title: E-commerce business strategy analysis and inspiration: Taking Taobao as an example

abstract: Taobao is a network retailer which founded in May 2003 and now is the most popular online retail platform in China with nearly 500 million registered users. More than 60 million people visit Taobao everyday and over 48000 items are sold every minute on this platform. During the expansion progress, Taobao has transformed from a C2C network market into a worldwide E-commerce trading platform including C2C, group purchase, distribution and other electronic commerce modes. And its future strategy is focusing on community, content and local. This article studies service and business model of Taobao from five aspects: service description and market context, service supply chain, quality of service, service management system and risk management. An analysis of the present situation of Taobao reveals that it has formed its unique business pattern and raises problems and suggestions. For Taobao stepping into cross-border E-commerce, the article analyses its strength, weakness and points out the direction of its future.


title: Building Highly-Optimized, Low-Latency Pipelines for Genomic Data Analysis

abstract: Next-generation sequencing has transformed ge- nomics into a new paradigm of data-intensive computing. The deluge of genomic data needs to undergo deep analysis to mine biological information. Deep analysis pipelines often take days to run, which entails a long cycle for algorithm and method development and hinders future application for clinic use. In this project, we aim to bring big data technology to the genomics domain and innovate in this new domain to revolutionize its data crunching power. Our work includes the development of a deep analysis pipeline, a parallel plat- form for pipeline execution, and a principled approach to optimizing the pipeline. We also present some initial evalua- tion results using existing long-running pipelines at the New York Genome Center, as well as a variety of real use cases that we plan to build in the course of this project.


title: Disentangled Representations for Manipulation of Sentiment in Text

abstract: The ability to change arbitrary aspects of a text while leaving the core message intact could have a strong impact in fields like marketing and politics by enabling e.g. automatic optimization of message impact and personalized language adapted to the receiver's profile. In this paper we take a first step towards such a system by presenting an algorithm that can manipulate the sentiment of a text while preserving its semantics using disentangled representations. Validation is performed by examining trajectories in embedding space and analyzing transformed sentences for semantic preservation while expression of desired sentiment shift.


title: Training a Feedback Loop for Hand Pose Estimation

abstract: We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. They remove the need for fitting a 3D model to the input data, which requires both a carefully designed fitting function and algorithm. We show that our approach outperforms state-of-the-art methods, and is efficient as our implementation runs at over 400 fps on a single GPU.


title: Accelerating Open vSwitch with Integrated GPU

abstract: With the fast development of Software Defined Networking (SDN) and network virtualization, software-based network virtual switches have emerged as a critical component to provide network services to VMs. Among virtual switches, Open vSwitch (OvS) is an open source virtual switch implementation commonly used and well-studied. Using Data Plane Development Kit (DPDK) with OvS to bypass the OS kernel and process packets in userspace provides tremendous performance benefits on general purpose platforms. Integrated GPUs, residing on the same die with the CPU on general purpose platforms, offering many advanced features such as on-chip interconnect CPU-GPU communication, and sharing physical/virtual memory, become a promising additional compute resource to further accelerate the OvS process. In this paper, we design and implement an inline GPU assisted OvS architecture, via offloading the expensive tuple space search to GPU and balancing switching processing between CPU and GPU. We evaluated the performance on an Intel® Xeon® processor of the E3-1575M v5 product family (code-name Skylake) with an integrated GT4e GPU. The results show that our proposed architecture improved the OvS throughput by 3x, compared to the optimized CPU-only OvS-DPDK implementation.


title: Female genital mutilation (FGM): Australian midwives' knowledge and attitudes.

abstract: Female genital mutilation (FGM) is a women's health and human rights issue attracting global interest. My purpose in this qualitative study was to report the knowledge and attitudes of Australian midwives toward FGM. Verbatim transcription and thematic analysis of semistructured interviews with 11 midwives resulted in these themes: knowledge of female genital mutilation and attitude toward female genital mutilation. Significant gaps in knowledge about FGM featured prominently. The midwives expressed anger toward FGM and empathy for affected women. Recommendations include increased information on FGM and associated legislation among midwives and other health providers in countries where FGM may be encountered.


title: Person re-identification by symmetry-driven accumulation of local features

abstract: In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.


title: Clingo = ASP + Control: Preliminary Report

abstract: We present the new ASP system clingo 4. Unlike its predecessors, being mere monolithic combinations of the grounder gringo with the solver clasp, the new clingo 4 series offers high-level constructs for realizing complex reasoning processes. Among others, such processes feature advanced forms of search, as in optimization or theory solving, or even interact with an environment, as in robotics or query-answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. In fact, clingo 4 carries out such complex reasoning within a single integrated ASP grounding and solving process. This avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo 4 accomplishes this by complementing ASP's declarative input language by control capacities expressed via the embedded scripting languages lua and python. On the declarative side, clingo 4 offers a new directive that allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side, viz. the scripting languages. By strictly separating logic and control programs, clingo 4 also abolishes the need for dedicated systems for incremental and reactive reasoning, like iclingo and oclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.


title: Using Maple to implement eLearning integrated with computer aided assessment

abstract: Advanced mathematics courses have been developed and refined by the first author, using an action research methodology, for more than a decade. These courses use the computer algebra system (CAS) Maple in an ‘immersion mode’ where all presentations and student work are done using Maple. Assignments and examinations are Maple files downloaded from the web, and submitted via the web. A recent, parallel, development has been a Maple component of an otherwise traditional first year calculus course. CAS is widely used to learn mathematics, but is also used in computer aided assessment (CAA) packages, such as AiM, STACK and MapleTA. In our courses, assignments are completed electronically within Maple and a variety of assessment strategies have been developed and trialled. Recently these assessment procedures have evolved into CAA. Since students directly work with, and submit, Maple files we do not require an external CAA package: we utilize Maple directly to implement the CAA. We have previously reported on o...


title: UAVs for humanitarian missions: Autonomy and reliability

abstract: Information plays a key role in natural disaster crisis management and relief. A new generation of lightweight UAVs may help improve the situational awareness and assessment. They may first of all relieve rescue teams from time-consuming data collection tasks. At the same time, those UAVs may assist research operations through a more insightful and automated guidance thanks to advanced sensing capabilities. In order to achieve this vision, two challenges must be addressed though. The first one is to achieve a sufficient autonomy for such vehicles, both in terms of navigation and of interpretation of the data sensed. The second one relates to the reliability of the UAV with respect to accidental (safety) or malicious (security) risks. This paper first discusses the potential of UAV assistance in several humanitarian scenarios, as well as potential problems in such situations. The question of autonomy is then addressed. Finally, a secure embedded UAV architecture that relies on cryptographic protocols and on specific hardware capabilities is sketched. Keywords—UAV; drones; 3D perception; security; safety


title: Optimized Differential GFSK Demodulator

abstract: Gaussian frequency shift keying (GFSK) is a promising digital modulation scheme. The design of simple and high-performance receivers for GFSK systems is a challenging task. In this letter, we develop an optimized differential GFSK demodulator and investigate the phase wrapping issue in its implementation. Simulation results show bit-error-rate (BER) performance improvement in comparison with conventional differential demodulators in both AWGN and flat fading channels. We also compare our proposed demodulator with other existing alternatives in terms of BER performance.


title: Unifying planning and scheduling as timelines in a component-based perspective

abstract: The timeline-based approach to planning represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This pape ...


title: Analysis and design considerations for EMI and losses of RCD snubber in flyback converter

abstract: The RCD snubber is usually used in flyback converter, in order to limit the voltage spikes caused by leakage inductance of the transformer. The design considerations of RCD snubber are commonly on the power losses dissipated in the snubber and the voltage spikes of the transistor. In fact, the design of RCD snubber will also affect the EMI performance of the converter. But the RCD design consideration for EMI performance is usually in contradiction with that for losses. In this paper, a detailed model is built up to analyze the voltage spikes, the residual energy and the power losses dissipated in the RCD snubber. And a novel design procedure is derived for selecting the resistor in the RCD circuit to make an optimal tradeoff between CM noises and losses. Experimental results consist well with theoretical analysis.


