title: IEEE 802.11ax: Next generation wireless local area networks

abstract: Recently, IEEE 802 started a task group to investigate and deliver next generation WLAN technologies for the scenarios of dense networks with a large number of stations and access point. The proposal is specified as the IEEE 802.11ax amendment. Due to the significant network capacity increase achieved by 802.11ax, the term high-efficiency WLAN (HEW) is also used in reference to this new amendment. This paper summarizes the IEEE 802.11ax standardization activities in progress and presents an overview of the most important features proposed in the 802.11ax amendment. Expected features and challenges for 802.11ax in the design of physical layer (PHY) and media access control sub-layer (MAC), toward a new era of wireless LANs, are also discussed.


Patient Awareness and Symptoms From an Incisional Hernia


Immunity Article Fate Mapping Reveals Origins and Dynamics of Monocytes and Tissue Macrophages


title: Agents and Devices: A Relative Definition of Agency

abstract: According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Humans tend to find the physical stance more helpful for certain systems, such as planets orbiting a star, and the intentional stance for others, such as living animals. We define a formal counterpart of physical and intentional stances within computational theory: a description of a system as either a device, or an agent, with the key difference being that `devices' are directly described in terms of an input-output mapping, while `agents' are described in terms of the function they optimise. Bayes' rule can then be applied to calculate the subjective probability of a system being a device or an agent, based only on its behaviour. We illustrate this using the trajectories of an object in a toy grid-world domain.


title: Trust in leadership: A multi-level review and integration

abstract: Leaders have been argued to play a key role in determining organizational effectiveness across all levels (e.g., individual, team, unit) that exist within organizations. A key component in a leader's ability to be effective within such environments is the degree to which subordinates and co-workers trust him/her. Therefore, it is not surprising that researchers and practitioners alike are interested in identifying the mechanisms through which trust in leadership can be developed as well as those factors which moderate this relationship [e.g., Gillespie, N. A., Mann, L. (2004). Transformational leadership and shared values: The building blocks of trust. Journal of Managerial Psychology, 19, 588–607; Kouzes and Posner, 1995; Roberts, K. H., O'Reilly, C. A. (1974). Failures in upward communication in organizations: Three possible culprits. Academy of Management Journal, 17, 205–215; Whitener, E. M. (1997). The impact of human resource activities on employee trust. Human Resource Management Review, 7, 389–404]. Despite this, research that has addressed the factors that foster trust in leaders and the outcomes of this trust has been disjointed and, as yet, no comprehensive model has been presented to systematically examine these factors. Therefore, the purpose of this article will be to present an integrative model of trust in leadership.


EVIDENCE BASED ( S 3 ) GUIDELINES FOR THE TREATMENT OF ANDROGENETIC ALOPECIA IN WOMEN AND IN MEN


title: Document classification by topic labeling

abstract: In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the Dirichlet distribution and then automatically assign a class label to each unlabeled document depending on its "closeness" to one of the aggregated topics. We present an extension to our algorithm based on the combination of Expectation-Maximization (EM) algorithm and a naive Bayes classifier. We show effectiveness of our algorithm on three real world datasets.


title: Contrastive Explanation: A Structural-Model Approach

abstract: The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making and action. While different sub-fields have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation in AI more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event -- the fact --- they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, "Why P rather than Q?". In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical AI problems: classification and planning. We believe that this model can be used to define contrastive explanation of other subfield-specific AI models.


title: Notice of RetractionA Novel Plasma Cutting Power Supply

abstract: This paper presents a method that the designed half-bridge inverter air plasma cutting machine power supply uses the non high frequency arc technology and analyzes the working principle of non high frequency arc, and gives detailed work cutting process timing diagram. The cutting machine power supply, it uses the controller-TMS320F2812 to control the entire power system, and the use of the chip UC3854 power factor correction circuit. When compared with the traditional cutting power supply, the designed plasma cutting power supply has a simple structure, low energy consumption, small electromagnetic interference and so on. In addition, the power system has been modeling and simulation, and built a prototype and verified.


title: Combining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction

abstract: The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.


title: Learning legged swimming gaits from experience

abstract: We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-the-art policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.


title: Developing a cost-effective OpenFlow testbed for small-scale Software Defined Networking

abstract: OpenFlow is the first standard interface for realizing Software-Defined Networking (SDN) that can decouple the data and control plane to provide scalable network management. To validate the performance and features of the OpenFlow standard, many researchers have utilized specialized hardware network devices such as NetFPGA. However, these devices are not suitable for implementing a small-scale SDN testbed due to high cost, complexity, and specialized programming languages. The well-known SDN emulator, Mininet[l], is also widely utilized but it is not enough to support network dynamicity and the performance of the virtualized hosts. In this paper, we suggest a more cost-effective alternative of implementing SDN testbed with Open vSwitch (OVS), based on the Raspberry-Pi that is a low-cost embedded Linux machine. We validate our testbed with the OpenFlow specification 1.0 and prove that its maximum network throughput shows almost the same performance compared to the NetFPGA-1G.


