title: Performance-influence models for highly configurable systems

abstract: Almost every complex software system today is configurable. While configurability has many benefits, it challenges performance prediction, optimization, and debugging. Often, the influences of individual configuration options on performance are unknown. Worse, configuration options may interact, giving rise to a configuration space of possibly exponential size. Addressing this challenge, we propose an approach that derives a performance-influence model for a given configurable system, describing all relevant influences of configuration options and their interactions. Our approach combines machine-learning and sampling heuristics in a novel way. It improves over standard techniques in that it (1) represents influences of options and their interactions explicitly (which eases debugging), (2) smoothly integrates binary and numeric configuration options for the first time, (3) incorporates domain knowledge, if available (which eases learning and increases accuracy), (4) considers complex constraints among options, and (5) systematically reduces the solution space to a tractable size. A series of experiments demonstrates the feasibility of our approach in terms of the accuracy of the models learned as well as the accuracy of the performance predictions one can make with them.


title: Noise Analysis, Calculation, and Reduction of External Rotor Permanent-Magnet Synchronous Motor

abstract: A detailed analysis of electromagnetic noise in external rotor permanent-magnet synchronous motors is presented in this paper. First, the spatial distribution and frequency characteristics of the electromagnetic force acting on the surface of the permanent magnet are discussed. Then, calculation models for electromagnetic force, structural vibration, and acoustic radiation are developed to predict noise by taking an external rotor in-wheel motor as example. The uneven distribution of electromagnetic force on the surface of the permanent magnet is taken into account by means of loading nodal force into the structural model which is verified by modal test. Through the mode superposition method, the vibration on the surface of the outer rotor is calculated, and the acoustic boundary element method is used to predict the acoustic radiation. Noise test is conducted to validate the simulated noise. It is shown in both simulation results and noise test that the electromagnetic force due to slotting effects contributes the most remarkable component to the overall noise. Finally, slot opening width is optimized to reduce the amplitude of magnetic force close to resonance frequencies, and the overall sound pressure level decreases by 6 dB(A) after optimization.


title: Strategies for distant speech recognitionin reverberant environments

abstract: Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.


title: Enhancing underwater images and videos by fusion

abstract: This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.


title: Causal Best Intervention Identification via Importance Sampling

abstract: Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in a causal DAG, to maximize the expected value of a target node $Y$ (downstream of $V$). There is a fixed total budget for sampling under various interventions. Also, there are cost constraints on different types of interventions. We pose this as a best arm identification problem with $K$ arms, where each arm is a soft intervention at $V$. The key difference from the classical setting is that there is information leakage among the arms. Each soft intervention is a distinct known conditional probability distribution between $V$ and its parents $pa(V)$. ::: We propose an efficient algorithm that uses importance sampling to adaptively sample using different interventions and leverage information leakage to choose the best. We provide the first gap dependent simple regret and best arm mis-identification error bounds for this problem. This generalizes the prior bounds available for the simpler case of no information leakage. In the case of no leakage, the number of samples required for identification is (upto polylog factors) $\tilde{O} (\max_i \frac{i}{\Delta_i^2})$ where $\Delta_i$ is the gap between the optimal and the $i$-th best arm. We generalize the previous result for the causal setting and show that $\tilde{O}(\max_i \frac{\sigma_i^2}{\Delta_i^2})$ is sufficient where $\sigma_i^2$ is the effective variance of an importance sampling estimator that eliminates the $i$-th best arm out of a set of arms with gaps roughly at most twice $\Delta_i$. We also show that $\sigma_i^2 << i$ in many cases. Our result also recovers (up to constants) prior gap independent bounds for this setting. We demonstrate that our algorithm empirically outperforms the state of the art, through synthetic experiments.


title: Architecture challenges for software ecosystems

abstract: Successful software products and product lines exhibit a force of gravity that attracts external developers. The organization developing the product actively seeks to involve external developers to add functionality to the system. On the other hand, external developers flock to a successful product as it provides an established market where underserved niches provide a business opportunity for these players. The product or product line evolves into a platform for external developers. This causes several software architecture challenges for a software ecosystem, including interface stability, evolution management, guaranteeing security and reliability and composition of independently developed functionality. The paper describes these challenges, discusses practical solutions and identifies research challenges.


title: Sleepless in Fairfax: the difference one more hour of sleep can make for teen hopelessness, suicidal ideation, and substance use

abstract: Insufficient sleep is a risk factor for depression, suicidality, and substance use, yet little is known about gender, ethnic, and community-level differences in sleep and its associated outcomes, especially during adolescence. Further, much of the prior work has compared groups of teens getting plenty as opposed to insufficient amounts of sleep rather than examine sleep hours continuously. The present study examined adolescent weekday self-reported sleep duration and its links with hopelessness, suicidality, and substance use in a suburban community with very early high school start times. We utilized a large (N = 27,939, 51.2 % female) and ethnically diverse sample of adolescents from the 2009 Fairfax County (Virginia) Youth Survey, an anonymous, self-report, population-level survey administered to all 8th, 10th and 12th grade students in public schools in the county. High-school students reported an average 6.5 h of sleep per school night, with 20 % obtaining ≤5 h, and only 3 % reporting the recommended 9 h/night. Females and minority youth obtained even less sleep on average, and the reduction in sleep in the transition from middle school to high school was more pronounced for females and for Asian students. Hierarchical, multivariate, logistic regression analyses, controlling for background variables, indicated that just 1 h less of weekday sleep was associated with significantly greater odds of feeling hopeless, seriously considering suicide, suicide attempts, and substance use. Relationships between sleep duration and suicidality were stronger for male teens, and sleep duration was more associated with hopelessness for white students compared to most ethnic minority groups. Implications for intervention at multiple levels are discussed.


title: Understanding Performance of Edge Content Caching for Mobile Video Streaming

abstract: Today’s Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks—from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users’ homes)—to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU.


title: STAR-Vote: A Secure, Transparent, Auditable, and Reliable Voting System

abstract: STAR-Vote is a collaboration between a number of academics and the Travis County (Austin), Texas elections office, which currently uses a DRE voting system and previously used an optical scan voting system. STAR-Vote represents a rare opportunity for a variety of sophisticated technologies, such as end-to-end cryptography and risk limiting audits, to be designed into a new voting system, from scratch, with a variety of real world constraints, such as election-day vote centers that must support thousands of ballot styles and run all day in the event of a power failure. This paper describes the current design of STAR-Vote which is now largely settled and whose development will soon begin.


title: Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man

abstract: Abstract—We present an application of Monte-Carlo TreeSearch (MCTS) for the game of Ms Pac-Man. Contrary tomost applications of MCTS to date, Ms Pac-Man requiresalmost real-time decision making and does not have a naturalend state. We approached the problem by performing Monte-Carlo tree searches on a 5 player max n tree representationof the game with limited tree search depth. We performed anumber of experiments using both the MCTS game agents (forpacman and ghosts) and agents used in previous work (forghosts). Performance-wise, our approach gets excellent scores,outperforming previous non-MCTS opponent approaches to thegame by up to two orders of magnitude.Index Terms—pac-man, MCTS, max-n, Monte-Carlo I. I NTRODUCTION With the recent success of Monte-Carlo Tree Search(MCTS) in Computer Go [1], [2] and General Game Playing(GGP) [3], there has been a growing interest in applyingthese techniques to other board-like strategy games such asHex [4]. However, there has been very limited research inthe application of MCTS in real time video games. In thispaper we investigate the use of Monte-Carlo Tree Search inthe game Ms Pac-Man, a predator/prey-like game popularisedin the 80s. From an AI research perspective, there are manyaspects of the game worthy of study, but in this particularpaper we are mostly focused on the performance of MCTS ina real time game setting. The rest of the paper is organisedas follows: in the next section (section II), we discuss thegame of Ms Pac-Man and the previous research on this game;in Section III, we describe our Ms Pac-Man simulator andthe experimental setup done in order to perform simulationson the game; in section IV we discuss MCTS in more depthtogether with some of its previous applications; in section Vwe present the methodology and rationale used in order tocreate the current set of agents. In section VI we perform foursets of experiments and present the results. Finally, sectionVII summarises the contributions of this research, and givesdirections for future developments. Throughout the text as aconvention, we use the word “pac-man” to refer to the pac-man agent, while “Ms Pac-Man” and “Pac-Man” (capitalisedﬁrst letter) refer to the actual game.II. M