title: A di/dt Feedback-Based Active Gate Driver for Smart Switching and Fast Overcurrent Protection of IGBT Modules

abstract: This paper presents an active gate driver (AGD) for IGBT modules to improve their overall performance under normal condition as well as fault condition. Specifically, during normal switching transients, a di/dt feedback controlled current source and current sink is introduced together with a push-pull buffer for dynamic gate current control. Compared to a conventional gate drive strategy, the proposed one has the capability of reducing the switching loss, delay time, and Miller plateau duration during turn-on and turn-off transient without sacrificing current and voltage stress. Under overcurrent condition, it provides a fast protection function for IGBT modules based on the evaluation of fault current level through the di/dt feedback signal. Moreover, the AGD features flexible protection modes, which overcomes the interruption of converter operation in the event of momentary short circuits. A step-down converter is built to evaluate the performance of the proposed driving schemes under various conditions, considering variation of turn-on/off gate resistance, current levels, and short-circuit fault types. Experimental results and detailed analysis are presented to verify the feasibility of the proposed approach.


title: Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory

abstract: The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the manufacturing system. However, the currently existing architectures are mainly for offline data analysis, not suitable for real-time data processing. In this paper, we first define the smart factory as a cloud-assisted and self-organized manufacturing system in which physical entities such as machines, conveyors, and products organize production through intelligent negotiation and the cloud supervises this self-organized process for fault detection and troubleshooting based on data analysis. Then, we propose a scheme to integrate knowledge reasoning and semantic data where the reasoning engine processes the ontology model with real time semantic data coming from the production process. Based on these ideas, we build a benchmarking system for smart candy packing application that supports direct consumer customization and flexible hybrid production, and the data are collected and processed in real time for fault diagnosis and statistical analysis.


title: The origins of attachment theory: John Bowlby and Mary Ainsworth.

abstract: Attachment theory is based on the joint work of John Bowlby (1907-1991) and Mary Salter Ainsworth (1913- ). Its developmental history begins in the 1930s, with Bowlby's growing interest in the link between maternal loss or deprivation and later personality development and with Ainsworth's interest in security theory. Although Bowlby's and Ainsworth's collaboration began in 1950, it entered its most creative phase much later, after Bowlby had formulated an initial blueprint of attachment theory, drawing on ethology, control systems theory, and psychoanalytic thinking, and after Ainsworth had visited Uganda, where she conducted the first empirical study of infantmother attachment patterns. This article summarizes Bowlby's and Ainsworth's separate and joint contributions to attachment theory but also touches on other theorists and researchers whose work influenced them or was influenced by them. The article then highlights some of the major new fronts along which attachment theory is currently advancing. The article ends with some speculations on the future potential of the theory. Attachment theory is the joint work of John Bowlby and Mary Ainsworth (Ainsworth & Bowlby, 1991). Drawing on concepts from ethology, cybernetics, information processing, developmental psychology, and psychoanalysis, John Bowlby formulated the basic tenets of the theory. He thereby revolutionized our thinking about a child's tie to the mother and its disruption through separation, deprivation, and bereavement. Mary Ainsworth's innovative methodology not only made it possible to test some of Bowlby's ideas empirically but also helped expand the theory itself and is responsible for some of the new directions it is now taking. Ainsworth contributed the concept of the attachment figure as a secure base from which an infant can explore the world. In addition, she formulated the concept of maternal sensitivity to infant signals and its role in the development of infant-mother attachment patterns. The ideas now guiding attachment theory have a long developmental history. Although Bowlby and Ainsworth worked independently of each other during their early careers, both were influenced by Freud and other psychoanalytic thinkers—directly in Bowlby's case, indirectly in Ainsworth's. In this article, I document the origins of ideas that later became central to attachment theory. I then discuss the subsequent period of theory building and consolidation. Finally, I review some of the new directions in which the theory is currently developing and speculate on its future potential. In taking this retrospective developmental approach to the origins of attachment theory, I am reminded of Freud's (1920/1955) remark:


Lumanote: A Real-Time Interactive Music Composition Assistant


title: Activation of plant pattern-recognition receptors by bacteria

abstract: The first active layer of plant innate immunity relies on the recognition by surface receptors of molecules indicative of non-self or modified-self. The activation of pattern-recognition receptors (PRRs) by pathogen-associated molecular patterns (PAMPs) is in essence sufficient to stop pathogen invasion through transcriptional reprogramming and production of anti-microbials. The few PRR/PAMP pairs that are characterised provide useful models to study the specificity of ligand-binding and likely activation mechanisms. Both classical and new approaches are still required to identify new bacterial PAMPs. Current genetic screens, functional genomics and biochemical analyses have identified the regulation mechanisms of PRR transcription and biogenesis, provided insights into the composition of PRR complexes at the plasma membrane and highlighted the roles of long-known signalling components in PAMP-triggered immunity (PTI).


title: Anti-debugging scheme for protecting mobile apps on android platform

abstract: The Android application package file, APK file, can be easily decompiled using Android reverse engineering tools. Thus, general apps can be easily transformed into malicious application through reverse engineering and analysis. These repacked apps could be uploaded in general android app market called Google Play Store and redistributed. To prevent theses malicious behaviors such as malicious code injection or code falsifications, many techniques and tools were developed. However, these techniques also can be analyzed using debuggers. Also, analyzed apps can be tampered easily. For example, when applying anti-analysis techniques to android apps using Dexprotector which is commercial tool for protecting android app, it can be seen that these techniques can also be analyzed using debugger. In this paper, to protect the android app from the attack using debugger, we propose anti-debugging techniques for native code debugging and managed code debugging of android apps.


Chat Speed OP : Practices of Coherence in Massive Twitch Chat


title: Combination of Feature Selection and Optimized Fuzzy Apriori Rules: The Case of Credit Scoring

abstract: Credit scoring is an important topic and banks collect different data from their loan applicants to make appropriate and correct decisions. Rule bases are favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants. This paper, uses four feature selection approaches as features pre-processing combined with fuzzy apriori. These methods are stepwise regression, Classification And Regression Tree (CART), correlation matrix and Principle Component Analysis (PCA). Particle Swarm is applied to find the best fuzzy apriori rules by searching different support and confidence. Considering Australian and German University of California at Irvine (UCI) and an Iranian bank datasets, different feature selections methods are compared in terms of accuracy, number of rules and number of features. The results are compared using T test; it reveals that fuzzy apriori combined with PCA creates a compact rule base and shows better results than the single fuzzy apriori model and other combined feature selection methods.


title: ParamILS: An Automatic Algorithm Configuration Framework

abstract: The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm's performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.


title: A New Approach to Updating Beliefs

abstract: We define a new notion of conditional belief, which plays the same role for Dempster-Shafer belief functions as conditional probability does for probability functions. Our definition is different from the standard definition given by Dempster, and avoids many of the well-known problems of that definition. Just as the conditional probability Pr(.IB) is a probability function which is the result of conditioning on B being true, so too our conditional belief function BeZ(-IB) is a belief function which is the result of conditioning on B being true. We define the conditional belief as the lower envelope (that is, the inf) of a family of conditional probability functions, and provide a closedform expression for it. An alternate way of understanding our definition of conditional belief is provided by considering ideas from an earlier paper [Fagin and Halpern, 19891, where we connect belief functions with inner measures. In particular, we show here how to extend the definition of conditional probability to nonmeasurable sets, in order to get notions of inner and outer conditional probabilities, which can be viewed as best approximations to the true conditional probability, given our lack of information. Our definition of conditional belief turns out to be an exact analogue of our definition of inner conditional probability.


title: A comparative evaluation of open-source graph processing platforms

abstract: In recent years, advances in digital sensors, communication, computation and storage have created huge collections of data. Many real applications in various fields require efficient and effective management of these large-scale, graph-structured data, demanding the design of new techniques and platforms for analyzing, processing and mining these large-scale graphs. There are distributed graph processing platforms running on a cluster of machines as well as non-distributed platforms working on a single machine. Most of the platforms use homogeneous processors such as multi-core CPUs while several platforms utilize both multi-core CPUs and many-core GPUs. The diversities of the available graphs, the processing algorithms, and the graph-processing platforms make the selection of a platform a difficult task. In this paper, we provide a comparative study on a selection of open-source graph processing platforms. We evaluate their performance, scalability and energy efficiency and discuss the reasons behind for designers or users of graph processing platforms.