title: Ch-08.qxd

abstract: Implicit learning is nonepisodic learning of complex information in an incidental manner, without awareness of what has been learned. Implicit learning experiments use 3 different stimulus structures (visual, sequence, and function) and 3 different dependent measures or response modalities (conceptual fluency, efficiency, and prediction and control). Implicit learning may require a certain minimal amount of attention and may depend on attentional and working memory mechanisms. The result of implicit learning is implicit knowledge in the form of abstract (but possibly instantiated) representations rather than verbatim or aggregate representations. Implicit learning shows biases and dissociations in learning different stimulus structures. The dependence of implicit learning on particular brain areas is discussed, some conclusions are drawn for modeling implicit learning, and the interaction of implicit and explicit learning is considered.


title: Discrete-event simulation for green supply chain management: A conceptual framework

abstract: Simulation of the green supply chain is inundated with complexities due to reverse flows, reverse logistics, product recovery, reuse, recycling, remanufacturing, and other green initiatives. The focus of this research is to develop a conceptual framework which forms a platform for building simulation models from a discrete-event simulation perspective. First, typical green supply chain players are identified and their interaction analyzed. Second, performance indicators specific to the green supply chain are identified. Third, conceptual models for each of the identified supply chain players are developed, from a discrete-event simulation perspective. The framework suggested provides a useful basis or platform for developing simulation models for the green supply chain.


title: Refinement and selection heuristics in subgroup discovery and classification rule learning

abstract: New double beam algorithms for subgroup discovery (SD) and classification rules (RL).Algorithms can use different heuristics for rule refinement and rule selection.Variants of new SD algorithm give more interesting rules than state-of-the-art.RL algorithm gives rules with comparable accuracy with state-of-the-art algorithms.Inverted heuristics in rule refinement produce rules with better coverage. Classification rules and rules describing interesting subgroups are important components of descriptive machine learning. Rule learning algorithms typically proceed in two phases: rule refinement selects conditions for specializing the rule, and rule selection selects the final rule among several rule candidates. While most conventional algorithms use the same heuristic for guiding both phases, recent research indicates that the use of two separate heuristics is conceptually better justified, improves the coverage of positive examples, and may result in better classification accuracy. The paper presents and evaluates two new beam search rule learning algorithms: DoubleBeam-SD for subgroup discovery and DoubleBeam-RL for classification rule learning. The algorithms use two separate beams and can combine various heuristics for rule refinement and rule selection, which widens the search space and allows for finding rules with improved quality. In the classification rule learning setting, the experimental results confirm previously shown benefits of using two separate heuristics for rule refinement and rule selection. In subgroup discovery, DoubleBeam-SD algorithm variants outperform several state-of-the-art related algorithms.


title: BIT: A Tool for Instrumenting Java Bytecodes

abstract: BIT (Bytecode Instrumenting Tool) is a collection of Java classes that allow one to build customized tools to instrument Java Virtual Machine (JVM) bytecodes. Because understanding program behavior is an essential part of developing effective optimization algorithms, researchers and software developers have built numerous tools that carry out program analysis. Although there are existing tools that analyze and modify executables on a variety of operating systems and machine architectures, there currently is no framework for carrying out the same task for JVM bytecodes. In this paper, we describe BIT, which allows the user to insert calls to analysis methods anywhere in the bytecode, so that information can be extracted from the user program while it is being executed. In this paper, we describe several simple tools built using BIT and also report on BIT's performance. We found that the overhead for the execution speed and size were between 23% to 150%.


title: AN improved method of optimization for electrical machines

abstract: Methods of optimizing the design of electrical machines are discussed. The augmented Lagrangian multiplier method is described, and an algorithm for this method is introduced. The method is compared with the more familiar exterior penalty function method using, as an example, the cost optimization of permanent magnetic variable-speed drive motors. The improved method is shown to be superior, both in its ability to reach an optimum consistently and in the number of required iterations. >


title: A Conceptual Framework of Online Natural Language Processing Pipeline Application

abstract: This paper describes a conceptual framework that enables online NLP pipelined applications to solve various interoperability issues and data exchange problems between tools and platforms; e.g., tokenizers and part-of-speech taggers from GATE, UIMA, or other platforms. We propose a restful wrapping solution, which allows for universal resource identification for data management, a unified interface for data exchange, and a light-weight serialization for data visualization. In addition, we propose a semantic mapping-based pipeline composition, which allows experts to interactively exchange data between heterogeneous components.


title: Topology Preserving Domain Adaptation for Addressing Subject Based Variability in SEMG Signal

abstract: A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to determine the fatigue state of a muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects cause variations in both marginal and conditional probability distributions in the features extracted across subjects, making it difficult to model the signal for any automated signal classification. However we observe that the manifold of the multidimensional SEMG data have an inherent similarity as the physiological state moves from no fatigue to fatigue state. In this paper we exploit this specific feature of the SEMG data and propose a domain adaptation technique that is based on intrinsic manifold of the data preserved in a low dimensional space, thus reducing the marginal probability differences between the subjects, followed by an instance selection methodology, based on similar conditional probabilities in the mapped domain. The proposed method provides significant improvement in subject independent accuracies compared to cases without any domain adaptation methods and also compared to other state-of-the-art domain adaptation methodologies.


