paper brings out the design patterns according to the various services involved in internet banking. The Pattern oriented Software Architecture uses the pattern which eliminates the difficulty of reusability in a particular context. The patterns are to be designed using BPM (Business Process Model) for effective cross cutting on process level. For implementing the above said BPM, the Internet banking has been taken to implement the pattern into it. The Analysis and identification of various processes in Internet Banking have been done, to identify the effective cross cutting features. With this process the pattern has been designed, as a reusability component to be used by the Software Architect. The pattern help us to resolve recurring problems constructively and based on proven solutions and also support us in understanding the architecture of a given software system. Once the model is finalized by analyzing, we found payment in the process of internet banking has a strategy pattern.


title: On Cognitive Computing

abstract: Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing. [Article copies are available for purchase from InfoSci-on-Demand.com]


title: Distinguishing specific sexual and general emotional effects in fMRI—Subcortical and cortical arousal during erotic picture viewing

abstract: Abstract Sexual activity involves excitement with high arousal and pleasure as typical features of emotions. Brain activations specifically related to erotic feelings and those related to general emotional processing are therefore hard to disentangle. Using fMRI in 21 healthy subjects (11 males and 10 females), we investigated regions that show activations specifically related to the viewing of sexually intense pictures while controlling for general emotional arousal (GEA) or pleasure. Activations in the ventral striatum and hypothalamus were found to be modulated by the stimulus' specific sexual intensity (SSI) while activations in the anterior cingulate cortex were associated with an interaction between sexual intensity and emotional valence. In contrast, activation in other regions like the dorsomedial prefrontal cortex, the mediodorsal thalamus and the amygdala was associated only with a general emotional component during sexual arousal. No differences were found in these effects when comparing females and males. Our findings demonstrate for the first time neural differentiation between emotional and sexual components in the neural network underlying sexual arousal.


title: Consistent image registration

abstract: This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.


title: Active cleaning of label noise

abstract: Mislabeled examples in the training data can severely affect the performance of supervised classifiers. In this paper, we present an approach to remove any mislabeled examples in the dataset by selecting suspicious examples as targets for inspection. We show that the large margin and soft margin principles used in support vector machines (SVM) have the characteristic of capturing the mislabeled examples as support vectors. Experimental results on two character recognition datasets show that one-class and two-class SVMs are able to capture around 85% and 99% of label noise examples, respectively, as their support vectors. We propose another new method that iteratively builds two-class SVM classifiers on the non-support vector examples from the training data followed by an expert manually verifying the support vectors based on their classification score to identify any mislabeled examples. We show that this method reduces the number of examples to be reviewed, as well as providing parameter independence of this method, through experimental results on four data sets. So, by (re-)examining the labels of the selective support vectors, most noise can be removed. This can be quite advantageous when rapidly building a labeled data set. HighlightsNovel method for label noise removal from data is introduced.It significantly reduces the required number of examples to be reviewed.Support vectors of SVM classifier can capture around 99% of label noise examples.Two-class SVM captures more label noise examples than one-class SVM classifierCombination of one-class and two-class SVM produces a marginal improvement.


title: Anatomy of news consumption on Facebook

abstract: The advent of social media and microblogging platforms has radically changed the way we consume information and form opinions. In this paper, we explore the anatomy of the information space on Facebook by characterizing on a global scale the news consumption patterns of 376 million users over a time span of 6 y (January 2010 to December 2015). We find that users tend to focus on a limited set of pages, producing a sharp community structure among news outlets. We also find that the preferences of users and news providers differ. By tracking how Facebook pages “like” each other and examining their geolocation, we find that news providers are more geographically confined than users. We devise a simple model of selective exposure that reproduces the observed connectivity patterns.


title: FPGA-based parallel hardware architecture for SIFT algorithm

abstract: A parallel hardware architecture for real-time image feature detection based on the SIFT (Scale Invariant Feature Transform) algorithm has been presented. The proposed parallel hardware architecture is completely stand-alone; it reads the input data directly from VITA2000 and provides the results via a Field-Programmable Gate Array (FPGA); image key points are extracted in combination with SIFT IP Core. Our proposed parallel hardware system could be able to detect feature points up to 30 frames per second with the resolution 1920∗1080; the proposed method has the similar accuracy to PC implementation. Experimental results shown that the hardware architecture for SIFT algorithm realizes fast feature extraction, the shortcomings of massive calculation and low speed in the process of extracting image features have been efficiently improved, which meets the real-time requirements in feature matching system. The achieved system performance is at least two orders of magnitude better than a PC-based solution; this algorithm can be applied to feature matching in the field of Robotics.


title: Porosity prediction, prior to drilling, in sandstones of the Kekiktuk Formation (Mississippian), North Slope of Alaska

abstract: Porosity in sandstones of the Kekiktuk Formation was successfully estimated prior to drilling of the 1 Leffingwell wildcat well (North Slope of Alaska). The estimate was based on a calibration dataset used to evaluate the effects of (1) framework grain composition, (2) depositional facies, and (3) postdepositional processes on porosity of Kekiktuk sandstones. The sandstones of the Kekiktuk Formation are chert-bearing sublitharenites and quartzarenites characterized by a homogeneous composition of the detrital framework in the study area. Thus, mineral composition is not a major factor responsible for differences in reservoir quality. Based on outcrop and available core observations, the Kekiktuk Formation was interpreted to include several wet fan-deltas. The depositional model suggested that the 1 Leffingwell well would penetrate the distal, fine-grained facies of one such system. A petrographic study indicated that in fine- and very fine-grained Kekiktuk sandstones, such as those predicted in the wildcat, porosity was reduced primarily by silica cementation. Silica cementation, in turn, is related to burial history. Because of the relationship among porosity, silica cementation, and burial history, burial history diagrams provided a measure of the effect of burial history on porosity in available calibration wells. A synthetic burial history curve was constructed prior to drilling of the 1 Leffingwell well from available seismic data. This burial history curve was then used to estimate the well's porosity based on the previously established porosity-burial history relationship.


title: Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

abstract: We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predict their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes, without any modifications. Our method is significantly faster than the previous image-based method, and generates results that outperforms it and other state-of-the-art deep generative scene models in terms of faithfulness to training data and perceived visual quality.


title: Experiences with Handheld Augmented Reality

abstract: In this paper, we present Studierstube ES, a framework for the development of handheld Augmented Reality. The applications run self-contained on handheld computers and smartphones with Windows CE. A detailed description of the performance critical tracking and rendering components are given. We also report on the implementation of a client-server architecture for multi-user applications, and a game engine for location based museum games that has been built on top of this infrastructure. Details on two games that were created, permanently deployed and evaluated in two Austrian museums illustrate the practical value of the framework and lessons learned from using it.


title: Chainspace: A Sharded Smart Contracts Platform

abstract: Chainspace is a decentralized infrastructure, known as a distributed ledger, that supports user defined smart contracts and executes user-supplied transactions on their objects. The correct execution of smart contract transactions is verifiable by all. The system is scalable, by sharding state and the execution of transactions, and using S-BAC, a distributed commit protocol, to guarantee consistency. Chainspace is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT), and extremely high-auditability, non-repudiation and `blockchain' techniques. Even when BFT fails, auditing mechanisms are in place to trace malicious participants. We present the design, rationale, and details of Chainspace; we argue through evaluating an implementation of the system about its scaling and other features; we illustrate a number of privacy-friendly smart contracts for smart metering, polling and banking and measure their performance.


title: Motion planning in urban environments: Part I

abstract: We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part II of a two-part paper, we describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.