THE CURRENT SITUATION OF CRIME ASSOCIATED WITH URBANIZATION : PROBLEMS EXPERIENCED AND COUNTERMEASURES INITIATED IN THE PHILIPPINES By Celia V .


title: Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing

abstract: Robust and real-time relative pose estimation is an integral part of a position-based visual servoing (PBVS) system. Traditionally, extended Kalman filter (EKF) has been used to solve for the nonlinear relative end-effector to object pose equations from a set of 2D-3D point correspondences. However, the performance of the estimation filter and the convergence of the pose estimates are highly sensitive to tuning of filter parameters, camera calibration, and image processing. In this paper, the application of iterated EKF (IEKF) for a robust high-speed PBVS system is studied. We also provide a detailed analysis of the stability and sensitivity of the EKF and IEKF pose estimation to uncertainties in (1) tuning of filter parameters, namely, process and measurement noise covariance matrices, initial state estimate, and sampling time (speed of PBVS system), (2) features selection, and (3) calibration of camera intrinsic parameters. Experimental results show that IEKF outperforms the standard EKF without bandwidth sacrifice and should be used to improve the robustness of the PBVS system to uncertainties


title: Robust multi-cell joint transmission beamforming based on uplink-downlink duality

abstract: This paper considers robust beamforming design for coordinated multiple point joint transmission systems with imperfect channel state information at the base stations (BSs). A robust lower bound duality relation between the downlink max-min worst-case SINR optimization problem and the virtual uplink min-max worst-case SINR optimization problem is first revealed. Based on this, an iterative optimization method is then proposed using the subgradient theory to solve the virtual uplink problem, by which the solution to the downlink optimization problem is easily obtained. It is proved that the convergence of the proposed algorithm can be guaranteed with monotonic boundary sequence theorem and the subgradient theory. Numerical simulation verifies the effectiveness of the proposed method.


title: Haplotype composition at the color locus is a major genetic determinant of skin color variation in Vitis ×labruscana grapes

abstract: Skin color is one of the most important fruit traits in grape, and has become greatly diversified due to hybridization and human selection. Many studies concerning the genetic control of grape color in European species (Vitis vinifera L.), especially the role of MYB-related genes, have been reported. On the other hand, there have been few studies of the MYB-related genes in grapes belonging to V. ×labruscana L.H. Bailey, a subgroup of grapes that originated from the hybridization of V. labrusca with V. vinifera. In the present study, we found a novel functional haplotype, HapE2 (consisting of the genes VlMYBA2 and VlMYBA1–3), in diploid V. ×labruscana. Moreover, we developed a method to determine the haplotype compositions of tetraploid grapes by means of quantitative real-time PCR, and investigated the relationship between haplotype composition and skin color. The color locus in V. ×labruscana grapes usually consists of functional haplotypes (HapE1 and/or HapE2), and non-functional haplotype HapA. The number of functional haplotypes in the genome was found to be correlated with the level of anthocyanin in the skin. Anthocyanin contents of grapes that contained HapE2 were significantly higher than those containing HapE1. These results suggest that the number and kind of functional haplotypes at the color locus are the major genetic factors that determine skin color variation. These findings provide new knowledge about the unique genetic control of color in V. ×labruscana grapes, and should contribute to development of new cultivars that have the desired color and anthocyanin content.


title: Mixed Pooling for Convolutional Neural Networks

abstract: Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic pooling operations with a stochastic procedure by randomly using the conventional max pooling and average pooling methods. The advantage of the proposed mixed pooling method lies in its wonderful ability to address the over-fitting problem encountered by CNN generation. Experimental results on three benchmark image classification datasets demonstrate that the proposed mixed pooling method is superior to max pooling, average pooling and some other state-of-the-art works known in the literature.


title: Predictors of cyberbullying perpetration among college students: An application of the Theory of Reasoned Action

abstract: We applied the Theory of Reasoned Action to cyberbullying perpetration.Lower empathy toward victims predicted more favorable attitudes toward cyberbullying.More positive cyberbullying attitudes predicted higher intentions to cyberbully.Higher cyberbullying intentions predicted more frequent cyberbullying perpetration. The present study tested the Theory of Reasoned Action (TRA) as an explanation for cyberbullying perpetration among 375 (128 male, 246 female) college students. Empathy toward cyberbullying victims was also included in the models. Participants completed the cyberbullying perpetration scale of the Cyberbullying Experiences Survey (Doane, Kelley, Chiang, & Padilla, 2013) that assesses four types of cyberbullying (deception, malice, public humiliation, and unwanted contact). Across all four models, results showed that lower empathy toward cyberbullying victims predicted more favorable attitudes toward cyberbullying perpetration, more favorable attitudes toward cyberbullying predicted higher intentions to cyberbully, and higher cyberbullying intentions predicted more frequent perpetration of cyberbullying behaviors. Injunctive norms regarding cyberbullying (e.g., perception of peers' approval of cyberbullying perpetration) predicted intentions to engage in malice and unwanted contact behaviors. The results demonstrate that the TRA is a useful framework for understanding cyberbullying perpetration.


title: Phasor, frequency and ROCOF measurements in microgrids: A practical approach

abstract: This paper discusses the phasor, frequency, and the rate of change of frequency (ROCOF) estimations in Microgrids. This is accomplished using the estimates processed by a real PMU and their comparisons with the digital Cosine filter, adding new metrics for frequency and ROCOF. A real event recorded is employed aiming to estimate the phasor parameters in time-domain signals. A traditional static phasor model algorithm for the digital Cosine filter is taken into account, using a rectangular window lasting two cycles. Both frequency and ROCOF metrics are conceived by drawing an analogy with their estimations if a dynamic phasor model is assumed. The attained results demonstrate that the digital filtering process done by the Cosine filter outperforms the provided estimates by the real PMU and the new metrics suitably track the changes in frequency and ROCOF.


title: EdgeIoT: Mobile Edge Computing for the Internet of Things

abstract: In order to overcome the scalability problem of the traditional Internet of Things architecture (i.e., data streams generated from distributed IoT devices are transmitted to the remote cloud via the Internet for further analysis), this article proposes a novel approach to mobile edge computing for the IoT architecture, edgeIoT, to handle the data streams at the mobile edge. Specifically, each BS is connected to a fog node, which provides computing resources locally. On the top of the fog nodes, the SDN-based cellular core is designed to facilitate packet forwarding among fog nodes. Meanwhile, we propose a hierarchical fog computing architecture in each fog node to provide flexible IoT services while maintaining user privacy: each user's IoT devices are associated with a proxy VM (located in a fog node), which collects, classifies, and analyzes the devices' raw data streams, converts them into metadata, and transmits the metadata to the corresponding application VMs (which are owned by IoT service providers). Each application VM receives the corresponding metadata from different proxy VMs and provides its service to users. In addition, a novel proxy VM migration scheme is proposed to minimize the traffic in the SDNbased core.