title: Word-and Sentence-level Confidence Measures for Machine Translation

abstract: A machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an estimation of the probability of correctness. In this article we describe several word- and sentence-level confidence measures relying on different features: mutual information between words, n-gram and backward n-gram language models, and linguistic features. We also try different combination of these measures. Their accuracy is evaluated on a classification task. We achieve 17% error-rate (0.84 f-measure) on wordlevel and 31% error-rate (0.71 f-measure) on sentence-level.


title: Production of formulaic expressions in L2 Chinese: A developmental investigation in a study abroad context

abstract: This study investigated the development of L2 Chinese formulaic com- petence in a study abroad context. Participants were 31 American students study - ing Chinese in a university in China (intermediate-level). They completed a com- puterized speaking test consisting of 24 formulae-use situations twice during their semester-long study abroad in China. The learners produced a formulaic expression according to each situation, and their production was evaluated on appropriateness (rated on a four-point scale by native speakers) and planning time. In addition, a survey was administered to gather information about the learners' perceived frequency of encounter with formulae-use situations. The learners showed significant gains on appropriateness and fluency. Reported fre- quency of encounter with target formulae-use situations did not correlate with the gains in formulae production, except for the learners with lower pretest score. Qualitative analysis revealed four patterns of change: (1) change toward target formulae, (2) change toward target-like slot-and-frame patterns, (3) change to- ward non-target formulae; and (4) stabilized non-target formulae use.


title: From proto-mimesis to language: Evidence from primatology and social neuroscience

abstract: How can we reconcile the conception of language as a conventional-normative semiotic system with a perception/action-based account of its structure and meaning? And why should linguistic meaning - as opposed to linguistic expression - be so closely related to motor activity and its neural underpinnings, as suggested by recent findings? A conceptual framework and evolutionary scenario building on the concept of bodily mimesis [Zlatev, J., 2005. What's in a schema? Bodily mimesis and the grounding of language. In: Hampe, B. (Ed.), From Perception to Meaning: Image Schemas in Cognitive Linguistics. Mouton de Gruyter, Berlin, pp. 313-343] imply answers to these questions. The article presents evidence for a particular evolutionary stage model by reviewing recent evidence on the capacity of non-human primates for intersubjectivity, imitation and gestural communication, and from neuroscientific studies of these capacities in monkeys and human subjects. It is argued that "mirror neuron" systems can subserve basic motoric and social capacities, but they need to be considerably extended in order to provide an efficient basis for bodily mimesis, and even more so for language. It is argued that while language may be ultimately "grounded" in perception and action, it is essential not to try to reduce it to them. (Less)


Archaeoastronomical Evidence for Wuism at the Hongshan Site of Niuheliang


title: An Approach to Text Summarization.

abstract: We propose an efficient text summarization technique that involves two basic operations. The first operation involves finding coherent chunks in the document and the second operation involves ranking the text in the individual coherent chunks and picking the sentences that rank above a given threshold. The coherent chunks are formed by exploiting the lexical relationship between adjacent sentences in the document. Occurrence of words through repetition or relatedness by sense relation plays a major role in forming a cohesive tie. The proposed text ranking approach is based on a graph theoretic ranking model applied to text summarization task.


title: Neural Activity in the Human Brain Relating to Uncertainty and Arousal during Anticipation

abstract: We used functional magnetic resonance neuroimaging to measure brain activity during delay between reward-related decisions and their outcomes, and the modulation of this delay activity by uncertainty and arousal. Feedback, indicating financial gain or loss, was given following a fixed delay. Anticipatory arousal was indexed by galvanic skin conductance. Delay-period activity was associated with bilateral activation in orbital and medial prefrontal, temporal, and right parietal cortices. During delay, activity in anterior cingulate and orbitofrontal cortices was modulated by outcome uncertainty, whereas anterior cingulate, dorsolateral prefrontal, and parietal cortices activity was modulated by degree of anticipatory arousal. A distinct region of anterior cingulate was commonly activated by both uncertainty and arousal. Our findings highlight distinct contributions of cognitive uncertainty and autonomic arousal to anticipatory neural activity in prefrontal cortex.


Information Security Risk Analysis – a Matrix-based Approach


title: Evaluation of implementation of enterprise resource planning information system with DeLone and McLean model approach

abstract: This study aimed to examine and analyze the variables that influence the implementation of Enterprise Resource Planning Information Systems at PT. Telkom Indonesia Tbk. This study used DeLone and McLean Information Systems Success Models (D&M IS Success Model), in which the success of information systems was proxied by user satisfaction. While the variables that influence the success of information system tested in this study were system quality, information quality and service quality. In addition, this study also tested and analyzed the effect of information systems success on the net benefits. This research was causal (conclusive). The data collection was conducted by distributing questionnaires to users of ERP SAP in PT Telkom for 189 people consisting of 116 finance and controlling modules users, 16 of human resources modules users and 57 of materials management module users. Data processing method was performed by Structural Equation Modelling (SEM) with partial least square (PLS) approach by using Smart PLS 3.0 software. In this study, it was identified that the user satisfaction of Enterprise Resources Planning (ERP) information systems in this model can be explained by the quality system, the quality of information and the service quality by 60.5% in the order from the greatest influence was the quality of information (path 0.361), the system quality (path 0,260) and the last was the variable of service quality (path 0,253). In addition, the net benefits of the implementation of Enterprise Resources Planning (ERP) information systems, both the benefits for individual's user as well as for the enterprise can be explained by the user satisfaction variable for 61.8%.


title: Joint Extraction Of Entities And Relations For Opinion Recognition

abstract: We present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. We identify two types of opinion-related entities --- expressions of opinions and sources of opinions --- along with the linking relation that exists between them. Inspired by Roth and Yih (2004), we employ an integer linear programming approach to solve the joint opinion recognition task, and show that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities. Performance further improves when a semantic role labeling system is incorporated. The resulting system achieves F-measures of 79 and 69 for entity and relation extraction, respectively, improving substantially over prior results in the area.


title: Detection and localization of persons behind obstacles using M-sequence through-the-wall radar

abstract: We describe the architecture and design of a through-the-wall radar. The radar is applied for the detection and localization of people hidden behind obstacles. It implements a new adaptive processing technique for people detection, which is introduced in this article. This processing technique is based on exponential averaging with adopted weighting coefficients. Through-the-wall detection and localization of a moving person is demonstrated by a measurement example. The localization relies on the time-of-flight approach.