A Clustering-Based Algorithm for Automatic Document Separation


title: Far be it from them: Patterns of subjunctive use amongst students, teachers and the community

abstract: Such is the case of the French subjunctive. For centuries grammarians have laboured to delimit the conditions requiring it and the meanings it conveys, resulting in a set of prescriptive rules that is notoriously complex, vague and contradictory. Conversely, mood choice has developed in a highly regular but untaught way in the community: though sensitive to some morphosyntactic conditions, it is predominantly lexically constrained, occurring primarily under a few matrix verbs and a handful of embedded verbs (Poplack 1992). Moreover, real-time comparisons of 19th and 20th century French reveal that, despite a rate increase, productivity is waning as the subjunctive becomes more lexically entrenched.


title: Image Annotation Within the Context of Personal Photo Collections Using Hierarchical Event and Scene Models

abstract: Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and exploit the contextual information naturally implied by the associated GPS and time metadata. First, we employ a constrained clustering method to partition a photo collection into event-based subcollections, considering that the GPS records may be partly missing (a practical issue). We then use conditional random field (CRF) models to exploit the correlation between photos based on 1) time-location constraints and 2) the relationship between collection-level annotation (i.e., events) and image-level annotation (i.e., scenes). With the introduction of such a multilevel annotation hierarchy, our system addresses the problem of annotating consumer photo collections that requires a more hierarchical description of the customers' activities than do the simpler image annotation tasks. The efficacy of the proposed system is validated by extensive evaluation using a sizable geotagged personal photo collection database, which consists of over 100 photo collections and is manually labeled for 12 events and 12 scenes to create ground truth.


title: Direct importance estimation with model selection and its application to covariate shift adaptation

abstract: A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Simulations illustrate the usefulness of our approach.


Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel


title: A New Method of Voiced/Unvoiced Classification Based on Clustering

abstract: In this paper, a new method for making v/uv decision is developed which uses a multi-feature v/uv classification algorithm based on the analysis of cepstral peak, zero crossing rate, and autocorrelation function (ACF) peak of short-time segments of the speech signal by using some clustering methods. This v/uv classifier achieved excellent results for identification of voiced and unvoiced segments of speech.


title: Paxos Quorum Leases: Fast Reads Without Sacrificing Writes

abstract: This paper describes quorum leases, a new technique that allows Paxos-based systems to perform reads with high throughput and low latency. Quorum leases do not sacrifice consistency and have only a small impact on system availability and write latency. Quorum leases allow a majority of replicas to perform strongly consistent local reads, which substantially reduces read latency at those replicas (e.g., by two orders of magnitude in wide-area scenarios). Previous techniques for performing local reads in Paxos systems either (a) sacrifice consistency; (b) allow only one replica to read locally; or (c) decrease the availability of the system and increase the latency of all updates by requiring all replicas to be notified synchronously. We describe the design of quorum leases and evaluate their benefits compared to previous approaches through an implementation running in five geo-distributed Amazon EC2 datacenters.


title: Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments

abstract: This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.


title: Effect of nitrogen-doped graphene quantum dots on the fibrillation of hen egg-white lysozyme

abstract: Abstract In this study, the fibrillation of hen egg-white lysozyme (HEWL) in the absence and presence of different amount of nitrogen-doped graphene quantum dots (N-GQDs) was studied by Thioflavin T (ThT) spectroscopy, Congo red (CR) binding assays, 8-anilino-1-naphthalenesulfonic acid (ANS) fluorescence assay, circular dichroism (CD) and transmission electron microscopy (TEM). The experimental results indicated that not only the fibrillation of HEWL at high temperature (65 °C) and low pH (pH = 2.0) could be inhibited effectively by N-GQDs, but the inhibition of HEWL by N-GQDs followed a dose-dependent manner. The results of this work suggested that the N-GQDs had a great potential for designing new therapeutic agents and were promising for future treatment of amyloid-related diseases.


title: Elements of Wireless Power Transfer Essential to High Power Charging of Heavy Duty Vehicles

abstract: Scaling wireless charging to power levels suitable for heavy duty passenger vehicles and mass transit bus requires in-depth assessment of wireless power transfer (WPT) architectures, component sizing and stress, package size, electrical insulation requirements, parasitic loss elements, and cost minimization. It is demonstrated through an architecture comparison that the voltage rating of the power inverter semiconductors will be higher for inductor-capacitor-capacitor (LCC) than for a more conventional Series–Parallel (S–P) tuning. Higher voltage at the source inverter dc bus facilitates better utilization of the semiconductors, hence lower cost. Electrical and thermal stress factors of the passive components are explored, in particular the compensating capacitors and coupling coils. Experimental results are presented for a prototype, precommercial, 10 kW wireless charger designed for heavy duty (HD) vehicle application. Results are in good agreement with theory and validate a design that minimizes component stress.


Challenges of Microservices Architecture: A Survey on the State of the Practice


title: RAMSES: RFID Augmented Module for Smart Environmental Sensing

abstract: This paper presents a radio frequency identification (RFID) augmented module for smart environmental sensing (RAMSES), which is a fully passive device with sensing and computation capabilities conceived to explore novel and unconventional RFID applications. RAMSES implements an RF energy-harvesting circuit enhanced by a dc-dc voltage booster in silicon-on-insulator technology, an ultralow-power microcontroller, temperature, light, and acceleration sensors, and a new-generation ${\rm I}^{2}{\rm C}$ -RFID chip to wirelessly deliver sensor data to standard RFID EPCglobal Class-1 Generation-2 readers. A preliminary RAMSES prototype, fabricated on a printed circuit board using low-cost off-the-shelf discrete components, has been extensively tested through experiments conducted both in lab and real-world application scenarios. The achieved results have demonstrated the ability of RAMSES to harvest the RF energy emitted by an interrogator placed up to 10 m of distance and autonomously perform sensing, computation, and data communication. To our knowledge, this is the longest range ever reported for fully passive RFID sensors. Furthermore, for applications requiring larger operating distances, RAMSES provides also a battery-assisted passive mode yielding up to 22-m communication range.


title: Sport motivation scale-6 (SMS-6): a revised six-factor sport motivation scale.