title: The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction

abstract: The realism of avatars in terms of behavior and form is critical to the development of collaborative virtual environments. In the study we utilized state of the art, real-time face tracking technology to track and render facial expressions unobtrusively in a desktop CVE. Participants in dyads interacted with each other via either a video-conference (high behavioral realism and high form realism), voice only (low behavioral realism and low form realism), or an “emotibox” that rendered the dimensions of facial expressions abstractly in terms of color, shape, and orientation on a rectangular polygon (high behavioral realism and low form realism). Verbal and non-verbal self-disclosure were lowest in the videoconference condition while self-reported copresence and success of transmission and identification of emotions were lowest in the emotibox condition. Previous work demonstrates that avatar realism increases copresence while decreasing self-disclosure. We discuss the possibility of a hybrid realism solution that maintains high copresence without lowering self-disclosure, and the benefits of such an avatar on applications such as distance learning and therapy.


title: Alternating diffusion for common manifold learning with application to sleep stage assessment

abstract: In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract the common source of variability from multiple measurements. This method is based on alternating-diffusion and is particularly adapted to time series. We show that the common source of variability is extracted from multiple sensors as if it were the only source of variability, extracted by a standard manifold learning method from a single sensor, without the influence of the sensor-specific variables. In addition, we present application to sleep stage assessment. We demonstrate that, indeed, through alternating-diffusion, the sleep information hidden inside multimodal respiratory signals can be better captured compared to single-modal methods.


title: The prediction of mental health based on variables of Self-esteem, life satisfaction and hope among College students

abstract: Background and objective: Mental health is one of the key variables in the field of Psychology and Educational Sciences that several factors have been studied in relation to it. The aim of this study is to predict students' mental health using the variables of self-esteem, life satisfaction was hope. Methods: This study was of the correlation among non-experimental studies. The population were included all students in shahrood University (9,000 student) and for the sample size used krejcie and Morgan table and 368 were selected. A booklet that participants were asked to respond to questions, including:1-demographic Questionnaire2-General Health Goldberg and Hiller Questionnaire 3- Snyder Hope questionnaire 4- Rosenberg self esteem questionnaire.5-Diner satisfaction with life Questionnaire. To analyze the data descriptive statistics (Charts, measure of central tendency and dispersion measures) and inferential statistics (Pearson correlation coefficient, t-test and stepwise regression analysis) were used. Results: The results show that between self-esteem, satisfaction with life, hope and mental health (r=0/76 0/70 and 0/76) There is a significant correlation. Students mental health can be predicted based on variables self-esteem and (β =0/42), satisfaction with life (β = 0/21) and hope (β =0/52). The proportion of self-esteem variable (0/42) was more. there was no difference Between self- esteem, hope and mental health, but between life satisfaction boys scores were more than girls. Conclusion: This study showed that, in general, students with good mental health, and results showed that with increasing self-esteem, hope and life satisfaction of students can improve their mental health. Paper Type: Research Article.


Skipindex: Towards a scalable peer-to-peer index service for high dimensional data


How to Backdoor Diffie-Hellman.


title: Coccygeal Pits

abstract: BACKGROUND ::: Congenital dermal sinuses represent cutaneous depressions or tracts that are lined by stratified squamous epithelium. They communicate between the surface of the skin and deeper structures and may occur anywhere along the craniospinal axis. These sinuses are thought to result from abnormal separation of the cutaneous and neural ectoderm between the third and fifth week of intrauterine life. They may be often accompanied by other cutaneous stigmata, various dysraphic abnormalities, or intraspinal tumors. In the sacrococcygeal area, cutaneous congenital abnormalities are relatively common. It is estimated that 2% to 4% of children harbor intergluteal dorsal dermal sinuses. These intergluteal sinuses in the perianal region are frequently referred to as pits or dimples. Their cause is considered similar to other congenital dermal sinuses and appears unrelated to acquired pilonidal conditions observed in adults. They may become susceptible to local recurrent infection from trauma or hirsutism. Controversy regarding the evaluation and management of cutaneous defects in the coccygeal region exists. ::: ::: ::: METHODS ::: Both a literature review and a career review of clinical material were performed. Databases for articles published in English were surveyed for key words relating to coccygeal sinuses using standard computerized search techniques. The medical records of children presenting to our neurosurgical clinic for evaluation of dorsal dermal sinuses were reviewed to identify those with intergluteal sinuses. ::: ::: ::: RESULTS ::: In the evaluation of reported cases and of our own, we were unable to identify any children with coccygeal sinuses without other cutaneous markers other than hair with findings suggestive of intraspinal communication. ::: ::: ::: CONCLUSIONS ::: Intergluteal dorsal dermal sinuses are relatively common lesions that frequently come to neurosurgical attention. They do not seem to be associated with significant risk of spinal cord and intraspinal anomalies. Simple intergluteal dorsal dermal sinuses without other cutaneous findings do not require radiographic or surgical evaluation and treatment. If other markers or neurologic symptoms are present, however, radiographic evaluation may be indicated.


title: RFID in the warehouse: A literature analysis (1995–2010) of its applications, benefits, challenges and future trends

abstract: Radio Frequency Identification (RFID) has been identified as a crucial technology for the modern 21st century knowledge-based economy. Some businesses have realised benefits of RFID adoption through improvements in operational efficiency, additional cost savings, and opportunities for higher revenues. RFID research in warehousing operations has been less prominent than in other application domains. To investigate how RFID technology has had an impact in warehousing, a comprehensive analysis of research findings available from articles through leading scientific article databases has been conducted. Articles from years 1995 to 2010 have been reviewed and analysed with respect to warehouse operations, RFID application domains, benefits achieved and obstacles encountered. Four discussion topics are presented covering RFID in warehousing focusing on its applications, perceived benefits, obstacles to its adoption and future trends. This is aimed at elucidating the current state of RFID in the warehouse and providing insights for researchers to establish new research agendas and for practitioners to consider and assess the adoption of RFID in warehousing functions.


title: A Formal Model for A System’s Attack Surface

abstract: Practical software security metrics and measurements are essential for secure software development. In this chapter, we introduce the measure of a software system’s attack surface as an indicator of the system’s security. The larger the attack surface, the more insecure the system. We formalize the notion of a system’s attack surface using an I/O automata model of the system and introduce an attack surface metric to measure the attack surface in a systematic manner. Our metric is agnostic to a software system’s implementation language and is applicable to systems of all sizes. Software developers can use the metric in multiple phases of the software development process to improve software security. Similarly, software consumers can use the metric in their decision making process to compare alternative software.


title: Parallel AES algorithm for fast Data Encryption on GPU

abstract: With the improvement of cryptanalysis, More and more applications are starting to use Advanced Encryption Standard (AES) instead of Data Encryption Standard (DES) to protect their information security. However, current implementations of AES algorithm suffer from huge CPU resource consumption and low throughput. In this paper, we studied the technologies of GPU parallel computing and its optimized design for cryptography. Then, we proposed a new algorithm for AES parallel encryption, and designed and implemented a fast data encryption system based on GPU. The test proves that our approach can accelerate the speed of AES encryption significantly.


title: Improving the Ridge Based Fingerprint Recognition Method Using Sweat Pores

abstract: Among several biometric traits possible to be used for people identification, fingerprint is still the most used. Current automated fingerprint identification systems are based on ridge patterns and minutiae, classified as first and second level features, respectively. However, the development of new fingerprint sensors and the growing demand for more secure systems are leading to the use of additional discriminative fingerprint characteristics known as third level features, such as the sweat pores. Recent researches on fingerprint recognition have focused on fingerprint fragments, in which methods based only on first and second level features tend to obtain low recognition rates. This paper proposes a robust method de- veloped for fingerprint recognition from fingerprint fragments based on ridges and sweat pores. We have extended a ridge- based fingerprint recognition method previously proposed in the literature, based on Hough Transform, by incorporating sweat pores information in the matching step. Experimental results showed that although the reduction of Equal Error Rate is modest, a significant improvement was observed when analyzing the FMR100 and FMR1000 metrics, which are more suitable for high security applications. For these two metrics, the proposed approach obtained a reduction superior to 10% of the rates, when compared to the original ridge-based approach.