title: Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection

abstract: Coronary angiography is considered to be a safe tool for the evaluation of coronary artery disease and perform in approximately 12 million patients each year worldwide. [1] In most cases, angiograms are manually analyzed by a cardiologist. Actually, there are no clinical practice algorithms which could improve and automate this work. Neural networks show high efficiency in tasks of image analysis and they can be used for the analysis of angiograms and facilitate diagnostics. We have developed an algorithm based on Convolutional Neural Network and Neural Network U-Net [2] for vessels segmentation and defects detection such as stenosis. For our research we used anonymized angiography data obtained from one of the city's hospitals and augmented them to improve learning efficiency. U-Net usage provided high quality segmentation and the combination of our algorithm with an ensemble of classifiers shows a good accuracy in the task of ischemia evaluation on test data. Subsequently, this approach can be served as a basis for the creation of an analytical system that could speed up the diagnosis of cardiovascular diseases and greatly facilitate the work of a specialist.


title: KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation

abstract: In this paper, we describe a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in eCommerce advertising, Kelkoo\footnote{\url{https://www.kelkoo.com/}}, during one month. This dataset gathers implicit feedback, in form of clicks, of users that have interacted with over 56 million offers displayed by Kelkoo, along with a rich set of contextual features regarding both customers and offers. In conjunction with a detailed description of the dataset, we show the performance of six state-of-the-art recommender models and raise some questions on how to encompass the existing contextual information in the system.


title: Final-year student nurses' perceptions of role transition

abstract: Role transition can be both challenging and exciting. This study presents the findings of phase one of a two-part study conducted by Deasy et al (2011), which explored final-year student nurses' (n=116) perceptions and expectations of role transition. The students were registered on four-year BSc nursing programmes at an Irish university. Data was analyzed using SPSS (version 16). A response rate of 84% was achieved. Over half of respondents said they were adequately prepared for the post of registered nurse. Respondents generally perceived themselves to be competent across a range of domains: managing workloads; prioritizing care delivery; interpersonal skills; time management skills; ethical decision making; and providing health information and education. In contrast, not all were confident about their knowledge and many expected the transition to be problematic. Most expected to be supported and to receive constructive feedback. Recommendations include nurturing supportive work environments to reduce stress and increase confidence.


Artificial Bee Colony ( ABC ) Algorithm Exploitation and Exploration Balance Milan TUBA


title: Image inpainting with a learned guidance vector field

abstract: Image inpainting is one of the challenging problems in image restoration. To recover the missing region, we can only rely on the information in the uncorrupted region of the input image and some prior knowledge. The latter can be learned from suitable training data or implemented through some smoothness constraints. In this paper, a new approach for image inpainting is proposed. Here, we iteratively learn a guidance vector field from training data and recover the missing region by solving the Poisson equation using the learned guidance vector field with Dirichlet boundary conditions. In addition, we also propose a method to select the best training set by using the correlation between neighboring patches of the damaged input image and training images. The experimental results on face images show that the new approach yields smooth and visually pleasing results.


title: High-Gain Directional Antenna for WLAN and WiMAX Applications

abstract: This letter presents a dual-band slot antenna with high gain for wireless local area network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) communication covering the IEEE802.11a/b and IEEE802.16d/e standards. The antenna consists of a microstrip-fed slot with a complimentary stub on a single dielectric medium. A reflecting ground plane is employed to achieve a unidirectional radiation pattern with an average gain of 9.1 dBi. The feed of the antenna is optimized to achieve radiation efficiency above 95% in the WLAN and WiMAX frequency bands. The radiation properties of a prototype antenna were measured in a compact antenna range. Simulated and measured results (impedance bandwidth, gain, and radiation patterns) are presented for the proposed antenna.


title: Hand Gesture Segmentation

abstract: In today’s world, the impact of modern computer system increases on everyday life. So Human Computer Interaction (HCI) has become more and more important in our daily lives. Also as now a day, the computing, communication and display technologies progress, the existing HCI techniques like mice, keyboards, limit the speed and naturalness of our interaction. Use of hand gesture can be used to provide natural interaction. Hand image segmentation is the most important step in every hand gesture recognition system. All subsequent steps heavily rely on the quality of the segmentation. The data which is lost due to improper segmentation can be hardly recovered in later phases of image understanding process. In this paper we present one algorithm to detect hand on plain, uniform background and another algorithm for complex static background.


title: Incorporating prior knowledge in medical image segmentation: a survey

abstract: Medical image segmentation, the task of partitioning an image into meaningful parts, is an important step toward automating medical image analysis and is at the crux of a variety of medical imaging applications, such as computer aided diagnosis, therapy planning and delivery, and computer aided interventions. However, the existence of noise, low contrast and objects' complexity in medical images are critical obstacles that stand in the way of achieving an ideal segmentation system. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. This paper surveys the different types of prior knowledge that have been utilized in different segmentation frameworks. We focus our survey on optimization-based methods that incorporate prior information into their frameworks. We review and compare these methods in terms of the types of prior employed, the domain of formulation (continuous vs. discrete), and the optimization techniques (global vs. local). We also created an interactive online database of existing works and categorized them based on the type of prior knowledge they use. Our website is interactive so that researchers can contribute to keep the database up to date. We conclude the survey by discussing different aspects of designing an energy functional for image segmentation, open problems, and future perspectives.