title: Gene selection with guided regularized random forest

abstract: The regularized random forest (RRF) was recently proposed for feature selection by building only one ensemble. In RRF the features are evaluated on a part of the training data at each tree node. We derive an upper bound for the number of distinct Gini information gain values in a node, and show that many features can share the same information gain at a node with a small number of instances and a large number of features. Therefore, in a node with a small number of instances, RRF is likely to select a feature not strongly relevant. Here an enhanced RRF, referred to as the guided RRF (GRRF), is proposed. In GRRF, the importance scores from an ordinary random forest (RF) are used to guide the feature selection process in RRF. Experiments on 10 gene data sets show that the accuracy performance of GRRF is, in general, more robust than RRF when their parameters change. GRRF is computationally efficient, can select compact feature subsets, and has competitive accuracy performance, compared to RRF, varSelRF and LASSO logistic regression (with evaluations from an RF classifier). Also, RF applied to the features selected by RRF with the minimal regularization outperforms RF applied to all the features for most of the data sets considered here. Therefore, if accuracy is considered more important than the size of the feature subset, RRF with the minimal regularization may be considered. We use the accuracy performance of RF, a strong classifier, to evaluate feature selection methods, and illustrate that weak classifiers are less capable of capturing the information contained in a feature subset. Both RRF and GRRF were implemented in the "RRF" R package available at CRAN, the official R package archive.


title: Attachment style, gender, and relationship stability: A longitudinal analysis.

abstract: Adult attachment styles in 354 heterosexual couples in serious dating relationships were examined. Principal findings included the following: (a) male and female attachment styles were nonrandomly paired, for example, no anxious-anxious or avoidant-avoidant pairs were found; (b) male and female styles related to concurrent relationship ratings of both partners in different but theoretically meaningful ways; (c) male and female styles contributed significantly to longitudinal prediction of relationship stability and status, even when prior duration and commitment to the relationship were statistically controlled; (d) specifically, relationships of avoidant men and of anxious women were surprisingly stable over 3 years, particularly in light of the relatively poor ratings of these relationships by both partners at Time 1


title: Discriminative out-of-distribution detection for semantic segmentation

abstract: Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes. However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input. These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive. We propose to address this issue by discriminative detection of OOD pixels in input data. Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task. Instead, we train a dedicated OOD model which discriminates the primary training set from a much larger"background"dataset which approximates the variety of the visual world. We perform our experiments on high resolution natural images in a dense prediction setup. We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset. We evaluate our approach on WildDash test, which is currently the only public test dataset that includes out-of-distribution images. The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.


title: Dynamic Sparse Graph for Efficient Deep Learning

abstract: We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the previous studies optimize for inference while neglect training or even complicate it. Training is far more intractable, since (i) the neurons dominate the memory cost rather than the weights in inference; (ii) the dynamic activation makes previous sparse acceleration via one-off optimization on fixed weight invalid; (iii) batch normalization (BN) is critical for maintaining accuracy while its activation reorganization damages the sparsity. To address these issues, DSG activates only a small amount of neurons with high selectivity at each iteration via a dimension-reduction search (DRS) and obtains the BN compatibility via a double-mask selection (DMS). Experiments show significant memory saving (1.7-4.5x) and operation reduction (2.3-4.4x) with little accuracy loss on various benchmarks.


title: Learning Over Long Time Lags

abstract: The advantage of recurrent neural networks (RNNs) in learning dependencies between time-series data has distinguished RNNs from other deep learning models. Recently, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on memory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short term memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancements and learning techniques for capturing long term dependencies in RNNs.


title: Linking Named Entities to Any Database

abstract: Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25% in accuracy.


title: Systems for Detecting Advanced Persistent Threats: A Development Roadmap Using Intelligent Data Analysis

abstract: Cyber-attacks against companies and governments are increasing in complexity, persistence and numbers. Common intrusion detection methods lack the ability to detect such - what are commonly termed - advanced persistent threats. A new approach is needed that takes the stepwise characteristics of this type of threats into account and links analysis methods to attack features. This paper takes up this challenge. First, an analysis framework is proposed to relate complex attack attributes to detection and business aspects. Second, the framework is used to define a development roadmap for designing advanced intrusion detection systems, such systems can analyze network traffic and client data at multiple network locations using both signature and anomaly detection methods derived from the intelligent data analysis field. Third, a test case is provided showing the potential power of the proposed development roadmap.


title: 3D distance fields: a survey of techniques and applications

abstract: A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?.


abstract: Classification and Correction of Alar-Columellar Discrepancies in Rhinoplasty Jack Gunter;Rod Rohrich;Ronald Friedman; Plastic and Reconstructive Surgery


title: Ontology based Scene Creation for the Development of Automated Vehicles

abstract: The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.


Kinematics and dynamics analysis of micro-robot for surgical applications


Memory‐Efficient Interactive Online Reconstruction From Depth Image Streams


abstract: A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. Its an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing.Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialistsThis bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aidsSaves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications.Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.


title: A novel simple IGBT model for power electronic systems EMI simulation

abstract: Insulated Gate Bipolar Transistors (IGBTs) have been widely used in power electronic systems, such as switched mode power supplies, electric vehicles' motor drives and so on. However, the high switching speed of the IGBT causes high levels of electromagnetic interference (EMI). This paper suggests a simple model with sufficient accuracy to predict the power electronics systems EMI levels. The predicted spectra of both voltage and current match the measurement results. Using the system time-domain simulation with the suggested model, the relationship between the layout of the converter and the EMI level is analyzed.


title: Performance Evaluation of a Closed Loop OFDM-MIMO Transmit Preprocessing Scheme Using Pilot Symbol Assisted Rateless Codes to Mitigate Fading in Rayleigh Fading Channels

abstract: The principal design problem encountered in wireless communications is how to overcome the distortion of received signals by time-varying propagation path due to the phenomenon called fading. Also, a well known problem of orthogonal frequency division multiplexing (OFDM) i.e the occurrence of high peak- to-average power ratio (PAPR) in the time domain signal reduces the efficiency of transmit high power amplifier.In this paper, we propose a closed loop orthogonal frequency division multiplexing multiple- input multiple-output (MIMO) transmit preprocessing system, where the channel coding method referred as pilot symbol assisted rateless coding (PSAR) is used to mitigate the effects of fading and three PAPR reduction methods are used and the best method to reduce PAPR is chosen. The performance of proposed system is measured in terms of bit error rate (BER) and complementary cumulative distribution function (CCDF) for PAPR. The proposed system achieves about 3 dB improvement in PAPR over the traditional OFDM.


title: Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?

abstract: In order to obtain optimal 3D structure and viewing parameter estimates, bundle adjustment is often used as the last step of feature-based structure and motion estimation algorithms. Bundle adjustment involves the formulation of a large scale, yet sparse minimization problem, which is traditionally solved using a sparse variant of the Levenberg-Marquardt optimization algorithm that avoids storing and operating on zero entries. This paper argues that considerable computational benefits can be gained by substituting the sparse Levenberg-Marquardt algorithm in the implementation of bundle adjustment with a sparse variant of Powell's dog leg non-linear least squares technique. Detailed comparative experimental results provide strong evidence supporting this claim


title: A Pole-Zero Placement Technique for Designing Second-Order IIR Parametric Equalizer Filters

abstract: A new procedure is presented for designing second-order parametric equalizer filters. In contrast to the traditional approach, in which the design is based on a bilinear transform of an analog filter, the presented procedure allows for designing the filter directly in the digital domain. A rather intuitive technique known as pole-zero placement, is treated here in a quantitative way. It is shown that by making some meaningful approximations, a set of relatively simple design equations can be obtained. Design examples of both notch and resonance filters are included to illustrate the performance of the proposed method and to compare with state-of-the-art solutions.