title: Shifted Jacobi tau method for solving the space fractional diffusion equation

abstract: In this paper, approximation techniques based on the shifted Jacobi together with spectral tau technique are presented to solve a class of initial-boundary value problems for the fractional diffusion equations with variable coefficients on a finite domain. The fractional derivatives are described in the Caputo sense. The technique is derived by expanding the required approximate solution as the elements of shifted Jacobi polynomials. Using the operational matrix of the fractional derivative, the problem can be reduced to a set of linear algebraic equations. Numerical examples are included to demonstrate the validity and applicability of the technique and a comparison is made with the existing results to show that the proposed method is easy to implement and produce accurate results.


title: Robust recognition of chess-boards under deformation

abstract: Current methods for formation of detected chess-board vertices into a grid structure tend to be weak in situations with a warped grid, and false and missing vertex-features. In this paper we present a highly robust, yet efficient, scheme suitable for inference of regular 2D square mesh structure from vertices recorded both during projection of a chess-board pattern onto 3D objects, and in the more simple case of camera calibration. Examples of the method's performance in a lung function measuring application, observing chess-boards projected on to patients' chests, are given. The method presented is resilient to significant surface deformation, and tolerates inexact vertex-feature detection. This robustness results from the scheme's novel exploitation of feature orientation information.


title: Hawkes processes in finance

abstract: In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high frequency finance this last decade. After a reminder of the main definitions and properties that characterize Hawkes processes, we review their main empirical applications to address many different problems in high frequency finance. Because of their great flexibility and versatility, we show that they have been successfully involved in issues as diverse as estimating the volatility at the level of transaction data, estimating the market stability, accounting for systemic risk contagion, devising optimal execution strategies or capturing the dynamics of the full order book.


title: A machine learning framework for network anomaly detection using SVM and GA

abstract: In today's world of computer security, Internet attacks such as Dos/DDos, worms, and spyware continue to evolve as detection techniques improve. It is not easy, however, to distinguish such new attacks using only knowledge of pre-existing attacks. In this paper the authors focused on machine learning techniques for detecting attacks from Internet anomalies. The machine learning framework consists of two major components: genetic algorithm (GA) for feature selection and support vector machine (SVM) for packet classification. By experiment it is also demonstrated that the proposed framework outperforms currently employed real-world NIDS.


title: PREVALENCE OF ACADEMIC PROCRASTINATION BEHAVIOR AMONG PRE-SERVICE TEACHERS, AND ITS RELATIONSHIP WITH DEMOGRAPHICS AND INDIVIDUAL PREFERENCES (AKADEM(K ERTELEME DAVRANI/ININ Ö1RETMEN ADAYLARI ARASINDAK( YAYGINLI1I, DEMOGRAF(K ÖZELL(KLER VE B(REYSEL TERC(HLERLE (L(/K(S()

abstract: aim of this study is to investigate the prevalence of academic procrastination behavior among pre- service teachers, and its relationship with demographics and individual preferences. The participants were 580 students who were studying in different major fields at the Faculty of Education in Pamukkale University. The age range varied from 19 to 28. In this study, Aitken's Procrastination Inventory and Personal Information Sheet were used to gather data. The analysis showed that 23 % of pre-service teachers exhibited procrastination behavior at a high level. Other findings indicated that procrastination behavior significantly differed by gender, time preferences for studying courses and exams, and was negatively related to academic achievement. Implications of the findings are discussed and some suggestions are made for the educator and counselors.


title: A computational model of perceived agency in video games

abstract: Agency, being one's ability to perform an action and have some influence over the world, is fundamental to interactive entertainment. Although much of the games industry is concerned with providing more agency to its players, what seems to matter more is how much agency each player will actually perceive. In this paper, we present a computational model of this phenomena, based on the notion that the amount of agency that one perceives depends on how much they desire the outcomes that result from their decisions. Using a structure for high-agency stories that we designed specifically for this intent, we present the results of a 141-participant user study that tests our model's ability to select subsequent events in an original interactive story. Using a newly validated survey instrument for measuring both agency and fun, we found with a high degree of confidence that event sequences selected by our model result in players perceiving more agency than players who experience event sequences that our model does not recommend.


title: Footwear Size Recommendation System

abstract: While shopping for fashion products, customers usually prefer to try-out products to examine fit, material, overall look and feel. Due to lack of try out options during online shopping, it becomes pivotal to provide customers with as much of this information as possible to enhance their shopping experience. Also it becomes essential to provide same experience for new customers. Our work here focuses on providing a production ready size recommendation system for shoes and address the challenge of providing recommendation for users with no previous purchases on the platform. In our work, we present a probabilistic approach based on user co-purchase data facilitated by generating a brand-brand relationship graph. Specifically we address two challenges that are commonly faced while implementing such solution. 1. Sparse signals for less popular or new products in the system 2. Extending the solution for new users. Further we compare and contrast this approach with our previous work and show significant improvement both in recommendation precision and coverage.


title: MultiDroid: An Energy Optimization Technique for Multi-Window Operations on OLED Smartphones

abstract: The inbuilt multi-window feature released with Android Nougat has enabled simultaneous working and visualization of multiple applications on mobile display screen. However, multi-window operations result in unnecessary energy drain due to increased multitasking, CPU load, and multithread processing. Considering linear dependence between power consumed and the displayed colors, we present the design and realization of MultiDroid , a novel display power reduction technique during multi-window operations for the OLED screens. MultiDroid employs dynamic local dimming based on the context switching between the application pairs displayed on the screen. The display optimization works based on the user interaction with the mobile screen, where dynamic changes on the display screens are implemented for the non-critical application window. MultiDroid architecture is validated through power modeling and correctness verification. Furthermore, a survey on multi-window usage has been conducted reflecting the application usage pattern. The performance of MultiDroid is evaluated on preferred application pairs on multi-window framework obtained from the survey. Modeling and comparative analysis of the energy profiles for the devised test cases on optimized and default multi-window screens reflect around 10% to 25% reduction in the overall device power consumption per hour with a negligible performance degradation. After experimental validation, we present user acceptance and feasibility of MultiDroid based on the feedback from 50 participants.


title: ECG arrhythmia classification using a 2-D convolutional neural network

abstract: In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN models; AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were used for the evaluation of the classifier. As a result, our classifier achieved 99.05% average accuracy with 97.85% average sensitivity. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent classification accuracy without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.


title: Collecting, organizing, and sharing pins in pinterest: interest-driven or social-driven?

abstract: Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.


title: The Essential Nature of Product Traceability and its Relation to Agile Approaches

abstract: Abstract This is a discussion of the essential features of product development traceability maps in relation to requirements, architecture, functional models, components, and tests as a set of order type hierarchies and their cross-links. This paper lays out the structure of these ideal traceability relationships that define the essence of the product under development. The importance of the trace relationships to the product is clarified and then the abandonment of traceability in the Agile approach is discussed. Following that, a way to transform between synthetic canonical narrative (story) representations that appear in the product backlog and the traditionally separate hierarchical form of the trace structure of the product will be examined. The fact that it is possible to transform back and forth between the canonic narrative and traditional hierarchical representations of trace structures, and the fact that trace structures can be produced in a ‘just in time’ fashion that evolves during product development demonstrate that these trace structures can be used in both an Agile and Lean fashion within the development process. Also, we can show that when the trace structure is produced outside the narrative representation it can have the additional benefit of helping to determine the precedent order of development so that rework can be avoided. The lack of the extrinsic external trace structure of the product that gives access to its intelligibility is, in fact, a form of technical debt. Thus, traditional trace structures using this model can be seen as an essential tool for product owners to produce sound and coherent development narratives and for structuring and prioritizing the backlog in the Agile and Lean approaches to software and systems development.