title: Double-Stator Air-Core Tubular Permanent Magnet Linear Motor for Vehicle Active Suspension Systems

abstract: This paper provides a direct-drive electromagnetic active suspension system which consists of a double- stator air-core tubular permanent magnet linear motor (DATPLM) in parallel with a coil spring. The topology of magnetic pole can be radial-magnetized, axial- magnetized and Halbach-magnetized. The proposed DATPLM can operate in both motor mode and generator mode which allows for bidirectional energy flow. Therefore, the suspension system can simultaneously eliminate the road disturbances and apply active roll and pitch control. Compared with the available slotted topologies, the proposed quasi-Halbach magnetized topology has excellent dynamic response, servo characteristics and very low force ripple.


title: Multi-entity models of resource description in the Semantic Web

abstract: Purpose – The 1998 International Federation of Library Associations (IFLA) document “Functional Requirements for Bibliographic Records” (FRBR) has inspired a family of models that view bibliographic resources in terms of multiple entities differentiated with regard to meaning, expression, and physicality. The purpose of this paper is to compare how three FRBR and FRBR-like models have been expressed as Semantic Web vocabularies based on Resource Description Framework (RDF). The paper focusses on IFLA’s own vocabulary for FRBR; RDF vocabularies for Resource Description and Access (RDA), an emergent FRBR-based standard for library cataloging; and BIBFRAME, an emergent FRBR-like, native-RDF standard for bibliographic data. Design/methodology/approach – Simple test records using the RDF vocabularies were analyzed using software that supports inferencing. Findings – In some cases, what the data actually means appears to differ from what the vocabulary developers presumably intended to mean. Data based on the F...


title: Pharmacist influence on economic and morbidity outcomes in a tertiary care teaching hospital

abstract: The influence of pharmacist participation on economic and morbidity outcomes at a tertiary care teaching hospital was studied. Patients admitted to internal medicine wards during a nine-month period were assigned to either a treatment team or a control team. Each team consisted of an attending physician, senior and junior medical residents, and medical students; the treatment team included a pharmacist who reviewed all patient charts, made rounds with the team, and recommended modifications of drug therapy. Pharmacy interaction with the control team was limited to contacting physicians about potentially dangerous orders, answering questions from the medical team, and handling orders for items not on the formulary or otherwise unavailable. After discharge, data from patient records were analyzed for pharmacy costs and total hospital costs and length of stay (as markers of the pharmacist's effect on economics and morbidity, respectively). Analysis of baseline characteristics showed that the two groups of patients were statistically comparable. Treatment team patients who were included in the data analysis (414) had significantly shorter stays (by a mean of 1.3 days) and lower pharmacy and total hospital costs (by a mean of $301 and $1654, respectively) than those included in the control team analysis (453). The direct participation of a pharmacist on a patient care team significantly decreased pharmacy and hospital costs, as well as length of stay, compared with minimal participation of a pharmacist.


title: Characterizing Political Fake News in Twitter by its Meta-Data

abstract: This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users.


title: A Methodology for Privacy-Aware IoT-Forensics

abstract: The Internet of Things (IoT) brings new challenges to digital forensics. Given the number and heterogeneity of devices in such scenarios, it bring extremely difficult to carry out investigations without the cooperation of individuals. Even if they are not directly involved in the offense, their devices can yield digital evidence that might provide useful clarification in an investigation. However, when providing such evidence they may leak sensitive personal information. This paper proposes PRoFIT; a new model for IoT-forensics that takes privacy into consideration by incorporating the requirements of ISO/IEC 29100:2011 throughout the investigation life cycle. PRoFIT is intended to lay the groundwork for the voluntary cooperation of individuals in cyber crime investigations.


title: Integrated Farming System and its Effect on farm Cash Income in Awka South Agricultural Zone of Anambra State, Nigeria

abstract: This study reviewed types of integrated farming system (IFS), profitability of IFS and its impact on farm cash income in Awka agricultural zone of Anambra State, Nigeria. Structured questionnaires were used to obtain information from 84 respondents selected by random sampling technique. Data obtained were analyzed by means of gross margin and net farm income, profit function and multiple regression methods. The highest net farm income of N1,156,730.00 or $7,462.77 was recorded by crop-livestock-fish partial integration which is closest to the full integration of crop-livestock-fish-processing-biogas. Output price parameter accounted for more of the maximum variable profit in profit function analysis. Farm cash income was significantly influenced by level of farmer's education, years of experience, type of integration and cost of farm inputs. Farm cash income could be increased through the provision of subsides for inputs to reduce cost of production and enlightenment campaigns to improve farmer's knowledge and technical skills.