title: Efficient Storage of Multi-Sensor Object-Tracking Data

abstract: The rapid development of Internet of Things (IoT) enables people to track objects by deploying multiple sensors, e.g., to track people in indoor spaces using RFID sensors. Multi-sensor object-tracking data are usually produced as records, which are thereby organized into small files and written to servers. However, the small-size property and high arriving-rate of multi-sensor object-tracking data will result in poor I/O performance of file systems such as HDFS. In this paper, we propose the first read/write-optimized solution for storing multi-sensor object-tracking data on HDFS. In particular, we exploit a distributed caching mechanism and a parallel file-merging policy to improve the I/O performance of HDFS. With our design, object-tracking data are first cached by a Distributed Memory File System (DMFS) on top of HDFS. These data are further merged into large files, which are then flushed to HDFS in parallel. We demonstrate that this mechanism is able to improve the write throughput of HDFS and outperforms existing centralized-cache-based approaches. In addition, in order to improve the search performance of object queries over multi-sensor object-tracking data, we propose a Sensor-Dependence Graph (SDG) to model sensor dependence and further present an SDG-based algorithm to efficiently cluster sensors. The object-tracking data from the sensors in the same cluster are merged into the same large files, which can reduce file scans during query processing and therefore improve search performance. We conduct extensive experiments to evaluate the performance of our proposal. The results suggest the efficiency of our proposal with respect to disk-write throughput, memory-write throughput, search performance, and sensor clustering.


title: A Traffic Accident Recording and Reporting Model at Intersections

abstract: In this paper, we suggested a vision-based traffic accident detection algorithm and developed a system for automatically detecting, recording, and reporting traffic accidents at intersections. A system with these properties would be beneficial in determining the cause of accidents and the features of an intersection that impact safety. This model first extracts the vehicles from the video image of the charge-couple-device camera, tracks the moving vehicles (MVs), and extracts features such as the variation rate of the velocity, position, area, and direction of MVs. The model then makes decisions on the traffic accident based on the extracted features. In a field test, the suggested model achieved a correct detection rate (CDR) of 50% and a detection rate of 60%. Considering that a sound-based accident detection system showed a CDR of 1% and a DR of 66.1%, our result is a remarkable achievement


Deep Probabilistic Modeling of Natural Images using a Pyramid Decomposition.


title: Recoil ion mass spectrometry. Part 2. Formation of slow, multiply charged recoil ions in collisions of fast negative ions with Ar and Kr atoms

abstract: Mass spectrometric studies have been conducted on slow, multiply charged recoil ions produced in collisions of O−, Si−, C− and C−2 ions with neutral beams of Ar and Kr atoms at impact energies in the range 40–70 keV. A crossed-beams apparatus incorporating a quadrupole mass spectrometer has been coupled to the mass-selective negative ion injection system of a tandem heavy-ion accelerator for these studies. In contrast to the situation pertaining to multiple ionisation in collisions with fast positively charged projectiles, where indirect ionisation mechanisms, such as excitation—autoionisation and Auger rearrangement processes may dominate multielectron ejection, the dependence of the relative multiple ionisation cross sections on the nature of the projectile species in the present experiments appears to indicate that in ionisation by negative ions, the overall electron density distribution in the projectile—target system may play a very significant role.


title: Control of wind turbine's pitch angle based on DFIG by using MRAC and PIP controller

abstract: In recent decades by reducing the fossil fuel sources, use of new energy and wind energy in particular, has attracted many attentions. The most important factors affecting the productivity of wind turbines is wind speed and direction that is constantly changing. One method for power control of wind turbines is change the angle of the wind turbine blade, proportional to the wind speed changes. In this paper in order to control of wind turbine's pitch, has been used of an adaptive method based on model reference control and this result compared with PIP control. The results show that PIP controller compared to adaptive controller has a weaker performance. The results show that the tracking error of model reference adaptive control improved about 25.64%. According to the results adaptive method has lesser than other classic methods and production power at varying wind speeds is reached faster to desired level.


title: Randomized Switched Antenna Array FMCW Radar for Automotive Applications

abstract: Frequency-modulated continuous-wave (FMCW) methods and switched antenna arrays (SAAs) are very attractive for application in automotive radar because of their low hardware complexity. In traditional SAA radar, the receiving antenna array elements are successively switched according to their natural spatial order [referred to as sequential SAAs (SSAAs)]. For radars using both SSAA and FMCW methods, the phase difference caused by the target azimuth and time delay will be difficult to distinguish, thus resulting in delay-space coupling, which may lead to degradation of the road target observation. In this paper, randomized SAA (RSAA) FMCW radar is proposed. The new radar scheme has high delay/space resolution for target location detection and character extraction, and can also solve the coupling problem. The signal model of RSAA FMCW radar is derived and compared with that of SSAA radar. The influence of the antenna-array switching mode on the radar performance is also analyzed. A conventional zero-padding algorithm and a new clustering FOCUSS algorithm are both introduced for the signal processing of the new radar scheme. The numerical results illustrate the performance of RSAA FMCW radar in high-resolution target imaging and delay-space decoupling.


title: CLIP: Continuous Location Integrity and Provenance for Mobile Phones

abstract: Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.


title: Incremental blind feedback: An effective approach to automatic query expansion

abstract: Automatic query expansion (AQE) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose a novel AQE algorithm which first adopts a systematic incremental approach to choose feedback documents from the top retrieved set and then selects the expansion terms aggregating the scores from each feedback set. We also devise a term selection measure and a number of weighting schemes based on easily computable features. A set of experiments with a large number of standard test collections reveals that the proposed incremental blind feedback algorithm outperforms a number of state-of-the-art query expansion methods with remarkable significance and consistency.


title: Wireless LAN Intrusion Detection and Prevention system for Malicious Access Point

abstract: In current trends, A Malicious AP might be set in any open spaces so as to imitate honest to goodness APs for adaptation. Existing malicious AP detection methods analyze wireless traffic by using extra devices, and the data of traffic is collected by servers. Malicious Access Points, if undetected, can take important data from the network. So many attackers took advantages of the undetected Malicious Access Points in ventures to not just get free Internet. In this paper, we consider the problem of “malicious” attacks in wireless local area networks (WLANs). A Malicious is essentially a rogue (phishing) Wi-Fi access point (AP) that looks like a legitimate one (with the same SSID) and we consider an unprotected client, other networks client to avoid wireless unauthorized clients.


title: Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image

abstract: In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the networks outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.


title: Narrative Prose Generation

abstract: Narrative generation has historically suffered from poor writing quality, stemming from a narrow focus on story grammars and plot design. Moreover, to-date natural language generation systems have not been capable of faithfully reproducing either the variety or complexity of naturally occurring narratives. In this article we first propose a model of narrative derived from work in narratology and grounded in observed linguistic phenomena. Next we describe the AUTHOR architecture for narrative generation and an end-to-end implementation of the AUTHOR model in the STORYBOOK narrative prose generation system. Finally, we present a formal evaluation of the narratives that STORYBOOK produces.


title: KNN-DTW Based Missing Value Imputation for Microarray Time Series Data

abstract: Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.


title: Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research

abstract: Typologies are an important way of organizing the complex cause-effect relationships that are key building blocks of the strategy and organization literatures. Here, I develop a novel theoretical perspective on causal core and periphery, which is based on how elements of a configuration are connected to outcomes. Using data on hightechnology firms, I empirically investigate configurations based on the Miles and Snow typology using fuzzy set qualitative comparative analysis (fsQCA). My findings show how the theoretical perspective developed here allows for a detailed analysis of causal core, periphery, and asymmetry, shifting the focus to midrange theories of causal processes.


title: Likelihood-informed dimension reduction for nonlinear inverse problems

abstract: The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively low-dimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the"likelihood-informed subspace"(LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao-Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.