title: The Challenge of Education and Learning in the Developing World

abstract: Across many different contexts, randomized evaluations find that school participation is sensitive to costs: Reducing out-of-pocket costs, merit scholarships, and conditional cash transfers all increase schooling. Addressing child health and providing information on how earnings rise with education can increase schooling even more cost-effectively. However, among those in school, test scores are remarkably low and unresponsive to more-of-the-same inputs, such as hiring additional teachers, buying more textbooks, or providing flexible grants. In contrast, pedagogical reforms that match teaching to students’ learning levels are highly cost effective at increasing learning, as are reforms that improve accountability and incentives, such as local hiring of teachers on short-term contracts. Technology could potentially improve pedagogy and accountability. Improving pre- and postprimary education are major future challenges.


title: Taking the Blame Game out of Data Centers Operations with NetPoirot

abstract: Today, root cause analysis of failures in data centers is mostly done through manual inspection. More often than not, cus- tomers blame the network as the culprit. However, other components of the system might have caused these failures. To troubleshoot, huge volumes of data are collected over the entire data center. Correlating such large volumes of diverse data collected from different vantage points is a daunting task even for the most skilled technicians. In this paper, we revisit the question: how much can you infer about a failure in the data center using TCP statistics collected at one of the endpoints? Using an agent that cap- tures TCP statistics we devised a classification algorithm that identifies the root cause of failure using this information at a single endpoint. Using insights derived from this classi- fication algorithm we identify dominant TCP metrics that indicate where/why problems occur in the network. We val- idate and test these methods using data that we collect over a period of six months in a production data center.


title: Information-theoretic tools for mining database structure from large data sets

abstract: Data design has been characterized as a process of arriving at a design that maximizes the information content of each piece of data (or equivalently, one that minimizes redundancy). Information content (or redundancy) is measured with respect to a prescribed model for the data, a model that is often expressed as a set of constraints. In this work, we consider the problem of doing data redesign in an environment where the prescribed model is unknown or incomplete. Specifically, we consider the problem of finding structural clues in an instance of data, an instance which may contain errors, missing values, and duplicate records. We propose a set of information-theoretic tools for finding structural summaries that are useful in characterizing the information content of the data, and ultimately useful in data design. We provide algorithms for creating these summaries over large, categorical data sets. We study the use of these summaries in one specific physical design task, that of ranking functional dependencies based on their data redundancy. We show how our ranking can be used by a physical data-design tool to find good vertical decompositions of a relation (decompositions that improve the information content of the design). We present an evaluation of the approach on real data sets.


title: Utilizing the Social and Behavioral Sciences to Assess, Model, Forecast and Preemptively Respond to Terrorism

abstract: To stay ahead of the terrorist threat curve, we need to utilize leading edge and innovative conceptual methodologies and software-based systems that are grounded in the social and behavioral sciences. It is by means of theoretically grounded, conceptually precise, methodologically rigorous, and analytically oriented research that we can attain the capability to fully understand the underlying conditions that give rise to terrorist insurgencies and the measures required to contain such threats. Such a comprehensive and multidisciplinary approach will enable us to understand how to assess, model, forecast and preemptively respond to current and future terrorist threats. Some of these leading edge methodologies and software systems may already be available – although not yet applied to the terrorism/combating terrorism discipline – while others may yet to be developed. To achieve the capability required to win the war on terrorism, we must first substantially improve and strengthen our social and behavioral scientific baseline understanding of all the root causes underlying the terrorist-based conflicts, as well as the organizational formations, nodes and linkages involved in how terrorists operate and conduct warfare.


title: VHDL synthesizable hardware architecture design of back propagation neural networks

abstract: Evolutionary Algorithms (EA) are used in many optimization problems such as the Artificial Neural Networks (ANN). But the main challenging issue of using these algorithms is the time taken for computing the function value, implementation and verification of hardware architecture design. In this paper we propose to implement the architecture design of Back Propagation Neural (BPN) networks using Very High Speed Integrated Circuits Hardware Description Language (VHDL). The simulation is carried out using Xilinx Spartan 3E.


title: Bluetooth positioning using RSSI and triangulation methods

abstract: Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.


title: A clustering scheme for hierarchical control in multi-hop wireless networks

abstract: In this paper we present a clustering scheme to create a hierarchical control structure for multi-hop wireless networks. A cluster is defined as a subset of vertices, whose induced graph is connected. In addition, a cluster is required to obey certain constraints that are useful for management and scalability of the hierarchy. All these constraints cannot be met simultaneously for general graphs, but we show how such a clustering can be obtained for wireless network topologies. Finally, we present an efficient distributed implementation of our clustering algorithm for a set of wireless nodes to create the set of desired clusters.


title: 1.2-V Supply, 100-nW, 1.09-V Bandgap and 0.7-V Supply, 52.5-nW, 0.55-V Subbandgap Reference Circuits for Nanowatt CMOS LSIs

abstract: This paper presents bandgap reference (BGR) and sub-BGR circuits for nanowatt LSIs. The circuits consist of a nano-ampere current reference circuit, a bipolar transistor, and proportional-to-absolute-temperature (PTAT) voltage generators. The proposed circuits avoid the use of resistors and contain only MOSFETs and one bipolar transistor. Because the sub-BGR circuit divides the output voltage of the bipolar transistor without resistors, it can operate at a sub-1-V supply. The experimental results obtained in the 0.18-μm CMOS process demonstrated that the BGR circuit could generate a reference voltage of 1.09 V and the sub-BGR circuit could generate one of 0.548 V. The power dissipations of the BGR and sub-BGR circuits corresponded to 100 and 52.5 nW.


title: AutonoVi-Sim: Autonomous Vehicle Simulation Platform with Weather, Sensing, and Traffic Control

abstract: We present AutonoVi-Sim, a novel high-fidelity simulation platform for autonomous driving data generation and driving strategy testing. AutonoVi-Sim is a collection of high-level extensible modules which allows the rapid development and testing of vehicle configurations and facilitates construction of complex traffic scenarios. Autonovi-Sim supports multiple vehicles with unique steering or acceleration limits, as well as unique tire parameters and dynamics profiles. Engineers can specify the specific vehicle sensor systems and vary time of day and weather conditions to generate robust data and gain insight into how conditions affect the performance of a particular algorithm. In addition, AutonoVi-Sim supports navigation for non-vehicle traffic participants such as cyclists and pedestrians, allowing engineers to specify routes for these actors, or to create scripted scenarios which place the vehicle in dangerous reactive situations. Autonovi-Sim facilitates training of deep-learning algorithms by enabling data export from the vehicle's sensors, including camera data, LIDAR, relative positions of traffic participants, and detection and classification results. Thus, AutonoVi-Sim allows for the rapid prototyping, development and testing of autonomous driving algorithms under varying vehicle, road, traffic, and weather conditions. In this paper, we detail the simulator and provide specific performance and data benchmarks.


title: Integrating multi-criteria decision making and clustering for business customer segmentation

abstract: Purpose – The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings – Business customers of an international original equipmen...


title: A 46 $\mu \text{W}$ 13 b 6.4 MS/s SAR ADC With Background Mismatch and Offset Calibration

abstract: A 6.4 MS/s 13 b ADC with a low-power background calibration for DAC mismatch and comparator offset errors is presented. Redundancy deals with DAC settling and facilitates calibration. A two-mode comparator and 0.3 fF capacitors reduce power and area. The background calibration can directly detect the sign of the dynamic comparator offset error and the DAC mismatch errors and correct both of them simultaneously in a stepwise feedback loop. The calibration achieves 20 dB spur reduction with little area and power overhead. The chip is implemented in 40 nm CMOS and consumes 46 $\mu \text{W}$ from a 1 V supply, and achieves 64.1 dB SNDR and a FoM of 5.5 fJ/conversion-step at Nyquist.