abstract: Background and Purpose: The sport motivation scale (SMS; [Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M., Briere, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The sport motivation scale (SMS). Journal of Sport and Exercise Psychology, 17, 35-53]) was developed to measure an athlete's motivation toward sport participation. However, the SMS does not measure the most autonomous form of extrinsic motivation, integrated regulation, which is inconsistent with self-determination theory (SDT; [Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. New York: Plenum Press]) upon which the instrument is based. Moreover, several studies (e.g., [Martens, M. P., & Webber, S. N. (2002). Psychometric properties of the sport motivation scale: An evaluation with college varsity athletes from the U.S. Journal of Sport and Exercise Psychology, 24, 254-270]) have questioned the factorial validity of the SMS. Hence, the purpose of this study was to develop a revised version of the SMS, including integrated regulation. Method: In Stage 1, the factorial validity of the SMS was examined using confirmatory factor analysis (CFA) on data collected from 614 Australians (elite athletes and university students). In Stage 2, the scale was revised by including integrated regulation items and replacing problematic items through an iterative process using CFA for data collected from 557 Australian university students. Concurrent validity of the revised scale was examined by evaluating correlations with the dispositional flow scale-2 (DFS-2) [Jackson,S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology]. Results: The revision led to development of a six-factor 24-item scale (SMS-6) that indicated a more parsimonious and improved fitting model consistent with SDT. Correlations between the SMS-6 and DFS-2 factors support the concurrent validity of the revised scale. Conclusion: From statistical and theoretical viewpoints, the revised SMS-6 was preferable to the original SMS, except for the discriminant validity issue of identification regulation. Further examination of the instrument is necessary by cross-validating the findings from this study. (c) 2007 Published by Elsevier Ltd.


of LaborThe Stability of Big-Five Personality Traits


title: Detection of Spatial Defect Patterns Generated in Semiconductor Fabrication Processes

abstract: Spatial defect patterns generated during integrated circuit (IC) manufacturing processes contain information about potential problems in the processes. The detection of these defect patterns is crucial to improve yield and reliability in IC manufacturing. This paper proposes a multistep defect analysis approach that provides clustering results with different levels of accuracy. A defect denoising step, based on the K th nearest-neighbor noise removal technique, determines the existence of any clustered local defects on a wafer. If local defects exist, the denoising step separates local defects from global defects. A defect clustering step applies a similarity-based clustering technique to group the local defects into clusters according to their spatial locations. A pattern identification step identifies the pattern for each of the local defect clusters (i.e., linear, curvilinear, amorphous, or ring-shaped patterns) via various model selection criteria. Finally, a fine tuning step is applied in order to improve the accuracy of the clustering performance. The fine tuning step is based on model-based clustering with a fixed number of clusters and known patterns for each cluster. The results of both simulated and real wafer map data demonstrate the potential of our approach, both in terms of computational speed and detection accuracy, for analyzing general defect patterns generated during the IC fabrication process.


title: Evaluation of Turkey's Hydroelectric Potential by Using Statistical Techniques

abstract: In today's world, known energy resources are rapidly consumed; therefore, seeking alternative energy resources is of crucial importance. Recurrent, renewable and raw material-free energy resources are now the focus of investment in many countries. Hydroelectric energy is one of the renewable energy resources in which many countries are investing, especially the European Union member states. Despite having a high potential for hydroelectricity, Turkey still makes use of only part of this potential. To use and scientifically evaluate this potential, this study statistically examined the effect of variables such as installed capacity, operation time and the amount of incoming water on hydroelectric energy generation in Turkey. Least Squares (LS) and Ridge regression (RR) techniques were used to determine the relation between the variables.


title: Executive Functioning as a Predictor of Children's Mathematics Ability: Inhibition, Switching, and Working Memory

abstract: Children's mathematical skills were considered in relation to executive functions. Using multiple measures-including the Wisconsin Card Sorting Task (WCST), dual-task performance, Stroop task, and counting span-it was found that mathematical ability was significantly correlated with all measures of executive functioning, with the exception of dual-task performance. Furthermore, regression analyses revealed that each executive function measure predicted unique variance in mathematics ability. These results are discussed in terms of a central executive with diverse functions (Shallice & Burgess, 1996) and with recent evidence from Miyake, et al. (2000) showing the unity and diversity among executive functions. It is proposed that the particular difficulties for children of lower mathematical ability are lack of inhibition and poor working memory, which result in problems with switching and evaluation of new strategies for dealing with a particular task. The practical and theoretical implications of these re...


title: A control middleware for cloud robotics

abstract: Future robot applications will require cloud computing. Using an architecture of cloud robotics would increase performance over hardware constraints (e.g., processors, memory, and power). In this paper, we propose a control middleware for cloud robotics, which not only can receive the robots' requests and allocate the cloud resources based on the robots demand, but also can ensure the connection between the robots and the cloud more stability. And then, we use an autonomous, humanoid robot to test the first stage of the control middleware. Effectiveness of this control middleware is demonstrated by experiments.


title: Observation of Nonlinear Harmonic Generation of Bulk Modes in SAW Devices

abstract: Nonlinear generation of harmonics is measured on the wafer level on temperature compensated surface-acoustic-wave resonators on lithium niobate. The second-harmonic response features strong peaks looking like multiple resonant modes. The product of the frequency difference between the successive peaks to the substrate thickness is found to remain almost constant. The results also show that the level of the harmonic signal strongly depends on the roughness of the back side of the substrate. Due to these findings, it was assumed that the signal is due to nonlinear generation of a bulk mode. This assumption is confirmed experimentally by measuring the second-harmonic wave field on the back side of the substrate with an interferometer. Possible nonlinear mechanisms explaining these results are discussed.


title: Did Darwin Get It Right?: Essays on Games, Sex and Evolution

abstract: Part 1 Science, ideology and myth: how to win the Nobel prize storming the fortress symbolism and chance science and the media molecules are not enough science, ideology and myth. Part 2 On human nature: the birth of sociobiology models of cultural and genetic change constraints on human behaviour biology and the behaviour of man tinkering boy or girl genes and memes natural selection of culture?. Part 3 Did Darwin get it right?: palaeontology at the high table current controversies in evolutionary biology did Darwin get it right? do we need a new evolutionary paradigm?. Part 4 Games, sex and evolution: why sex? the limitations of evolution theory the evolution of animal intelligence evolution and the theory of games. Part 5 The laws of the game: the counting problem understanding science matchsticks, brains and curtain rings hypercycles and the origin of life popper's world rottenness is all.


title: 5G Architectural Design Patterns

abstract: In this work, we present novel Architectural Design Patterns towards open, cloud-based 5G communications. We provide a brief classification of technologies that cannot be ignored in the design process of 5G systems and illustrate how a new technological added value can be created, when current methodologies, design paradigms, as well as design patterns and their extensions are properly exploited in efficient Radio Access Network (RAN) architectures. We believe that in many cases, the required technology is already there; nevertheless the correct approach has to be worked out and placed within an appropriate context, especially in the case of the integration of complex RAN systems. The enhancements in RF optimization, the progress in cloud computing, Software Defined Networks (SDN) and Network Function Virtualization (NFV), new design concepts such as Network Slicing have to become part of the RAN design methodology. Diverse architectural concepts should break existing stereotypes to pave the way towards the true 5G system integration.


title: Neurodegenerative Diseases Target Large-Scale Human Brain Networks

abstract: During development, the healthy human brain constructs a host of large-scale, distributed, function-critical neural networks. Neurodegenerative diseases have been thought to target these systems, but this hypothesis has not been systematically tested in living humans. We used network-sensitive neuroimaging methods to show that five different neurodegenerative syndromes cause circumscribed atrophy within five distinct, healthy, human intrinsic functional connectivity networks. We further discovered a direct link between intrinsic connectivity and gray matter structure. Across healthy individuals, nodes within each functional network exhibited tightly correlated gray matter volumes. The findings suggest that human neural networks can be defined by synchronous baseline activity, a unified corticotrophic fate, and selective vulnerability to neurodegenerative illness. Future studies may clarify how these complex systems are assembled during development and undermined by disease.