title: Compression stockings to prevent post-thrombotic syndrome: a randomised placebo-controlled trial

abstract: BACKGROUND ::: Post-thrombotic syndrome (PTS) is a common and burdensome complication of deep venous thrombosis (DVT). Previous trials suggesting benefit of elastic compression stockings (ECS) to prevent PTS were small, single-centre studies without placebo control. We aimed to assess the efficacy of ECS, compared with placebo stockings, for the prevention of PTS. ::: ::: ::: METHODS ::: We did a multicentre randomised placebo-controlled trial of active versus placebo ECS used for 2 years to prevent PTS after a first proximal DVT in centres in Canada and the USA. Patients were randomly assigned to study groups with a web-based randomisation system. Patients presenting with a first symptomatic, proximal DVT were potentially eligible to participate. They were excluded if the use of compression stockings was contraindicated, they had an expected lifespan of less than 6 months, geographical inaccessibility precluded return for follow-up visits, they were unable to apply stockings, or they received thrombolytic therapy for the initial treatment of acute DVT. The primary outcome was PTS diagnosed at 6 months or later using Ginsberg's criteria (leg pain and swelling of ≥1 month duration). We used a modified intention to treat Cox regression analysis, supplemented by a prespecified per-protocol analysis of patients who reported frequent use of their allocated treatment. This study is registered with ClinicalTrials.gov, number NCT00143598, and Current Controlled Trials, number ISRCTN71334751. ::: ::: ::: FINDINGS ::: From 2004 to 2010, 410 patients were randomly assigned to receive active ECS and 396 placebo ECS. The cumulative incidence of PTS was 14·2% in active ECS versus 12·7% in placebo ECS (hazard ratio adjusted for centre 1·13, 95% CI 0·73-1·76; p=0·58). Results were similar in a prespecified per-protocol analysis of patients who reported frequent use of stockings. ::: ::: ::: INTERPRETATION ::: ECS did not prevent PTS after a first proximal DVT, hence our findings do not support routine wearing of ECS after DVT. ::: ::: ::: FUNDING ::: Canadian Institutes of Health Research.


title: PTSD symptoms, demographic characteristics, and functional status among veterans treated in VA primary care clinics

abstract: We hypothesized that PTSD symptomatology would have an inverse relationship with functional status and would vary as a function of sociodemographic variables. Primary care patients (N = 513) at two VA Medical Centers were randomly selected and recruited to participate. After adjustment for other demographic variables, PTSD symptom levels were significantly related to age (younger patients had more severe symptoms), employment status (disabled persons had higher symptom levels), war zone experience, and clinic location. PTSD symptomatology was inversely related to mental and physical functioning, even after control for potential confounding. These findings have implications for screening and service delivery in VA primary care clinics, and support the more general finding in the literature that PTSD is associated with impaired functioning.


title: Multilingual NMT with a language-independent attention bridge

abstract: In this paper, we propose a multilingual encoder-decoder architecture capable of obtaining multilingual sentence representations by means of incorporating an intermediate {\em attention bridge} that is shared across all languages. That is, we train the model with language-specific encoders and decoders that are connected via self-attention with a shared layer that we call attention bridge. This layer exploits the semantics from each language for performing translation and develops into a language-independent meaning representation that can efficiently be used for transfer learning. We present a new framework for the efficient development of multilingual NMT using this model and scheduled training. We have tested the approach in a systematic way with a multi-parallel data set. We show that the model achieves substantial improvements over strong bilingual models and that it also works well for zero-shot translation, which demonstrates its ability of abstraction and transfer learning.


title: PISCES: A Programmable, Protocol-Independent Software Switch

abstract: Hypervisors use software switches to steer packets to and from virtual machines (VMs). These switches frequently need upgrading and customization—to support new protocol headers or encapsulations for tunneling and overlays, to improve measurement and debugging features, and even to add middlebox-like functions. Software switches are typically based on a large body of code, including kernel code, and changing the switch is a formidable undertaking requiring domain mastery of network protocol design and developing, testing, and maintaining a large, complex codebase. Changing how a software switch forwards packets should not require intimate knowledge of its implementation. Instead, it should be possible to specify how packets are processed and forwarded in a high-level domain-specific language (DSL) such as P4, and compiled to run on a software switch. We present PISCES, a software switch derived from Open vSwitch (OVS), a hard-wired hypervisor switch, whose behavior is customized using P4. PISCES is not hard-wired to specific protocols; this independence makes it easy to add new features. We also show how the compiler can analyze the high-level specification to optimize forwarding performance. Our evaluation shows that PISCES performs comparably to OVS and that PISCES programs are about 40 times shorter than equivalent changes to OVS source code.


Blockchain Technology Acceptance in Electronic Medical Record System


title: Understanding user activity patterns of the Swarm app: a data-driven study

abstract: Location-based social apps have been widely used by people to share their location information with friends. These apps provide rich spatial-temporal information for researchers to investigate user activity patterns. In this work, we collect check-in data from Swarm, and analyze the user behavior in a way of combining spatial and temporal features of check-ins. The results reveal users' different preferences for venue categories in different time of the day. Our work presents activity patterns of human behavior and the distinctions of life habits among three cities, Hong Kong, New York City, and San Francisco. Our findings can be further applied to Swarm's incentive mechanism and recommendation systems.


title: An Analysis of the Elastic Net Approach to the Traveling Salesman Problem

abstract: This paper analyzes the elastic net approach (Durbin and Willshaw 1987) to the traveling salesman problem of finding the shortest path through a set of cities. The elastic net approach jointly minimizes the length of an arbitrary path in the plane and the distance between the path points and the cities. The tradeoff between these two requirements is controlled by a scale parameter K. A global minimum is found for large K, and is then tracked to a small value. In this paper, we show that (1) in the small K limit the elastic path passes arbitrarily close to all the cities, but that only one path point is attracted to each city, (2) in the large K limit the net lies at the center of the set of cities, and (3) at a critical value of K the energy function bifurcates. We also show that this method can be interpreted in terms of extremizing a probability distribution controlled by K. The minimum at a given K corresponds to the maximum a posteriori (MAP) Bayesian estimate of the tour under a natural statistical interpretation. The analysis presented in this paper gives us a better understanding of the behavior of the elastic net, allows us to better choose the parameters for the optimization, and suggests how to extend the underlying ideas to other domains.


title: Longitudinal effects of media violence on aggression and empathy among German adolescents

abstract: To address the longitudinal relation between adolescents' habitual usage of media violence and aggressive behavior and empathy, N = 1237 seventh and eighth grade high school students in Germany completed measures of violent and nonviolent media usage, aggression, and empathy twice in twelve months. Cross-lagged panel analyses showed significant pathways from T1 media violence usage to higher physical aggression and lower empathy at T2. The reverse paths from T1 aggression or empathy to T2 media violence usage were nonsignificant. The links were similar for boys and girls. No links were found between exposure to nonviolent media and aggression or between violent media and relational aggression. T1 physical aggression moderated the impact of media violence usage, with stronger effects of media violence usage among the low aggression group.


title: Spotlight on dream recall: the ages of dreams

abstract: Brain and sleep maturation covary across different stages of life. At the same time, dream generation and dream recall are intrinsically dependent on the development of neural systems. The aim of this paper is to review the existing studies about dreaming in infancy, adulthood, and the elderly stage of life, assessing whether dream mentation may reflect changes of the underlying cerebral activity and cognitive processes. It should be mentioned that some evidence from childhood investigations, albeit still weak and contrasting, revealed a certain correlation between cognitive skills and specific features of dream reports. In this respect, infantile amnesia, confabulatory reports, dream-reality discerning, and limitation in language production and emotional comprehension should be considered as important confounding factors. Differently, growing evidence in adults suggests that the neurophysiological mechanisms underlying the encoding and retrieval of episodic memories may remain the same across different states of consciousness. More directly, some studies on adults point to shared neural mechanisms between waking cognition and corresponding dream features. A general decline in the dream recall frequency is commonly reported in the elderly, and it is explained in terms of a diminished interest in dreaming and in its emotional salience. Although empirical evidence is not yet available, an alternative hypothesis associates this reduction to an age-related cognitive decline. The state of the art of the existing knowledge is partially due to the variety of methods used to investigate dream experience. Very few studies in elderly and no investigations in childhood have been performed to understand whether dream recall is related to specific electrophysiological pattern at different ages. Most of all, the lack of longitudinal psychophysiological studies seems to be the main issue. As a main message, we suggest that future longitudinal studies should collect dream reports upon awakening from different sleep states and include neurobiological measures with cognitive performances.