title: Using grid for accelerating density-based clustering

abstract: Clustering analysis is a primary method for data mining. The ever increasing volumes of data in different applications forces clustering algorithms to cope with it. DBSCAN is a well-known algorithm for density-based clustering. It is both effective so it can detect arbitrary shaped clusters of dense regions and efficient especially in existence of spatial indexes to perform the neighborhood queries efficiently. In this paper we introduce a new algorithm GriDBSCAN to enhance the performance of DBSCAN using grid partitioning and merging, yielding a high performance with the advantage of high degree of parallelism. We verified the correctness of the algorithm theoretically and experimentally, studied the performance theoretically and using experiments on both real and synthetic data. It proved to run much faster than original DBSCAN. We compared the algorithm with a similar algorithm, EnhancedDBSCAN, which is also an enhancement to DBSCAN using partitioning. Experiments showed the new algorithm's superiority in performance and degree of parallelism.


title: WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

abstract: In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically selecting relevant image regions from weak annotations, e.g. global image labels, and encompasses the following contributions. Firstly, WELDON leverages recent improvements on the Multiple Instance Learning paradigm, i.e. negative evidence scoring and top instance selection. Secondly, the deep CNN is trained to optimize Average Precision, and fine-tuned on the target dataset with efficient computations due to convolutional feature sharing. A thorough experimental validation shows that WELDON outperforms state-of-the-art results on six different datasets.


INTEGRATING MOBILE IT/CLOUD INTO ENTERPRISE ARCHITECTURE: A COMPARATIVE ANALYSIS


title: A comparative study of HTM and other neural network models for online sequence learning with streaming data

abstract: Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms. HTM and long-short term memory (LSTM) give the best prediction accuracy. HTM additionally demonstrates many other features that are desirable for real-world sequence learning, such as fast adaptation to changes in the data stream, robustness to sensor noise and fault tolerance. These features make HTM an ideal candidate for online sequence learning problems.


title: A deliberate practice account of typing proficiency in everyday typists.

abstract: The concept of deliberate practice was introduced to explain exceptional performance in domains such as music and chess. We apply deliberate practice theory to intermediate-level performance in typing, an activity that many people pursue on a regular basis. Sixty university students with several years typing experience participated in laboratory sessions that involved the assessment of abilities, a semistructured interview on typing experience as well as various typing tasks. In line with traditional theories of skill acquisition, experience (amount of typing since introduction to the keyboard) was related to typing performance. A perceptual speed test (digit-symbol substitution) and a measure of motor abilities (tapping) were not significantly related to performance. In line with deliberate practice theory, the highest level of performance was reported among participants who had attended a typing class in the past and who reported to adopt the goal of typing quickly during everyday typing. Findings suggest that even after several years of experience engagement in an everyday activity can serve as an opportunity for further skill improvement if individuals are willing to push themselves.


title: Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves

abstract: We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low-rank, low-frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques--Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross-validation--to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.


title: Does computational thinking correlate with personality?: the non-cognitive side of computational thinking

abstract: Computational thinking (CT) is being considered as a key set of problem-solving skills to be acquired by the new generations of digital citizens and workers in order to thrive in a computer-based world. However, from a psychometric point of view, CT is still a poorly defined psychological construct: there is no full consensus on a formal definition of CT or how to measure it; and its correlations with other psychological constructs, whether cognitive or non-cognitive, have not been completely established. In response to the latter, this paper aims to study specifically the correlations between CT and the several dimensions from the 'Big Five' model of human personality: Conscientiousness, Openness to Experience, Extraversion, Agreeableness, and Neuroticism. To do so, the Computational Thinking Test (CTt) and the Big Five Questionnaire-Children version (BFQ-C) are administered on a sample (n = 99) of Spanish students from 5th to 10th grade. Results show statistically significant correlations between CT and: Openness to Experience (r = 0.41), Extraversion (r = 0.30), and Conscientiousness (r = 0.27). These results are partially consistent with the literature about the links between cognitive and personality variables, and corroborate the existence of a non-cognitive side of CT. Hence, educational interventions aimed at fostering CT should take into account these non-cognitive issues in order to be comprehensive and successful.


title: Cluster, grid and cloud computing: A detailed comparison

abstract: Cloud computing is rapidly growing as an alternative to conventional computing. However, it is based on models like cluster computing, distributed computing, utility computing and grid computing in general. This paper presents an end-to-end comparison between Cluster Computing, Grid Computing and Cloud Computing, along with the challenges they face. This could help in better understanding these models and to know how they differ from its related concepts, all in one go. It also discusses the ongoing projects and different applications that use these computing models as a platform for execution. An insight into some of the tools which can be used in the three computing models to design and develop applications is given. This could help in bringing out the innovative ideas in the field and can be explored to the needs in the computing world.


title: Mid-term results of large diameter heads on cross-linked polyethylene liners in total hip replacement.

abstract: BACKGROUND ::: Highly cross-linked polyethylene liners in total hip replacement (THR) have allowed the use of larger diameter femoral heads. Larger heads allow for increased range of motion, decreased implant impingement, and protection against dislocation. The purpose of this study is to assess the clinical and radiographic outcomes of patients with large femoral heads THR at 4 years postop. ::: ::: ::: MATERIALS AND METHODS ::: Study includes 28 patients who had a primary THR with a 36 mm larger femoral head were retrospectively for minimum 4 years follow-up. All patients received a cementless acetabular shell and a highly cross-linked polyethylene liner with an inner diameter of 36 mm. The median radiographic follow-up was 4 years (range 2.0-6.0), and patients were assessed clinically by Harris hip score. ::: ::: ::: RESULTS ::: The mean follow-up is minimum 4 years (range 2-6 years) results in all operated patients showed marked improvement in Harris hip score from preoperative mean 49.1 to 89.9 at 4 years or more follow-up. The complications include superficial infection (n = 2). No dislocation, or no osteolysis was seen in the pelvis or proximal femur, and no components failed due to aseptic loosening. There was no evidence of cup migration, screw breakage, or eccentric wear on the liner. ::: ::: ::: CONCLUSION ::: The mid-term results in this series of patients with LDH using 36 mm femoral head articulating with highly cross linked polyethylene showed excellent clinical, and radiological results, in terms of, joint restoration that replicates the natural anatomy, optimized range of motion without impingement & reduced opportunity for postoperative dislocation.