title: Concept and Design of A Fully Autonomous Sewer Pipe Inspection Mobile Robot "KANTARO"

abstract: In current conventional method, the sewer pipe inspection is undertaken using a cable-tethered robot with an on-board video camera system, completely, tele-operated by human operator. All commercial sewer inspection robots have platforms with poor mobility functions so that those robots are only capable to move into the straight pipes. Inspecting the sewage pipes using the state of the arts inspection methods by the current robots is costly, mostly human cost, and not fast enough to check and inspect the amount of sewage pipes will grow stronger than it has actually happened, specially in Japan. In order to realize inexpensive and effective inspection system, an autonomous pipe inspection method should be introduced to improve the inspection efficiency by reducing the time and manpower in the inspection process. The development of a fully autonomous pipe inspection system, requires design and development of an un-tethered mobile robot equipped with the required sensors using for autonomous pipe assessment and damage detection, and capability of navigating, completely, autonomously inside of sewer networks including different types of pipe-bends such as curves and junctions. KANTARO, presented in this paper, is the prototype of a passive-active intelligent, fully autonomous, un-tethered robot which has an intelligent modular architecture in its sensor and mechanism. KANTARO prototype robot, including a novel passive-active intelligent moving mechanism, can move into the straight pipe and pass various kinds of pipe bends without need to any intelligence of the controller or sensor reading. In order to realize a fully autonomous inspection robot, we also developed a small and intelligent 2D laser scanner for detecting of the navigational landmarks, independently with the main computer system, and fusion with a fish eye camera to assess the pipe state and fault detection.


title: Image super-resolution as sparse representation of raw image patches

abstract: This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image. We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods.


Bidirectional Relationship Between Reduced Blood pH and Acute Pancreatitis: A Translational Study of Their Noxious Combination


title: A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

abstract: Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques-over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)-along with four popular classification methods-logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.


title: LTE/SAE Security Issues on 4G Wireless Networks

abstract: The authors give an overview on the state of the art of potential security issues that occur in the deployment of the LTE/SAE (Long-Term Evolution/System Architecture Evolution) protocol in emerging 4G wireless technologies. Although security concerns and challenges in wireless networks will remain a hot topic in the future, the LTE/SAE standard could adapt to these rising challenges, becoming more robust and secure. By looking at the authentication and ciphering algorithms, such as EAP-AKA (Extensible Authentication Protocol for Authentication and Key Agreement), currently operating within the LTE protocol, the authors analyze several vulnerabilities in LTE/SAE security architecture—specifically, insecure AKA key derivation procedures and the lack of fast reauthentications during handovers.


title: Pulsed eddy-current response to a conducting half-space

abstract: Eddy-current nondestructive evaluation commonly carried out using single frequency time harmonic excitations, but a pulsed excitation offers a simple and effective alternative. The pulse signals have been calculated for a probe coil whose current rises and falls exponentially, approximating a square wave when the exponential time constant is small. Predictions of the induced electromotive force (EMF) across a coil above a half-space conductor and of the magnetic field on the coil axis have been compared with experiments. The comparison shows excellent agreement between theory and experiment.


Verb Replacer: An English Verb Error Correction System


title: EvoDAG: A semantic Genetic Programming Python library

abstract: Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been considerable interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we describe EvoDAG (Evolving Directed Acyclic Graph) which is a Python library that implements a steady-state semantic Genetic Programming with tournament selection using an extension of our previous crossover operators based on orthogonal projections in the phenotype space. To show the effectiveness of EvoDAG, it is compared against state-of-the-art classifiers on different benchmark problems, experimental results indicate that EvoDAG is very competitive.


title: Multi-task GLOH feature selection for human age estimation

abstract: In this paper, we propose a novel age estimation method based on gradient location and orientation histogram (GLOH) descriptor and multi-task learning (MTL). The GLOH, one of the state-of-the-art local descriptor, is used to capture the age-related local and spatial information of face image. As the extracted GLOH features are often redundant, MTL is designed to select the most informative GLOH bins for age estimation problem, while the corresponding weights are determined by ridge regression. This approach largely reduces the dimensions of feature, which can not only improve performance but also decrease the computational burden. Experiments on the public available FG-NET database show that the proposed method can achieve comparable performance over previous approaches while using much fewer features.


title: Automatically Solving Number Word Problems by Semantic Parsing and Reasoning

abstract: This paper presents a semantic parsing and reasoning approach to automatically solving math word problems. A new meaning representation language is designed to bridge natural language text and math expressions. A CFG parser is implemented based on 9,600 semi-automatically created grammar rules. We conduct experiments on a test set of over 1,500 number word problems (i.e., verbally expressed number problems) and yield 95.4% precision and 60.2% recall.


title: Improving the Transcription of Academic Lectures for Information Retrieval

abstract: Recording university lectures through lecture capture systems is increasingly common, generating large amounts of audio and video data. Transcribing recordings greatly enhances their usefulness by making them easy to search. However, the number of recordings accumulates rapidly, rendering manual transcription impractical. Automatic transcription, on the other hand, suffers from low levels of accuracy, partly due to the special language of academic disciplines, which standard language models do not cover. This paper looks into the use of Wikipedia to dynamically adapt language models for scholarly speech. We propose Ranked Word Correct Rate as a new metric better aligned with the goals of improving transcript search ability and specialist word recognition. The study shows that, while overall transcription accuracy may remain low, targeted language modelling can substantially improve search ability, an important goal in its own right.


title: Methods for fault tolerance in networks-on-chip

abstract: Networks-on-Chip constitute the interconnection architecture of future, massively parallel multiprocessors that assemble hundreds to thousands of processing cores on a single chip. Their integration is enabled by ongoing miniaturization of chip manufacturing technologies following Moore's Law. It comes with the downside of the circuit elements' increased susceptibility to failure. Research on fault-tolerant Networks-on-Chip tries to mitigate partial failure and its effect on network performance and reliability by exploiting various forms of redundancy at the suitable network layers. The article at hand reviews the failure mechanisms, fault models, diagnosis techniques, and fault-tolerance methods in on-chip networks, and surveys and summarizes the research of the last ten years. It is structured along three communication layers: the data link, the network, and the transport layers. The most important results are summarized and open research problems and challenges are highlighted to guide future research on this topic.


title: Action-Reaction: Forecasting the Dynamics of Human Interaction

abstract: Forecasting human activities from visual evidence is an emerging area of research which aims to allow computational systems to make predictions about unseen human actions. We explore the task of activity forecasting in the context of dual-agent interactions to understand how the actions of one person can be used to predict the actions of another. We model dual-agent interactions as an optimal control problem, where the actions of the initiating agent induce a cost topology over the space of reactive poses – a space in which the reactive agent plans an optimal pose trajectory. The technique developed in this work employs a kernel-based reinforcement learning approximation of the soft maximum value function to deal with the high-dimensional nature of human motion and applies a mean-shift procedure over a continuous cost function to infer a smooth reaction sequence. Experimental results show that our proposed method is able to properly model human interactions in a high dimensional space of human poses. When compared to several baseline models, results show that our method is able to generate highly plausible simulations of human interaction.


title: A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

abstract: In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.