A Critical Review of Surveys Emphasizing on Routing in Wireless Sensor Networks—An Anatomization under General Survey Design Framework


title: A Wideband Circularly Polarized Antenna for Microwave and Millimeter-Wave Applications

abstract: A wideband circularly polarized antenna with a single feed is achieved by adjusting the shapes and dimensions of a linearly polarized magneto-electric (ME) dipole antenna. Experimentally, an antenna prototype operating at around 2 GHz exhibits a wide impedance bandwidth of 73.3% for SWR ≤ 2 and 46.6% for SWR ≤ 1.5, a 3-dB axial ratio bandwidth of 47.7% and an antenna gain of 6.8 ± 1.8 dBic. Based on the design operating at lower microwave frequencies, a millimeter-wave antenna is designed on a dielectric substrate. The antenna has a wide impedance bandwidth (SWR ≤ 2) of 56.7% covering 38.5 to 69 GHz and a 3-dB axial ratio bandwidth of 41% covering 45.8 to 69.4 GHz, over which the antenna boresight gain varies from 5 to 9.9 dBic. This validates that the design can be realized on a single-layer printed circuit board.


Toward Topic Search on the Web


Feasibility of an interactive voice response system for monitoring depressive symptoms in a lower-middle income Latin American country


title: Aircraft Cavity-Backed Nonprotruding Wideband Antenna

abstract: A novel circular aperture antenna is presented. The proposed nonprotruding antenna is composed of a cylindrical cavity embedding an inverted hat antenna whose profile is defined by three elliptical segments. This profile can be used to control and shape the return loss response over a wide frequency band (31% at -15 dB). The present design combines the benefits of a low-profile antenna and the broad bandwidth of the inverted hat monopole. A parametric analysis of the antenna is conducted. Omnidirectional monopole-like radiation pattern and vertical polarization are also verified with measurements and simulations. This antenna constitutes a low drag candidate for a distance measurement equipment (DME) aircraft navigation system.


title: Adaptive Admittance Control for Human–Robot Interaction Using Model Reference Design and Adaptive Inverse Filtering

abstract: Corobotics involves humans and robots working collaboratively as a team. This requires physical human-robot interaction (pHRI) systems that can adapt to the preferences of different humans and have good robustness and stability properties. In this brief, a new inner-loop/outer-loop robot controller formulation is developed that makes pHRI robust to changes in both corobot and human user. First, an inner-loop controller with guaranteed robustness and stability causes a robot to behave like a prescribed admittance model. Second, an outer-loop controller tunes the admittance model so that the robot system assists humans with varying levels of skill to achieve task-specific objectives. This design technique cleanly separates robot-specific control from task performance objectives and allows formal inclusion in an outer design of both an ideal task model and unknown human operator dynamics. Experimental results with the controllers running on a PR2 robot demonstrate the effectiveness of this approach.


title: Adversarial Learning for Chinese NER from Crowd Annotations

abstract: To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. In this paper, we propose an approach to performing crowd annotation learning for Chinese Named Entity Recognition (NER) to make full use of the noisy sequence labels from multiple annotators. Inspired by adversarial learning, our approach uses a common Bi-LSTM and a private Bi-LSTM for representing annotator-generic and -specific information. The annotator-generic information is the common knowledge for entities easily mastered by the crowd. Finally, we build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we create two data sets for Chinese NER tasks from two domains. The experimental results show that our system achieves better scores than strong baseline systems.


title: Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance

abstract: In this paper, we study 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the lacking of a representational capability and the training difficulty impede 1-bit CNNs from performing as well as real-valued networks. To this end, we propose Bi-Real net with a novel training algorithm to tackle these two challenges. To enhance the representational capability, we propagate the real-valued activations generated by each 1-bit convolution via a parameter-free shortcut. To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware binarization for weight updating, a better initialization method, and a two-step scheme for training a deep network. Experiments on ImageNet show that an 18-layer Bi-Real net with the proposed training algorithm achieves 56.4% top-1 classification accuracy, which is 10% higher than the state-of-the-arts (e.g., XNOR-Net), with a greater memory saving and a lower computational cost. Bi-Real net is also the first to scale up 1-bit CNNs to an ultra-deep network with 152 layers, and achieves 64.5% top-1 accuracy on ImageNet. A 50-layer Bi-Real net shows comparable performance to a real-valued network on the depth estimation task with merely a 0.3% accuracy gap.


title: Network Neuroscience Theory of Human Intelligence

abstract: An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g , emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g , represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation.


title: Finite State Machine based Vending Machine Controller with Auto-Billing Features

abstract: Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM) modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper the process of four state (user Selection, Waiting for money insertion, product delivery and servicing) has been modelled using MEALY Machine Model. The proposed model is tested using Spartan 3 development board and its performance is compared with CMOS based machine.


title: Book Review: Deep Learning

abstract: This book offers a solution to more intuitive problems in these areas. These solutions allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relationship to simpler concepts. By gathering knowledge from experience, this approach avoids the need for human operators to specify formally all of the knowledge needed by the computer. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If the authors draw a graph to show how these concepts have been built on top of each other, the graph will be deep, with many layers. For this reason, the authors call this approach “AI Deep Learning.”


title: Chemical etching of InP and GaInAsP for fabricating laser diodes and integrated optical circuits.

abstract: Chemical etching techniques were developed for fabricating GaInAsP/InP DH lasers which aim at single (transverse and longitudinal) mode oscillations and their integrations. About fifty to sixty etching solutions were tested by examining the flatness of the etched surface. The volume ratio 1 : 2 : 1 in a combination of HCl, CH3COOH, and H2O2 was found to be most suitable for our purpose. Its etching rate for InP and GaxIn1-xAsyP1-y was in proportion to t0.6~0.8 at about 20°C, where t is the etching time, and it also etched Au/Zn or Au/Sn electrodes with an etching rate of 10-20 A/s at about 20°C.


title: Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition

abstract: The proliferate unstructured data generated in online social networks leads to significant research advances in the recognition of user profiles (e.g., age, gender, ethnicity, etc.), but meanwhile brings new challenges. These attributes are referred to as soft biometrics and provide a semantic description of users. Identifying users' soft biometric traits in online social networks is crucial for a variety of applications such as customized marketing, personalized recommendation, and urban planning. Compared to conventional studies, we address the importance and challenges of identifying soft biometrics in online social networks and provide a case study on gender recognition of Twitter users based on their tweets texts and profile images. We first apply an efficient approach to label the soft biometric attributes of users using their self-reported names, instead of labor-forced methodology such as manually labeling and crowdsourcing. Then we investigate the approaches using texts and profile images, respectively, including Hashtag-based TF-IDF, LDA Topic Distribution, SIFT-based TF-IDF and Convolutional Neural Networks (CNN). Finally, we propose an ensemble method by combining models learned from both texts and images to enhance gender identification. The performance in our extensive experiments is demonstrated to be comparative with state-of the-art work.