title: Big Data and Privacy: Emerging Issues

abstract: The goals of big data and privacy are fundamentally opposed to each other. Big data and knowledge discovery are aimed reducing information asymmetries between organizations and the data sources, whereas privacy is aimed at maintaining information asymmetries of data sources. A number of different definitions of privacy are used to investigate some of the tensions between different characteristics of big data and potential privacy concerns. Specifically, the author examines the consequences of unevenness in big data, digital data going from local controlled settings to uncontrolled global settings, privacy effects of reputation monitoring systems, and inferring knowledge from social media. In addition, the author briefly analyzes two other emerging sources of big data: police cameras and stingray for location information.


title: Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling

abstract: Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through l1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.


title: High-speed bounding with the MIT Cheetah 2: Control design and experiments

abstract: This paper presents the design and implementation of a bounding controller for the MIT Cheetah 2 and its experimental results. The paper introduces the architecture of the controller along with the functional roles of its subcomponents. The application of impulse scaling provides feedforward force profiles that automatically adapt across a wide range of speeds. A discrete gait pattern stabilizer maintains the footfall sequence and timing. Continuous feedback is layered to manage balance during the stance phase. Stable hybrid limit cycles are exhibited in simulation using simplified models, and are further validated in untethered three-dimensional bounding experiments. Experiments are conducted both indoors and outdoors on various man-made and natural terrains. The control framework is shown to provide stable bounding in the hardware, at speeds of up to 6.4 m/s and with a minimum total cost of transport of 0.47. These results are unprecedented accomplishments in terms of efficiency and speed in untethered ...


title: Phishing Detection in IMs using Domain Ontology and CBA - An innovative Rule Generation Approach

abstract: User ignorance towards the use of communication services like Instant Messengers, emails, websites, social networks etc. is becoming the biggest advantage for phishers. It is required to create technical awareness in users by educating them to create a phishing detection application which would generate phishing alerts for the user so that phishing messages are not ignored. The lack of basic security features to detect and prevent phishing has had a profound effect on the IM clients, as they lose their faith in e-banking and e-commerce transactions, which will have a disastrous impact on the corporate and banking sectors and businesses which rely heavily on the internet. Very little research contributions were available in for phishing detection in Instant messengers. A context based, dynamic and intelligent phishing detection methodology in IMs is proposed, to analyze and detect phishing in Instant Messages with relevance to domain ontology (OBIE) and utilizes the Classification based on Association (CBA) for generating phishing rules and alerting the victims. A PDS Monitoring system algorithm is used to identify the phishing activity during exchange of messages in IMs, with high ratio of precision and recall. The results have shown improvement by the increased percentage of precision and recall when compared to the existing methods.


title: An analysis of social network-based Sybil defenses

abstract: Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense. In this paper, we show that, despite their considerable differences, existing Sybil defense schemes work by detecting local communities (i.e., clusters of nodes more tightly knit than the rest of the graph) around a trusted node. Our finding has important implications for both existing and future designs of Sybil defense schemes. First, we show that there is an opportunity to leverage the substantial amount of prior work on general community detection algorithms in order to defend against Sybils. Second, our analysis reveals the fundamental limits of current social network-based Sybil defenses: We demonstrate that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order make their attacks more effective.


title: Improving Image Classification Using Semantic Attributes

abstract: The Bag-of-Words (BoW) model—commonly used for image classification—has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.


title: K: An Instance-based Learner Using and Entropic Distance Measure

abstract: The use of entropy as a distance measure has several benefits. It provides a consistent approach to handling symbolic attributes, real valued attributes and missing values. We discuss the approach of taking all possible transformation paths between instances. We describe K*, an instance-based learner that uses such a measure, and present results that compare favourably with several machine learning algorithms.


title: Sound Localization for Humanoid Robots - Building Audio-Motor Maps based on the HRTF

abstract: Being able to locate the origin of a sound is important for our capability to interact with the environment. Humans can locate a sound source in both the horizontal and vertical plane with only two ears, using the head related transfer function HRTF, or more specifically features like interaural time difference ITD, interaural level difference ILD, and notches in the frequency spectra. In robotics notches have been left out since they are considered complex and difficult to use. As they are the main cue for humans' ability to estimate the elevation of the sound source this have to be compensated by adding more microphones or very large and asymmetric ears. In this paper, we present a novel method to extract the notches that makes it possible to accurately estimate the location of a sound source in both the horizontal and vertical plane using only two microphones and human-like ears. We suggest the use of simple spiral-shaped ears that has similar properties to the human ears and make it easy to calculate the position of the notches. Finally we show how the robot can learn its HRTF and build audiomotor maps using supervised learning and how it automatically can update its map using vision and compensate for changes in the HRTF due to changes to the ears or the environment.


title: Effective thermal conductivity of metallic foams determined with the transient plane source technique

abstract: This article presents experimental results of thermal conductivity in metal foams. The thermal conductivity of cellular solids differs from those of their corresponding dense material. Therefore, the various pore size level effects contributing to the thermal conductivity are comprised by introducing an effective thermal conductivity. In this work we investigated metallic foams with a porosity ranging from 0.65 to 0.82 manufactured by the Slip Reaction Foam Sintering (SRFS) Process using a nickel-based powder. For the determination of the effective thermal conductivity at room temperature, we employed the Transient Plane Source Technique also known as Hot Disk. We verified the influence of the porosity and the thermal conductivity of the solid phase on the effective conductivity and compared the measured values of the effective conductivity with theoretical ones calculated using the thermal conductivity of the solid phase and the solid volume fraction. We present here a simple model that demonstrates the influence of the geometry of the grid elements qualitatively, discuss the applicability of the method to heterogeneous materials such as metallic foams and give an outlook about further investigations at higher temperatures.


title: Analysis and Design of Circular Waveguide Polarizers with Elliptical Irises

abstract: A new design of a polarizer realized in circular waveguide with the insertion of short lengths of guide with elliptical cross-section is introduced. The new structure, that requires a manufacturing by milling techniques only, compares favourably with circular waveguide implementations, since it is composed only by waveguides with separable cross-section, hence producing a definite advantage in electromagnetic modeling. With respect to previously known solutions using square waveguide sections it has the intrinsic advantage of avoiding the rectangular-to-circular transition when the component is used, as customary, with a circular feed. Measured and theoretical results for a polarizer with a 90° ± 1° differential phase shift over the operating 7.75-8.55 GHz band show a very good agreement.


title: Understanding the Technology and Organizational Elements of Customer Relationship Management Systems

abstract: To build a clear understanding of the management of the technology and organizational elements of CRM systems, this research tries to form propositions based on literature findings and experts’ experiences to explain (1) the elements of the technology and organizational sides of a CRM system, and (2) how the technology and organizational elements interrelate to create effective CRM operation. The goal is to build in-depth knowledge of the dynamics of CRM systems and to suggest lasting organizational capabilities that are adaptive to environmental changes. Adopting the socio-technical view, the technology and organizational elements of CRM are closely interrelated in the operation of a business. The technology elements of an enterprise-wide CRM system can be organized into three parts: communicational CRM that coordinates all channels of customer contacts, execution CRM that consists of all value chain activities that execute customer relationship establishment and maintenance, and intelligent CRM that consolidates business intelligence about customers. The three interrelated elements provide a technical structure for customer value-generation activities. While the organizational resources including structure, processes, culture, and user abilities are elements that need to be aligned and coordinated with CRM technologies. Management of a CRM system, therefore, can be considered as managing an organism that requires interdependent elements to work together to maintain a continuous capability of creating and sustaining customer relationships.