Developing innovative applications in agriculture using data mining


title: The first draft genome of the aquatic model plant Lemna minor opens the route for future stress physiology research and biotechnological applications

abstract: Freshwater duckweed, comprising the smallest, fastest growing and simplest macrophytes has various applications in agriculture, phytoremediation and energy production. Lemna minor, the so-called common duckweed, is a model system of these aquatic plants for ecotoxicological bioassays, genetic transformation tools and industrial applications. Given the ecotoxic relevance and high potential for biomass production, whole-genome information of this cosmopolitan duckweed is needed. The 472 Mbp assembly of the L. minor genome (2n = 40; estimated 481 Mbp; 98.1 %) contains 22,382 protein-coding genes and 61.5 % repetitive sequences. The repeat content explains 94.5 % of the genome size difference in comparison with the greater duckweed, Spirodela polyrhiza (2n = 40; 158 Mbp; 19,623 protein-coding genes; and 15.79 % repetitive sequences). Comparison of proteins from other monocot plants, protein ortholog identification, OrthoMCL, suggests 1356 duckweed-specific groups (3367 proteins, 15.0 % total L. minor proteins) and 795 Lemna-specific groups (2897 proteins, 12.9 % total L. minor proteins). Interestingly, proteins involved in biosynthetic processes in response to various stimuli and hydrolase activities are enriched in the Lemna proteome in comparison with the Spirodela proteome. The genome sequence and annotation of L. minor protein-coding genes provide new insights in biological understanding and biomass production applications of Lemna species.


title: Underwater reconstruction using depth sensors

abstract: In this paper we describe experiments in which we acquire range images of underwater surfaces with four types of depth sensors and attempt to reconstruct underwater surfaces. Two conditions are tested: acquiring range images by submersing the sensors and by holding the sensors over the water line and recording through water. We found out that only the Kinect sensor is able to acquire depth images of submersed surfaces by holding the sensor above water. We compare the reconstructed underwater geometry with meshes obtained when the surfaces were not submersed. These findings show that 3D underwater reconstruction using depth sensors is possible, despite the high water absorption of the near infrared spectrum in which these sensors operate.


title: Learning Shape Priors for Single-View 3D Completion and Reconstruction

abstract: The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.


title: Unbiased scalable softmax optimization

abstract: Recent neural network and language models have begun to rely on softmax distributions with an extremely large number of categories. In this context calculating the softmax normalizing constant is prohibitively expensive. This has spurred a growing literature of efficiently computable but biased estimates of the softmax. In this paper we present the first two unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and does not require extra work at the end of each epoch). We compare our unbiased methods' empirical performance to the state-of-the-art on seven real world datasets, where they comprehensively outperform all competitors.


title: An 802.11 a/b/g/n digital fractional-N PLL with automatic TDC linearity calibration for spur cancellation

abstract: A fractional-N digital phase-locked loop (PLL) architecture with low fractional spur is presented in this paper. A 2-D Vernier time-to-digital convertor (TDC) is implemented to achieve wide detection range with fine resolution. The TDC is calibrated automatically utilizing the ramp signal generated from the fractional-N accumulator for optimal linearity. A digi-phase spur cancellation technique with automatic TDC gain tracking is also implemented to further suppress the fractional spurs. The chip also includes an improved multimodulus divider (MMD) structure that overcomes the glitch problem during division ratio toggling associated with the prior art MMDs, enabling carrier synthesis across wide frequency range continuously. As part of an 802.11a/b/g/n transceiver, the DPLL can provide coverage for both 2.4/5 G WiFi bands. The proposed fractional-N DPLL is implemented in a 55-nm CMOS technology. The DPLL achieves a largest fractional spur level of −55 dBc without using a sigma–delta modulator and an in-band phase noise of −107 dBc/Hz (0.55 ps integrated jitter) while consuming 9.9 mW.


Intelligent Web Crawling


title: Security issues and vulnerabilities in connected car systems

abstract: The Connected Revolution has reached the automotive industry and the Internet penetrates into the modern vehicles. Formerly acquiring data from a vehicle was the tool of Fleet Management Systems handling commercial vehicles. In the recent years connectivity began to appear in the passenger vehicles also. The first features were infotainment and navigation, having low security needs remaining far from the vehicular networks. Then telematics and remote control, such as keyless entry appeared and created a new security threat in the vehicle. The paper shows how the connected feature changes the vehicle and also presents vulnerabilities of each element to show the importance of cautious system security design.


title: A novel power efficient N-MOS based 1-bit full adder

abstract: A full adder circuit is considered as one of the basic building blocks of Digital Signal Processors (DSPs), Arithmetic and Logic Units (ALUs), Application Specific Integrated Circuits (ASICs) and many other digital circuits and systems. Today, efficient full adder circuit design is one of the main challenges for VLSI engineers. This paper proposes a novel 1-bit full adder circuit designed using N-MOS based pass transistors logic. This proposed full adder has been simulated using 0.35um CMOS process technology at 2.5V supply voltage with ISIM and also with DSCH 2.6c simulator. The results of the post layout simulation (using MICROWIND 2.6a) have been compared with the results of similar previously reported adder circuits. The result analysis shows that the proposed adder circuit is highly efficient in terms of power, delay and area consumption, compared to many other full adder circuits.


Tense and Aspect in Runyankore Using a Context-Free Grammar


title: Smart Jacket Design for Neonatal Monitoring with Wearable Sensors

abstract: Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless communication. The smart jacket aims for providing reliable health monitoring as well as a comfortable clinical environment for neonatal care and parent-child interaction. We present the first version of the neonatal jacket that enables ECG measurement by textile electrodes. We also explore a new solution for skin-contact challenges that textile electrodes pose. The jacket is expandable with new wearable technologies and has aesthetics that appeal to parents and medical staff. An iterative design process in close contact with the users and experts lead to a balanced integration of technology, user focus and aesthetics. We demonstrate the prototype and the experimental results obtained in clinical setting.


title: Point inversion and projection for NURBS curve and surface: Control polygon approach

abstract: This paper presents an accurate and efficient method to solve both point projection and point inversion for NURBS curves and surfaces. We first subdivide the NURBS curve or surface into a set of Bezier subcurves or patches. Based on the relationship between the test point and the control polygon of Bezier curve or the control point net of the Bezier patch, we extract candidate Bezier subcurves or Bezier patches and then find the approximate candidate points. Finally, by comparing the distances between the test point and candidate points, we are able to find the closest point. We improve its accuracy by using the Newton-Raphson method.


title: State-of-the-art: software inspections after 25 years

abstract: Software inspections, which were originally developed by Michael Fagan in 1976, are an important means to verify and achieve sufficient quality in many software projects today. Since Fagan's initial work, the importance of software inspections has been long recognized by software developers and many organizations. Various proposals have been made by researchers in the hope of improving Fagan's inspection method. The proposals include structural changes to the process and several types of support for the inspection process. Most of the proposals have been empirically investigated in different studies. This is a review paper focusing on the software inspection process in the light of Fagan's inspection method and it summarizes and reviews other types of software inspection processes that have emerged in the last 25 years. This paper also addresses important issues related to the inspection process and examines experimental studies and their findings that are of interest with the purpose of identifying future avenues of research in software inspection.


title: Medical Image Synthesis with Context-Aware Generative Adversarial Networks

abstract: Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.


title: Insights into Internet Memes

abstract: Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings.


title: Synthetic fibers in atmospheric fallout: A source of microplastics in the environment?

abstract: Sources, pathways and reservoirs of microplastics, plastic particles smaller than 5mm, remain poorly documented in an urban context. While some studies pointed out wastewater treatment plants as a potential pathway of microplastics, none have focused on the atmospheric compartment. In this work, the atmospheric fallout of microplastics was investigated in two different urban and sub-urban sites. Microplastics were collected continuously with a stainless steel funnel. Samples were then filtered and observed with a stereomicroscope. Fibers accounted for almost all the microplastics collected. An atmospheric fallout between 2 and 355 particles/m(2)/day was highlighted. Registered fluxes were systematically higher at the urban than at the sub-urban site. Chemical characterization allowed to estimate at 29% the proportion of these fibers being all synthetic (made with petrochemicals), or a mixture of natural and synthetic material. Extrapolation using weight and volume estimates of the collected fibers, allowed a rough estimation showing that between 3 and 10 tons of fibers are deposited by atmospheric fallout at the scale of the Parisian agglomeration every year (2500 km(2)). These results could serve the scientific community working on the different sources of microplastic in both continental and marine environments.