title: The relationship of logistics to supply chain management: developing a common industry definition

abstract: Over the past few years there has been confusion and disagreement among general business practitioners and operations professionals concerning the terms “logistics” and “supply chain management”. Various formal definitions have been offered for both terms. In addition, the common usage of each term in industry varies. Business terms are often defined over time, by the common use or application of the term. Examines the historical definitions of both terms, looks at current practitioner views of the terms, and proposes a hierarchy for the relationship between logistics and supply chain management.


title: NFR Modeling Approaches

abstract: Non-functional requirements (NFRs) are one of the major concerns in system requirement engineering. NFRs must be considered for a successful software project and thus modeling of NFRs is of equal importance. Unfortunately, most of the available literature is focused on modeling of functional requirements and comparatively less effort is done on modeling of non-functional requirements. In this paper we have conducted a literature survey to find the different approaches available to model non-functional requirements and concluded (on the basis of a quality criterion) that which approach is better than others for this purpose.


title: Preprocessing for image classification by convolutional neural networks

abstract: In recent times, the Convolutional Neural Networks have become the most powerful method for image classification. Various researchers have shown the importance of network architecture in achieving better performances by making changes in different layers of the network. Some have shown the importance of the neuron's activation by using various types of activation functions. But here we have shown the importance of preprocessing techniques for image classification using the CIFAR10 dataset and three variations of the Convolutional Neural Network. The results that we have achieved, clearly shows that the Zero Component Analysis(ZCA) outperforms both the Mean Normalization and Standardization techniques for all the three networks and thus it is the most important preprocessing technique for image classification with Convolutional Neural Networks.


title: CiteSense: supporting sensemaking of research literature

abstract: Making sense of research literature is a complicated process that involves various information seeking and compre-hension tasks. The lack of support for sensemaking in existing systems presents important design challenges and opportunities. This research proposes the design of an integral environment to support literature search, selection, organization and comprehension. Our system prototype, CiteSense, offers lightweight interaction tools and a smooth transition among various information activities. This research deepens our understanding of the design of systems that support the sensemaking of research literature.


title: Context-Aware Movie Recommendations: An Empirical Comparison of Pre-Filtering, Post-Filtering and Contextual Modeling Approaches

abstract: Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.


title: Methodologies for fast yield ramp with limited engineering resources utilizing Inline Defect data overlay to SRAM Bitmap failure and Logic Diagnostics

abstract: Yield ramp is a significant metric in determining the profitability of the semiconductor industry. Defect inspection methodologies play a major role in detecting key defect modes inline before functional testing. A fast, production ready Yield learning methodology is needed to quantify the impact of inline detected and non-detected defects and to provide a way of determining the priority of processes. This information helps the engineering teams to highlight critical yield-limiting defects and facilitate an understanding of failure modes. Current conventional strategies have limitations of inaccuracy and larger cycle time. The methodologies presented in this paper utilize Inline Defect data overlay to SRAM Bitmap failure and Logic Diagnostics for fast yield ramp in limited engineering resources. These methodologies have demonstrated exceptional cycle time reduction on cutting edge 28nm, 20nm and 14nm test chips including customer products.


title: Microservices: yesterday, today, and tomorrow

abstract: Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity. Before presenting the current state of the art in the field, this chapter reviews the history of software architecture, the reasons that led to the diffusion of objects and services first, and microservices later. Finally, open problems and future challenges are introduced. This survey primarily addresses newcomers to the discipline, while offering an academic viewpoint on the topic. In addition, we investigate some practical issues and point out a few potential solutions.


title: FACTORS INFLUENCING CONSUMERS' ATTITUDE TOWARDS E-COMMERCE PURCHASES THROUGH ONLINE SHOPPING

abstract: Online shopping is the process of buying goods and services from merchants who sell on the internet. Shoppers can visit web stores from the comfort of their homes and shop as they sit in front of the computer. The main purpose of this study is to determine the factors influencing consumers’ attitude towards e-commerce purchases through online shopping. The study also investigate how socio-demographic (age, income and occupation), pattern of online buying (types of goods, e-commerce experience and hours use on internet) and purchase perception (product perception, customers’ service and consumers’ risk) affect consumers’ attitude towards online shopping. Convenience sampling method was conducted in this study and the sample comparison of 100 respondents in Taman Tawas Permai, Ipoh. Data were collected via self-administered questionnaire which contains 15 questions in Part A (respondents’ background and their pattern of using internet and online buying), 34 questions in Part B (attitude towards online purchase) and 36 questions in Part C (purchase perception towards online shopping). One-way ANOVA were used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods) and dependant variable such as attitude towards online shopping. The findings revealed that there is no significant difference in attitude towards online shopping among age group (F = 1.020, p 0.05). The research finding also showed that there is no significant difference in attitude towards online shopping among occupation group (F = 1.607, p 0.05). This study also indicated that there is a significant relationship between product perception and attitude towards online shopping among the respondents (r = 0.471**, p 0.05). Further study should explore other factors that influencing consumers’ attitude towards e-commerce purchases through online shopping with a broader range of population and high representative sampling method.


title: Hardware accelerated convolutional neural networks for synthetic vision systems

abstract: In this paper we present a scalable hardware architecture to implement large-scale convolutional neural networks and state-of-the-art multi-layered artificial vision systems. This system is fully digital and is a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images. We present a performance comparison between a software, FPGA and ASIC implementation that shows a speed up in custom hardware implementations.


title: Searchable Encryption with Secure and Efficient Updates

abstract: Searchable (symmetric) encryption allows encryption while still enabling search for keywords. Its immediate application is cloud storage where a client outsources its files while the (cloud) service provider should search and selectively retrieve those. Searchable encryption is an active area of research and a number of schemes with different efficiency and security characteristics have been proposed in the literature. Any scheme for practical adoption should be efficient -- i.e. have sub-linear search time --, dynamic -- i.e. allow updates -- and semantically secure to the most possible extent. Unfortunately, efficient, dynamic searchable encryption schemes suffer from various drawbacks. Either they deteriorate from semantic security to the security of deterministic encryption under updates, they require to store information on the client and for deleted files and keywords or they have very large index sizes. All of this is a problem, since we can expect the majority of data to be later added or changed. Since these schemes are also less efficient than deterministic encryption, they are currently an unfavorable choice for encryption in the cloud. In this paper we present the first searchable encryption scheme whose updates leak no more information than the access pattern, that still has asymptotically optimal search time, linear, very small and asymptotically optimal index size and can be implemented without storage on the client (except the key). Our construction is based on the novel idea of learning the index for efficient access from the access pattern itself. Furthermore, we implement our system and show that it is highly efficient for cloud storage.


title: Content-Based Image Retrieval Using Multiple Features

abstract: Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also attempted during the implementation process, but the best result is generated from the algorithms that weights color and texture. 80% satisfying rate is achieved.