A morphological image preprocessing suite for ocr on natural scene images


title: A generalized uncertainty principle and sparse representation in pairs of bases

abstract: An elementary proof of a basic uncertainty principle concerning pairs of representations of R/sup N/ vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete dictionaries. The main contribution in this paper is the improvement of an important result due to Donoho and Huo (2001) concerning the replacement of the l/sub 0/ optimization problem by a linear programming (LP) minimization when searching for the unique sparse representation.


title: Face photo retrieval by sketch example

abstract: Face photo-sketch matching has received great attention in recent years due to its vital role in law enforcement. The major challenge of matching face photo and sketch is difference of visual characteristics between face photo and sketch which is referred as modality gap. Earlier approaches have reduced the modality gap by synthesizing face photos and sketches in a same modality (photo or sketch). However, the effectiveness of these approaches is highly affected by synthesis results. That means a poor synthesis might degrade the performance of matching. Therefore, recent works have focused to directly match face photo and sketch of different modalities. However, the features used by these approaches are not robust against modality gap. In this paper, a modality-invariant face descriptor called Gabor Shape is proposed to retrieve face photos based on a probe sketch. Experiments on CUFS and CUFSF datasets show that the new descriptor outperforms the state-of-the-art approaches.


title: Exploring strategies for training deep neural networks

abstract: Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions. Hinton et al. recently proposed a greedy layer-wise unsupervised learning procedure relying on the training algorithm of restricted Boltzmann machines (RBM) to initialize the parameters of a deep belief network (DBN), a generative model with many layers of hidden causal variables. This was followed by the proposal of another greedy layer-wise procedure, relying on the usage of autoassociator networks. In the context of the above optimization problem, we study these algorithms empirically to better understand their success. Our experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy helps the optimization by initializing weights in a region near a good local minimum, but also implicitly acts as a sort of regularization that brings better generalization and encourages internal distributed representations that are high-level abstractions of the input. We also present a series of experiments aimed at evaluating the link between the performance of deep neural networks and practical aspects of their topology, for example, demonstrating cases where the addition of more depth helps. Finally, we empirically explore simple variants of these training algorithms, such as the use of different RBM input unit distributions, a simple way of combining gradient estimators to improve performance, as well as on-line versions of those algorithms.


title: Financial Aspect-Based Sentiment Analysis using Deep Representations

abstract: The topic of aspect-based sentiment analysis (ABSA) has been explored for a variety of industries, but it still remains much unexplored in finance. The recent release of data for an open challenge (FiQA) from the companion proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA contains high quality labels, but it still lacks data quantity to apply traditional ABSA deep learning architecture. In this paper, we employ high-level semantic representations and methods of inductive transfer learning for NLP. We experiment with extensions of recently developed domain adaptation methods and target task fine-tuning that significantly improve performance on a small dataset. Our results show an 8.7% improvement in the F1 score for classification and an 11% improvement over the MSE for regression on current state-of-the-art results.


title: Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System

abstract: Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.


Insulin therapy in the elderly: becoming the norm


title: A Fully Micromachined W-Band Coplanar Waveguide to Rectangular Waveguide Transition

abstract: A novel fully micromachined coplanar waveguide (CPW) to rectangular waveguide transition is presented in this paper. A metalized probe is adopted to couple the signal from the CPW to the rectangular waveguide through an aperture located at the center of the bottom broad wall of the rectangular waveguide. The transition was optimized using HFSS 10 over the whole W-band. In the proposed transition, the CPW and the rectangular waveguide are patterned and integrated on the same side of the substrate, while the coupling probe is patterned on the substrate instead of being fabricated separately. These changes make the transition more suitable for RF packaged circuits and easily extendable to THz applications. The measured average insertion loss of the back-to-back structure is 2.25 dB and the return loss is better than 11 dB over the whole W-band.


title: Experimental approach of FMCW signal generation using Direct Digital Synthesizer module

abstract: In this paper, the process of generating a signal frequency modulated continuous wave (FMCW) using direct digital synthesizer (DDS) will be presented starting from the design process simulation using the software on the computer until the realization and measurement of the generation of such signals on a module DDS, the kits from Analog Devices. Generation conducted a baseband signal with a bandwidth of 50 MHz in the frequency range of 50–100 MHz, the output power generated from −3.90 up to −7.80 dBm. FMCW signal is raised using Chirp waveform with the reference clock of 30 MHz and can be applied as a signal generator on the system Radar.