Towards Time-Aware Knowledge Graph Completion


title: Evaluation of Commonsense Knowledge with Mechanical Turk

abstract: Efforts to automatically acquire world knowledge from text suffer from the lack of an easy means of evaluating the resulting knowledge. We describe initial experiments using Mechanical Turk to crowdsource evaluation to non-experts for little cost, resulting in a collection of factoids with associated quality judgements. We describe the method of acquiring usable judgements from the public and the impact of such large-scale evaluation on the task of knowledge acquisition.


Sustained hippocampal chromatin regulation in a mouse model of depression and antidepressant action


title: Facilitating Internalization: The Self-Determination Theory Perspective

abstract: Self-determination theory (Deci & Ryan, 1985) posits that (a) people are inherently motivated to internalize the regulation of uninteresting though important activities; (b) there are two different processes through which such internalization can occur, resulting in qualitatively different styles of self-regulation; and (c) the social context influences which internalization process and regulatory style occur. The two types of internalization are introjection, which entails taking in a value or regulatory process but not accepting it as one's own, and integration, through which the regulation is assimilated with one's core sense of self. Introjection results in internally controlling regulation, whereas integration results in self-determination. An experiment supported our hypothesis that three facilitating contextual factors—namely, providing a meaningful rationale, acknowledging the behaver's feelings, and conveying choice—promote internalization, as evidenced by the subsequent self-regulation of behavior. This experiment also supported our expectation that when the social context supports self-determination, integration tends to occur, whereas when the context does not support self-determination, introjection tends to occur.


title: Multiview Deep Learning for Predicting Twitter Users' Location

abstract: The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets.


Family planning and population control in developing countries: Ethical and sociocultural dilemmas.


Investigation of Marine-Derived Fungal Diversity and Their Exploitable Biological Activities


title: Treatment of Periodontitis for the Prevention of Endothelial Dysfunction: A Narrative Review.

abstract: Endothelial dysfunction (ED) is the initial step in the development of atherosclerosis, leading to cardiovascular disease (CVD). It has been suggested that periodontal disease (PD) could be associated to pathogenesis of atherosclerosis, since it is able to trigger a host response with systemic inflammation. Although a number of epidemiological studies have shown that periodontitis could be associated with ED, it is still unclear whether periodontal treatment could improve ED and therefore cardiovascular outcomes. In this narrative review we analysed the literature in the databases of Medline under ''endothelial function OR dysfunction OR vasodilatation'', AND ''periodontal disease" OR periodontal treatment" AND "cardiovascular disease" OR atherosclerosis AND "endothelial biomarker". Research articles, systematic reviews and clinical trials were screened. ED could be related to periodontitis as well as to CVD. Periodontal treatment reduces the risk of teeth loss and may improve ED and the risk of CVD. Since controversial results exist, there is an urgent need for well-designed clinical trials to find and validate novel biomarkers of endothelial function, such as circulating endothelial progenitors, which may be crucial for further investigation of the association of PD with endothelial function and CVD.


Predicting Preterm Birth Based on Maternal and Fetal Data


title: Binarization With Boosting and Oversampling for Multiclass Classification

abstract: Using a set of binary classifiers to solve multiclass classification problems has been a popular approach over the years. The decision boundaries learnt by binary classifiers (also called base classifiers) are much simpler than those learnt by multiclass classifiers. This paper proposes a new classification framework, termed binarization with boosting and oversampling (BBO), for efficiently solving multiclass classification problems. The new framework is devised based on the one-versus-all (OVA) binarization technique. Unlike most previous work, BBO employs boosting for solving the hard-to-learn instances and oversampling for handling the class-imbalance problem arising due to OVA binarization. These two features make BBO different from other existing works. Our new framework has been tested extensively on several multiclass supervised and semi-supervised classification problems using five different base classifiers, including neural networks, C4.5, k -nearest neighbor, repeated incremental pruning to produce error reduction, support vector machine, random forest, and learning with local and global consistency. Experimental results show that BBO can exhibit better performance compared to its counterparts on supervised and semi-supervised classification problems.


title: General Knowledge Embedded Image Representation Learning

abstract: Image representation learning is a fundamental problem in understanding semantics of images. However, traditional classification-based representation learning methods face the noisy and incomplete problem of the supervisory labels. In this paper, we propose a general knowledge base embedded image representation learning approach, which uses general knowledge graph, which is a multitype relational knowledge graph consisting of human commonsense beyond image space, as external semantic resource to capture the relations of concepts in image representation learning. A relational regularized regression CNN (R $^3$ CNN) model is designed to jointly optimize the image representation learning problem and knowledge graph embedding problem. In this manner, the learnt representation can capture not only labeled tags but also related concepts of images, which involves more precise and complete semantics. Comprehensive experiments are conducted to investigate the effectiveness and transferability of our approach in tag prediction task, zero-shot tag inference task, and content-based image retrieval task. The experimental results demonstrate that the proposed approach performs significantly better than the existing representation learning methods. Finally, observation of the learnt relations show that our approach can somehow refine the knowledge base to describe images and label the images with structured tags.


title: The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

abstract: Social Networks Sites (SNSs) are dominating all internet users’ generations, especially the students’ communities. Consequently, academic institutions are increasingly using SNSs which leads to emerge a crucial question regarding the impact of SNSs on students’ academic performance. This research investigates how and to what degree the use of SNSs affects the students’ academic performance. The current research’s data was conducted by using drop and collect surveys on a large population from the University of Jordan. 366 undergraduate students answered the survey from different faculties at the university. In order to study the impact of SNSs on student’s academic performance, the research hypotheses was tested by using descriptive analysis, T-test and ANOVA. Research results showed that there was a significant impact of SNS on the student’s academic performance. Also, there was a significant impact of SNS use per week on the student’s academic performance, whereas no differences found in the impact of use of SNSs on academic performance due to age, academic achievement, and use per day to most used sites. The findings of this research can be used to suggest future strategies in enhancing student’s awareness in efficient time management and better multitasking that can lead to improving study activities and academic achievements.


Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies


title: Sparse representation using nonnegative curds and whey

abstract: It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer to it as nonnegative curds and whey (NNCW). The NNCW procedure consists of two stages. In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regressiontype nonnegative matrix factorization problems. In the second stage we incorporate these representations into a new sparse and nonnegative representation by using the group nonnegative garrote. This procedure is particularly appropriate for discriminant analysis owing to its supervised and nonnegativity nature in sparsity pursuing. Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method.


title: Smart grid standards for home and building automation

abstract: The involvement of smart grid in home and building automation systems has led to the development of diverse standards for interoperable products to control appliances, lighting, energy management and security. Smart grid enables a user to control the energy usage according to the price and demand. These standards have been developed in parallel by different organizations, which are either open or proprietary. It is necessary to arrange these standards in such a way that it is easier for potential readers to easily understand and select a suitable standard according to their functionalities without going into the depth of each standard. In this paper, we review the main smart grid standards proposed by different organizations for home and building automation in terms of different function fields. In addition, we evaluate the scope of interoperability, benefits and drawbacks of the standard.


title: Bio-Inspired Model Learning Visual Goals and Attention Skills Through Contingencies and Intrinsic Motivations

abstract: Animal learning is driven not only by biological needs but also by intrinsic motivations (IMs) serving the acquisition of knowledge. Computational modeling involving IMs is indicating that learning of motor skills requires that autonomous agents self-generate tasks/goals and use them to acquire skills solving/leading to them. We propose a neural architecture driven by IMs that is able to self-generate goals on the basis of the environmental changes caused by the agent’s actions. The main novelties of the model are that it is focused on the acquisition of attention (looking) skills and that its architecture and functioning are broadly inspired by the functioning of relevant primate brain areas (superior colliculus, basal ganglia, and frontal cortex). These areas, involved in IM-based behavior learning, play important functions for reflexive and voluntary attention. The model is tested within a simple simulated pan-tilt camera robot engaged in learning to switch on different lights by looking at them, and is able to self-generate visual goals and learn attention skills under IM guidance. The model represents a novel hypothesis on how primates and robots might autonomously learn attention skills and has a potential to account for developmental psychology experiments and the underlying brain mechanisms.


title: CDMA radio on FSO links over gamma turbulence channels with nonzero boresight pointing errors

abstract: In this work, we investigate the performance of a radio-on-free-space optical (RoFSO) communication system for transmission of CDMA signals. The optical signal propagation is affected by the combined effects of turbulence-induced fading, modelled by the gamma distribution, and the generalized nonzero boresight pointing errors. The performance assessment is implemented with the average BER metric evaluation for each user. Finally, the corresponding numerical results are presented for various parameter values concerning the CDMA wireless optical transmission.


title: An Ultra Low Power Bandgap Operational at Supply From 0.75 V

abstract: We present an ultra low power (200 nA current consumption) reverse bandgap voltage reference operational from supply voltages down to 0.75 V. The reference is a part of microprocessor system on chip implemented in a digital 130 nm CMOS process and has a total area of 0.07 mm2. The reference accuracy is ± 2.5% (5 sigma) over a temperature range of - 20 to 85°C without trimming. With trimming ± 0.5% accuracy is achieved.


title: A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation

abstract: New wireless technology for tele-home-care purposes gives new possibilities for monitoring of vital parameters with wearable biomedical sensors, and will give the patient the freedom to be mobile and still be under continuously monitoring and thereby to better quality of patient care. This paper describes a new concept for wireless and wearable electrocardiogram (ECG) sensor transmitting signals to a diagnostic station at the hospital, and this concept is intended for detecting rarely occurrences of cardiac arrhythmias and to follow up critical patients from their home while they are carrying out daily activities.


title: Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

abstract: We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus. We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order.


title: Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

abstract: Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic networks and business-review networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing approaches neither explore rich semantic information embedded in the network structures nor take user's preference as a guidance. ::: In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework. It models vertices as low-dimensional vectors to explore network structure-embedded similarity. To accommodate user preferences at defining similarity semantics, our proposed framework, ESim, accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Moreover, an efficient and parallel sampling-based optimization algorithm has been developed to learn embeddings in large-scale HINs. Extensive experiments on real-world large-scale HINs demonstrate a significant improvement on the effectiveness of ESim over several state-of-the-art algorithms as well as its scalability.


title: HGAA: An Architecture to Support Hierarchical Group and Attribute-Based Access Control

abstract: Attribute-Based Access Control (ABAC), a promising alternative to traditional models of access control, has gained significant attention in recent academic literature. This attention has lead to the creation of a number of ABAC models including our previous contribution, Hierarchical Group and Attribute-Based Access Control (HGABAC). However, to date few complete solutions exist that provide both an ABAC model and architecture that could be implemented in real life scenarios. This work aims to advance progress towards a complete ABAC solution by introducing Hierarchical Group Attribute Architecture (HGAA), an architecture to support HGABAC and close the gap between a model and real world implementation. In addition to HGAA we also present an attribute certificate specification that enables users to provide proof of attribute ownership in a pseudonymous and off-line manner, as well as an update to the Hierarchical Group Policy Language (HGPL) to support our namespace for uniquely identifying attributes across disparate security domains. Details of our HGAA implementation are given and a preliminary analysis of its performance is discussed as well as directions for future work.


title: What do cross-national comparisons of personality traits tell us? The case of conscientiousness

abstract: Much research contrasts self-reported personality traits across cultures. We submit that this enterprise is weakened by significant methodological problems (in particular, the reference-group effect) that undermine the validity of national averages of personality scores. In this study, behavioral and demographic predictors of conscientiousness were correlated with different cross-national measures of conscientiousness based on self-reports, peer reports, and perceptions of national character. The predictors correlated strongly with perceptions of national character, but not with self-reports and peer reports. Country-level self- and peer-report measures of conscientiousness failed as markers of between-nation differences in personality.


title: Models in software engineering - an introduction

abstract: Abstract.Modelling is a concept fundamental for software engineering. In this paper, the word is defined and discussed from various perspectives. The most important types of models are presented, and examples are given. Models are very useful, but sometimes also dangerous, in particular to those who use them unconsciously. Such problems are shown. Finally, the role of models in software engineering research is discussed.


Combining Unassisted and Robot-Guided Practice Benefits Motor Learning for a Golf Putting Task


title: 3D CAPTCHA: A Next Generation of the CAPTCHA

abstract: Nowadays, the Internet is now becoming a part of our everyday lives. Many services, including Email, search engine, and web board on Internet, are provided with free of charge and unintentionally turns them into vulnerability services. Many software robots or, in short term, bots are developed with purpose to use such services illegally and automatically. Thus, web sites employ human authentication mechanism called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) to counter this attack. Unfortunately, many CAPTCHA have been already broken by bots and some CAPTCHA are difficult to read by human. In this paper, a new CAPTCHA method called 3D CAPTCHA is proposed to provide an enhanced protection from bots. This method based on assumption that human can recognize 3D character image better than Optical Character Recognition (OCR) software bots.


title: Sound—Vortex Interaction in Infinite Media

abstract: This lecture describes the interaction of sound and vorticity in infinite media. The scattering of sound by the vorticity is decribed. Recent results on ultrasound propagation through a disordered flow are given.