Automatically Quantifying Radiographic Knee Osteoarthritis Severity Final Report-CS 229-Machine Learning


title: A theoretical investigation of the optical properties of metal nanoparticles in water for photo thermal conversion enhancement

abstract: Abstract Nanofluids are used in photo thermal absorbers such as direct absorption solar collectors (DASC) to improve the absorption and heat transfer. A range of experimental studies have investigated the photo thermal conversion in nanofluids based on metal nanoparticles. The photo thermal conversion efficiency depends mainly on the ability of the metal nanoparticle based nanofluid to absorb light which in turn is governed by the particle localized surface plasmon resonance (LSPR). In this article we investigate the optical properties of metal nanoparticles in water, and based on this we judge their suitability for use in water based nanofluids. We simulate the optical properties and provide an overview of the absorption, scattering and extinction properties of solid and spherical aluminum, silver, copper and gold nanoparticles suspended in water. We study nanoparticle diameters between 20 and 140 nm for the wavelength range 300–1350 nm (200–1250 nm for aluminum). We compare our simulation results with the available experimental results presented in the literature, and find good agreement.


title: Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding

abstract: Topic modeling provides a valuable method for identifying the linguistic contexts that surround social institutions or policy domains. This article uses Latent Dirichlet Allocation (LDA) to analyze how one such policy domain, government assistance to artists and arts organizations, was framed in almost 8000 articles. These comprised all articles that referred to government support for the arts in the U.S. published in five U.S. newspapers between 1986 and 1997—a period during which such assistance, once noncontroversial, became a focus of contention. We illustrate the strengths of topic modeling as a means of analyzing large text corpora, discuss the proper choice of models and interpretation of model results, describe means of validating topic-model solutions, and demonstrate the use of topic models in combination with other statistical tools to estimate differences between newspapers in the prevalence of different frames. Throughout, we emphasize affinities between the topic-modeling approach and such central concepts in the study of culture as framing, polysemy, heteroglossia, and the relationality of meaning. # 2013 Elsevier B.V. All rights reserved.


title: Enhanced Dual-Band Ambient RF Energy Harvesting With Ultra-Wide Power Range

abstract: This letter presents a novel dual-band rectifier with extended power range (EPR) and an optimal incident RF power strategy in the settings where the available RF energy fluctuates considerably. It maintains high power conversion efficiency (PCE) in an ultra-wide input power range by adopting a pHEMT in the proposed topology. Simultaneous RF power incident mode is proposed and preferred to the traditional independent mode for multi-band harvesting. Measured results show that more than 30% PCE is obtained with input power ranging from $-15~{\rm dBm}$ to 20 dBm and peak PCE of 60% is maintained from 5 to 15 dBm. Positive power gain is achieved from $-20~{\rm dBm}$ to more than 10 dBm. Investigation about the effect of RF power incident ratio on dual-band harvesting's performance is presented and it provides a good reference for future multi-band harvesting system design.


title: Analysis of Types of Self-Improving Software

abstract: Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, and provide a review of the relevant literature. Finally, we address security implications from self-improving intelligent software.


title: Web Service Recommendation via Exploiting Location and QoS Information

abstract: Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real-world Web services to demonstrate the effectiveness of our approach.


title: ACCURATE OPTICAL TARGET POSE DETERMINATION FOR APPLICATIONS IN AERIAL PHOTOGRAMMETRY

abstract: We propose a new design for an optical coded target based on concentric circles and a position and orientation determination algorithm optimized for high distances compared to the target size. If two ellipses are fitted on the edge pixels corresponding to the outer and inner circles, quasi-analytical methods are known to obtain the coordinates of the projection of the circles center. We show the limits of these methods for quasi-frontal target orientations and in presence of noise and we propose an iterative refinement algorithm based on a geometric invariant. Next, we introduce a closed form, computationally inexpensive, solution to obtain the target position and orientation given the projected circle center and the parameters of the outer circle projection. The viability of the approach is demonstrated based on aerial pictures taken by an UAV from elevations between 10 to 100 m. We obtain a distance RMS below 0.25 % under 50 m and below 1 % under 100 m with a target size of 90 cm, part of which is a deterministic bias introduced by image exposure.


Optimal Data Architecture for an Telemedicine Analytic Platform


title: Downloaded from jsr.sagepub.com by guest on May 30, 2011Customer-to-Customer Interactions: Broadening the Scope of Word of Mouth

abstract: The increasing emphasis on understanding the antecedents and consequences of customer-to-customer (C2C) interactions is one of the essential developments of customer management in recent years. This interest is driven much by new online environments that enable customers to be connected in numerous new ways and also supply researchers’ access to rich C2C data. These developments present an opportunity and a challenge for firms and researchers who need to identify the aspects of C2C research on which to focus, as well as develop research methods that take advantage of these new data. The aim here is to take a broad view of C2C interactions and their effects and to highlight areas of significant research interest in this domain. The authors look at four main areas: the different dimensions of C2C interactions; social system issues related to individuals and to online communities; C2C context issues including product, channel, relational and market characteristics; and the identification, modeling, and assessment of business outcomes of C2C interactions.


title: Arc Flash Light Intensity Measurement System Design

abstract: Electrical arcs emit extremely high intensity light across a wide spectrum that can damage the eye's delicate structures, such as the cornea or the retina. Developing industry safety standards to mitigate light hazards is a critical component of arc flash safety. Accurate measurement of light intensity is the first step in understanding the level of eye protection needed. Traditionally, light measurement devices are an assembly of light-detecting resistors made from cadmium sulfide (CdS) or silicon (Si) cells. However, their response times are inadequate for capturing the peak light intensity or the dynamic changes during the initial arc flash. In addition, the brightness of an arc flash is greater than the measurable light levels from traditional light measurement devices. Furthermore, the light sensitivity of traditional sensors does not match the Commission Internationale de l'Eclairage luminosity function $V(\lambda)$ curve, which defines the human eye's sensitivity to bright light as a function of wavelength under typical ambient light conditions. This paper discusses a novel light measurement technique to overcome the limitations of traditional approaches. This design is capable of measuring the light intensity of the arc flash as perceived by the human eyes. These results can be used to evaluate the potential of an arc flash to harm the eyes.


A Mixed-Initiative System for Building Mixed-Initiative Systems


title: Combining Multiple Criteria and Multidimension for Movie Recommender System

abstract: Most of current Recommender Systems based on Content-Based Filtering, Collaborative Fil- tering, Demographic Filtering and Hybrid Filtering which are concentrated on user and item entities. Many research papers are improved by pointing out either Multiple Criteria Rating approach or Multidi- mensional approach for Recommender System. This paper proposes an advanced Recommender System to provide higher quality of recommendations by com- bining the Multiple Criteria rating and the Multidi- mensional approaches. For the Multiple Criteria ap- proach, this paper proposed a method that changes the way of weighting to be more suitable and also con- cern about the frequency of the selection movie fea- tures. To do Multidimensional approach, the Multi- ple Linear Regression is applied to analyze the contex- tual information of user characteristics. According to the experimental evaluation, the combining of Multi- ple Criteria Rating and Multidimensional approaches provide more accurate recommendation results than the current Hybrid Recommender Systems.


title: The Amaryllidaceae alkaloids: biosynthesis and methods for enzyme discovery

abstract: Amaryllidaceae alkaloids are an example of the vast diversity of secondary metabolites with great therapeutic promise. The identification of novel compounds in this group with over 300 known structures continues to be an area of active study. The recent identification of norbelladine 4′-O-methyltransferase (N4OMT), an Amaryllidaceae alkaloid biosynthetic enzyme, and the assembly of transcriptomes for Narcissus sp. aff. pseudonarcissus and Lycoris aurea highlight the potential for discovery of Amaryllidaceae alkaloid biosynthetic genes with new technologies. Recent technical advances of interest include those in enzymology, next generation sequencing, genetic modification, nuclear magnetic resonance spectroscopy, and mass spectrometry.


EEG Correlates of Ten Positive Emotions