title: Analogy-driven 3D style transfer

abstract: Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to algorithmically distinguish style from structure, a high-level, potentially ill-posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies. In IQ testing, shape analogy queries present the subject with three shapes: source, target and exemplar, and ask them to select an output such that the transformation, or analogy, from the exemplar to the output is similar to that from the source to the target. The logical process involved in identifying the source-to-target analogies implicitly detects the structural differences between the source and target and can be used effectively to facilitate style transfer. Since the exemplar has a similar structure to the source, applying the analogy to the exemplar will provide the output we seek. The main technical challenge we address is to compute the source to target analogies, consistent with human logic. We observe that the typical analogies we look for consist of a small set of simple transformations, which when applied to the exemplar generate a continuous, seamless output model. To assemble a shape analogy, we compute an optimal set of source-to-target transformations, such that the assembled analogy best fits these criteria. The assembled analogy is then applied to the exemplar shape to produce the desired output model. We use the proposed framework to seamlessly transfer a variety of style properties between 2D and 3D objects and demonstrate significant improvements over the state of the art in style transfer. We further show that our framework can be used to successfully complete partial scans with the help of a user provided structural template, coherently propagating scan style across the completed surfaces.


title: Combining and measuring the benefits of bimanual pen and direct-touch interaction on horizontal interfaces

abstract: Many research projects have demonstrated the benefits of bimanual interaction for a variety of tasks. When choosing bimanual input, system designers must select the input device that each hand will control. In this paper, we argue for the use of pen and touch two-handed input, and describe an experiment in which users were faster and committed fewer errors using pen and touch input in comparison to using either touch and touch or pen and pen input while performing a representative bimanual task. We present design principles and an application in which we applied our design rationale toward the creation of a learnable set of bimanual, pen and touch input commands.


title: Modeling User Rating Profiles For Collaborative Filtering

abstract: In this paper we present a generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP). The generative process which underlies URP is designed to produce complete user rating profiles, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model [7], and LDA [1], but has clear advantages over each.


title: Tangible bits: towards seamless interfaces between people, bits and atoms

abstract: This paper presents our vision of Human Computer Interaction (HCI): "Tangible Bits." Tangible Bits allows users to "grasp & manipulate" bits in the center of users’ attention by coupling the bits with everyday physical objects and architectural surfaces. Tangible Bits also enables users to be aware of background bits at the periphery of human perception using ambient display media such as light, sound, airflow, and water movement in an augmented space. The goal of Tangible Bits is to bridge the gaps between both cyberspace and the physical environment, as well as the foreground and background of human activities. This paper describes three key concepts of Tangible Bits: interactive surfaces; the coupling of bits with graspable physical objects; and ambient media for background awareness. We illustrate these concepts with three prototype systems ‐ the metaDESK, transBOARD and ambientROOM ‐ to identify underlying research issues.


title: Attribute Reduction in Decision-Theoretic Rough Set Models

abstract: Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decision-monotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure @c in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the @c measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction.


title: Conditions of bifidobacterial colonization in preterm infants: a prospective analysis.

abstract: BACKGROUND ::: Premature birth results in a delayed and abnormal qualitative pattern of gut colonization. This abnormal pattern is thought to affect intestinal development and contribute to a higher risk of gastrointestinal infectious diseases such as neonatal necrotizing enterocolitis (NEC). In particular, bifidobacteria are thought to play a major role. We therefore studied bifidobacterial colonization in preterm infants during the first month of life. ::: ::: ::: PATIENTS AND METHODS ::: Fecal samples were prospectively analyzed in 52 infants born at a gestational age ranging from 30 to 35 weeks fed with a preterm formula alone and, in 18, with their mother's milk. Fecal samples were collected twice per week during the hospital stay. Bifidobacterial colonization was analyzed with culture and a molecular method. ::: ::: ::: RESULTS ::: Bifidobacterial colonization occurred in 18 infants at a median age of 11 days, always greater than the corrected mean gestational age of 35.4 weeks (SD, 0.9) and greater than 34 weeks for 16 of 18. Colonization by bifidobacteria was affected by neither birthweight nor mode of delivery nor antibiotics given to the mother or infant. In contrast, birth gestational age had a significant impact on colonization by bifidobacteria (P < 0.05), which always occurred in children born at a birth gestational age greater than 32.9 weeks (P < 0.05). ::: ::: ::: CONCLUSIONS ::: Birth gestational age seems to act as a major determinant of bifidobacterial colonization in the premature infant, suggesting the role of gut maturation, a finding that should probably be taken into account in manipulations of the gut flora aimed at reducing NEC.


title: Toward a theory of in-network computation in wireless sensor networks

abstract: Sensor networks are not just data networks with sensors being the sources of data. Rather, they are often developed and deployed for a specific application, and the entire network operation is accordingly geared toward satisfying this application. For overall system efficiency, it may be necessary for nodes to perform computations on data, as opposed to simply originating or forwarding data. Thus, the entire network can be viewed as performing an application-specific distributed computation. The topic of this article is to survey some lines of research that may be useful in developing a theory of in-network computation, which aims to elucidate how a wireless sensor network should efficiently perform such distributed computation. We review several existing approaches to computation problems in network settings, with a particular emphasis on the communication aspect of computation. We begin by studying the basic two-party communication complexity model and how to optimally compute functions of distributed inputs in this setting. We proceed to larger multihop networks, and study how block computation and function structure can be exploited to provide greater computational throughput. We then consider distributed computation problems in networks subject to noise. Finally, we review some randomized gossip-based approaches to computing aggregate functions in networks. These are diverse approaches spanning many different research communities, but together may find a role in the development of a more substantial theoretical foundation for sensor networks.