QChain: Quantum-resistant and decentralized PKI using Blockchain


title: Trimodal Scan-Based Test Paradigm

abstract: This paper presents a novel scan-based design for test (DFT) paradigm. Compared with conventional scan, the presented approach either significantly reduces test application time while preserving high fault coverage or allows applying a much larger number of vectors within the same time interval. An equally important factor is the toggling activity during test—with this scheme, it remains similar to that of the mission mode. Several techniques are introduced that allow integration of the proposed scheme with the state-of-the-art test generation and application methods. In particular, the new scheme uses redesigned scan cells to dynamically configure scan chains into different modes of operation for use with the underlying test-per-clock principle. The experimental results obtained for large and complex industrial application-specific IC designs illustrate the feasibility of the proposed test scheme despite additional costs and efforts entailed in consolidating architectural changes and operations across a DFT flow.


title: Coil design guidelines for high efficiency of wireless power transfer (WPT)

abstract: Efficient operation of a wireless power transfer (WPT) system is a major design challenge in many WPT applications. This paper presents a method for designing a high efficiency WPT system when restrictions are imposed on receiver coil (Rx) size. The optimum pitch of the transmitter (Tx) and Rx coils is evaluated by analyzing the coil quality factor and coupling coefficient. Empirical formulae are proposed to derive generalized design guidelines for a given Rx size. The proposed empirical approach can be directly used for the design of a WPT with miniaturized Rx coils without using time consuming optimization algorithms. The proposed empirical expressions are validated with experimental results.


title: A robust feedforward compensation scheme for multistage operational transconductance amplifiers with no Miller capacitors

abstract: A multistage operational transconductance amplifier with a feedforward compensation scheme which does not use Miller capacitors is introduced. The compensation scheme uses the positive phase shift of left-half-plane (LHP) zeroes caused by the feedforward path to cancel the negative phase shift of poles to achieve a good phase margin. A two-stage path increases further the low frequency gain while a feedforward single-stage amplifier makes the circuit faster. The amplifier bandwidth is not compromised by the absence of the traditional pole-splitting effect of Miller compensation, resulting in a high-gain wideband amplifier. The capacitors of a capacitive amplifier using the proposed techniques can be varied more than a decade without significant settling time degradation. Experimental results for a prototype fabricated in an AMI 0.5-/spl mu/m CMOS process show DC gain of around 90 dB and a 1% settling time of 15 ns for a load capacitor of 12 pF. The power supply used is /spl plusmn/1.25 V.


title: Advances in affordable digital array radar

abstract: This paper firstly reviews the issues and benefits of various levels on digitisation in a DAR (digital array radar) as discussed above. It then focuses on the key technology of high levels of digitisation; that of low cost, high performance, small and modular digital receivers. This paper presents current UK R&D employing low cost COTS components to enable this to be realised. Present S Band and X Band digital receiver designs are used as examples of state of the art modular hardware. (6 pages)


INDOOR SPATIAL ANALYSIS USING SPACE SYNTAX


title: Gate and Base Drivers for Silicon Carbide Power Transistors: An Overview

abstract: Silicon carbide (SiC) power transistors have started gaining significant importance in various application areas of power electronics. During the last decade, SiC power transistors were counted not only as a potential, but also more importantly as an alternative to silicon counterparts in applications where high efficiency, high switching frequencies, and operation at elevated temperatures are targeted. Various SiC device designs have been proposed and excessive investigations in terms of simulation and experimental studies have shown their advantageous performance compared to silicon technology. On a system-level, however, the design of gate and base drivers for SiC power transistors is very challenging. In particular, a sophisticated driver design is not only associated with properly switching the transistor and decreasing the switching power losses, but also it must incorporate protection features, as well as comply with the electromagnetic compatibility. This paper shows an overview of the gate and base drivers for SiC power transistors which have been proposed by several highly qualified scientists. In particular, the basic operating principle of each driver along with their applicability and drawbacks are presented. For this overview, the three most successful SiC power transistors are considered: junction-field-effect transistors, bipolar-junction transistors, and metal-oxide-semiconductor field-effect transistors. Last but not least, future challenges on gate and base drivers design are also presented.


title: Improving Induction Motor Efficiency with Die-cast Copper Rotor Cages

abstract: This paper examines some of the electromagnetic considerations for achieving near optimum efficiency of a hypothetical 10-Hp industrial motor with a copper rotor cage. The loss budgets for the motor with an aluminum cage and that for a substituted copper cage are calculated using a Matlab program. Rotor bar shape is then varied to improve "pull-up" torque and control starting current of the copper rotor machine. The performance of this modeled motor is then compared to the performance of recently introduced commercial high efficiency copper rotor machines and some of the design considerations used by the manufacturers to achieve the performance are discussed. Advances in the die casting process that have made the copper rotor motor cost effective are reviewed.


title: Benchmarking and Error Diagnosis in Multi-instance Pose Estimation

abstract: We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm's performance. Our technique is applied to compare the two leading methods for human pose estimation on the COCO Dataset, measure the sensitivity of pose estimation with respect to instance size, type and number of visible keypoints, clutter due to multiple instances, and the relative score of instances. The performance of algorithms, and the types of error they make, are highly dependent on all these variables, but mostly on the number of keypoints and the clutter. The analysis and software tools we propose offer a novel and insightful approach for understanding the behavior of pose estimation algorithms and an effective method for measuring their strengths and weaknesses.


title: Procedural Hair Generation

abstract: This paper presents a hair generation technique for cartoon and Anime characters. The main goal of this work is to provide a procedural generation technique, robust enough in order to create different types of hairs. We propose a parametric model that can produce diversity of obtained results through parameters variation. The model is organized in three steps: firstly, the strands generation, followed by positioning them in the head of the characters. Finally, the third step is responsible for providing cartoon rendering, including common characteristics in cartoon shading, as the black edges and solid colors. Visual inspection of obtained results indicate that generated hairs are coherent with expected in terms of visual aspects.


title: The winner decision model of tic tac toe game by using multi-tape turing machine

abstract: The Tic tac toe is very popular game having a 3 × 3 grid board and 2 players. A Special Symbol (X or O) is assigned to each player to indicate the slot is covered by the respective player. The winner of the game is the player who first cover a horizontal, vertical and diagonal row of the board having only player's own symbols. This paper presents the design model of Tic tac toe Game using Multi-Tape Turing Machine in which both player choose input randomly and result of the game is declared. The computational Model of Tic tac toe is used to describe it in a formal manner.


title: A study of applications, challenges, and channel models on the Internet of Underwater Things

abstract: The Internet of Underwater Things (IoUT) is a novel class of Internet of Things (IoT), and one of the emerging technologies toward smart city. To support the concept of IoUT, Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising system. In this paper, we survey the potential IoUT applications, point out the challenges and differences between UWSNs and traditional sensor networks, and investigate the channel models. We validated the models by simulations.


title: Clustering longitudinal clinical marker trajectories from electronic health data: applications to phenotyping and endotype discovery

abstract: Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability— variations in measurements that are not due to disease subtype—which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.


Machine Learning Attacks on PolyPUF, OB-PUF, RPUF, and PUF-FSM


title: Bit Contiguous Memory Allocation for Processing In Memory

abstract: Given the recent resurgence of research into processing in or near memory systems, we find an ever increasing need to augment traditional system software tools in order to make efficient use of the PIM hardware abstractions. One such architecture, the Micron In-Memory Intelligence (IMI) DRAM, provides a unique processing capability within the sense amp stride of a traditional 2D DRAM architecture. This accumulator processing circuit has the ability to compute both horizontally and vertically on pitch within the array. This unique processing capability requires a memory allocator that provides physical bit locality in order to ensure numerical consistency. In this work we introduce a new memory allocation methodology that provides bit contiguous allocation mechanisms for horizontal and vertical memory allocations for the Micron IMI DRAM device architecture. Our methodology drastically reduces the complexity by which to find new, unallocated memory blocks by combining a sparse matrix representation of the array with dense continuity vectors that represent the relative probability of finding candidate free blocks. We demonstrate our methodology using a set of pathological and standard benchmark applications in both horizontal and vertical memory modes.


title: Time-Driven Activity-Based Costing

abstract: The traditional ABC model has been difficult for many organizations to implement because of the high costs incurred to interview and survey people for the initial ABC model, the use of subjective and costly-to-validate time allocations, and the difficulty of maintaining and updating the model as (i) processes and resource spending change, (ii) new activities are added, and (iii) increases occur in the diversity and complexity of individual orders, channels and customers. Time-driven ABC requires estimates of only two parameters: (1) the unit cost of supplying capacity and (2) the time required to perform a transaction or an activity. A time-driven ABC model: - can be estimated and installed quickly - is easily updated to reflect changes in processes, order variety, and resource costs - can be data fed from transactional ERP and CRM systems - can be validated by direct observation of the model's estimates of unit times - can scale easily to handle millions of transactions while still delivering fast processing times and real-time reporting - explicitly incorporates resource capacity and highlights unused resource capacity for management action - exploits time equations that incorporate variation in orders and customer behavior without expanding model complexity The paper uses simple numerical examples to articulate the fundamentals of time-driven ABC and provides several examples of companies that have implemented the approach and enjoyed rapid and significant profit improvements.


title: An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

abstract: The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.


title: Road marking detection using LIDAR reflective intensity data and its application to vehicle localization

abstract: A correct perception of road signalizations is required for autonomous cars to follow the traffic codes. Road marking is a signalization present on road surfaces and commonly used to inform the correct lane cars must keep. Cameras have been widely used for road marking detection, however they are sensible to environment illumination. Some Light Detection and Ranging (LIDAR) sensors return infrared reflective intensity information which is insensible to illumination condition. Existing road marking detectors that analyzes reflective intensity data focus only on lane markings and ignores other types of signalization. The authors propose a road marking detector based on Otsu thresholding method that make possible segment LIDAR point clouds into asphalt and road marking. The results show the possibility of detecting any road marking (crosswalks, continuous lines, dashed lines). The road marking detector has also been integrated with Monte Carlo localization method so that its performance could be validated. According to the results, adding road markings onto curb maps lead to a lateral localization error of 0.3119 m.


title: The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network

abstract: In this protocol we describe how to perform the Multi-Source Interference Task (MSIT), a validated functional magnetic resonance imaging (fMRI) task that reliably and robustly activates the cingulo-frontal-parietal cognitive/attention network (CFP network) within individual subjects. The MSIT can be used to (i) identify the cognitive/attention network in normal volunteers and (ii) test its integrity in people with neuropsychiatric disorders. It is simple to perform, can be completed in less than 15 min and is not language specific, making it appropriate for children, adults and the elderly. Since its validation, over 100 adults have performed the task. The MSIT produces a robust and temporally stable reaction time interference effect (range 200-350 ms), and single runs of the MSIT have produced CFP network activation in approximately 95% of tested subjects. The robust, reliable and temporally stable neuroimaging and performance data make the MSIT a useful task with which to study normal human cognition and psychiatric pathophysiology.


title: Customer retention : case studies of agencies in the professional service sector

abstract: In recent years, customer retention has gained increased value among both goods and service providing firms. However although extensive research exist on the concept of customer retention and i ...


title: Subjectivity and Sentiment Analysis of Modern Standard Arabic

abstract: Although Subjectivity and Sentiment Analysis (SSA) has been witnessing a flurry of novel research, there are few attempts to build SSA systems for Morphologically-Rich Languages (MRL). In the current study, we report efforts to partially fill this gap. We present a newly developed manually annotated corpus of Modern Standard Arabic (MSA) together with a new polarity lexicon. The corpus is a collection of newswire documents annotated on the sentence level. We also describe an automatic SSA tagging system that exploits the annotated data. We investigate the impact of different levels of preprocessing settings on the SSA classification task. We show that by explicitly accounting for the rich morphology the system is able to achieve significantly higher levels of performance.


title: Magic decorator: automatic material suggestion for indoor digital scenes

abstract: Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.


title: Do Online Companies Control the Behavior of OnlineConsumers?: Exploratory Study on the Impacting Variables fromMiddle East

abstract: The paper aims to design constructs such as internal and external variables and test the impact of the same on online buying behaviour. The constructs focus on the controlling aspect of a company on online consumer purchase. The data were collected from 168 online users and results were tested through simple and multiple linear regression model. The results revealed that both the constructs are consistent and have an impact over online buying behavior. However, the external variables found to have more impact over online purchase that that of the internal variables.


title: A Proposal for Software Ecosystems Engineering

abstract: Software Ecosystems (SECOs) have emerged as an approach to improve software reuse in industry considering relations among companies and stakeholders. Companies and organizations have opened up their platforms and artifacts to others, including partners and third-part developers around the world. This changes the traditional software industry because it requires mature research in software architecture, component-based software engineering and software product line in a market and business environment. In this sense, this paper presents an initial proposal for SECOs engineering in order to outline a set of steps that combines three different dimensions of SECOs and joins different perspectives in SECOs research literature through a survey. In this paper, the focus is on the first dimension, that is, architecture. A preliminary analysis done by the Brazilian Software Reuse Lab's SECOs at COPPE/UFRJ points out that several concepts presented at IWSECO 2009 and IWSECO 2010 can be connected in a broader SE approach.


title: Dynamic power control mechanism for interference coordination of device-to-device communication in cellular networks

abstract: Device-to-device (D2D) communication in cellular networks can bring about interference between user equipments (UEs) in cellular and D2D communications, which may decrease performance of cellular networks. In this paper, we propose dynamic power control mechanism to reduce interference and improve performance of cellular systems. The proposed mechanism excludes cellular communication UE with same resource, base station, and areas of adjacent cells from the coverage area of D2D communication UE by adjusting transmit power of the D2D communication UE. By simulations, we show that the proposed dynamic power control mechanism can improve performance of the entire communication systems.


title: Assessing and Improving Domain Knowledge Representation in DBpedia

abstract: With the development of knowledge graphs and the billions of triples generated on the Linked Data cloud, it is paramount to ensure the quality of data. In this work, we focus on one of the central hubs of the Linked Data cloud, DBpedia. In particular, we assess the quality of DBpedia for domain knowledge representation. Our results show that DBpedia has still much room for improvement in this regard, especially for the description of concepts and their linkage with the DBpedia ontology. Based on this analysis, we leverage open relation extraction and the information already available on DBpedia to partly correct the issue, by providing novel relations extracted from Wikipedia abstracts and discovering entity types using the dbo:type predicate. Our results show that open relation extraction can indeed help enrich domain knowledge representation in DBpedia.