title: Protein hydrolysates and ultrafiltered < 1 KDa fractions from Phaseolus lunatus, Phaseolus vulgaris and Mucuna pruriens exhibit antihyperglycemic activity, intestinal glucose absorption and α-glucosidase inhibition with no acute toxicity in rodents.

abstract: BACKGROUND ::: Protein hydrolysates from food plants, such as legumes, have emerged as a new alternative to treat hyperglycemia, an important risk factor contributing to the development of type 2 diabetes mellitus (T2DM) and its complications. The aim of this work was to assess the antihyperglycemic activity and inhibition of α-glucosidase, and intestinal glucose absorption, and acute toxicity of total hydrolysates and < 1 kDa fractions from Phaseolus lunatus L., Phaseolus vulgaris L., and Mucuna pruriens (L.) DC., obtained by hydrolysis with Alcalase®-Flavourzyme® or pepsine-pancreatin enzymatic systems. ::: ::: ::: RESULTS ::: In vivo results showed that three of six total hydrolysates and four of six < 1 kDa fractions suppressed starch-induced postprandial hyperglycemia (ED50 range between 1.4 and 93 mg kg-1 ). In vitro, total hydrolysates and fractions, particularly from M. pruriens, inhibited carbohydrate intestinal absorption (from 19.2 to 40%), and α-glucosidase activity (IC50 from 0.86 to 75 mg mL-1 ). Finally, none of the hydrolysates and fractions tested did not show any signs of toxicity (LD50 > 5000 mg kg-1 ). ::: ::: ::: CONCLUSION ::: These results suggest that hydrolysates and < 1 kDa fractions from P. lunatus, P. vulgaris and M. pruriens are suitable candidates to treat or prevent T2DM. © 2018 Society of Chemical Industry.


title: Using NLP on news headlines to predict index trends

abstract: This paper attempts to provide a state of the art in trend prediction using news headlines. We present the research done on predicting DJIA trends using Natural Language Processing. We will explain the different algorithms we have used as well as the various embedding techniques attempted. We rely on statistical and deep learning models in order to extract information from the corpuses.


title: CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving

abstract: The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD). The proposed detector inherits the architecture of SSD and introduces a novel Comprehensive Feature Enhancement(CFE) module into it. Experimental results on this competition dataset as well as the MSCOCO dataset demonstrate that the proposed detector (named CFENet) performs much better than the original SSD and the state-of-the-art method RefineDet especially for small objects, while keeping high efficiency close to the original SSD. Specifically, the single scale version of the proposed detector can run at the speed of 21 fps, while the multi-scale version with larger input size achieves the mAP 29.69, ranking second on the leaderboard


title: Baidu Translate: Research and Products

abstract: In this presentation, I would like to introduce the research and products of machine translation in Baidu. As the biggest Chinese search engine, Baidu has released its machine translation system in June, 2011. It now supports translations among 27 languages on multiple platforms, including PC, mobile devices, etc. Hybrid translation approach is important for building an Internet translation system. As we know, the translation demands on the Internet come from various domains, including news wires, patents, poems, idioms, etc. It is difficult for a single translation system to achieve high accuracy on all domains. Therefore, hybrid translation is practically needed. Generally, we build a statistical machine translation (SMT) system, using the training corpora automatically crawled from the web. For the translation of idioms (e.g. “有 志者,事竟成,where there is a will, there is a way”), hot words/expressions (e.g. “一带一路, One Belt and One Road ”), example-based translation methods are used. To improve the translation of date (e.g. “2012年7月6日, July 6, 2012”), numbers (e.g. “三千五百万, thirty-five million), etc, rule-based methods are used as pre-process. To improve translation quality for the resourcepoor language pairs, we used pivot-based methods. Wu and Wang (2007) proposed the triangulation method that combines the source-pivot and the pivot-target phrase tables to induce a sourcetarget phrase table. To fill up the data gap between the source-pivot and pivot-target corpora, Wu and Wang (2009) employed a hybrid method combining RBMT and SMT systems. We also proposed a method to use a Markov random walk to discover implicit relations between phrases in the source and target languages (Zhu et al., 2013), thus to improve the coverage of phrase pairs. We utilized the co-occurrence frequency of source-target phrase pairs to estimate phrase translation probabilities (Zhu et al., 2014). On May 20th this year, we have launched a neural machine translation (NMT) system for Chinese-English translation. The system conducts end-to-end translation with a source language encoder and a target language decoder. Both the encoder and decoder are recurrent neural networks. The strength of NMT lies in that it can learn semantic and structural translation information by taking global contexts into account. We further integrated the SMT and NMT system to improve translation quality. We also released off-line translation packs for NMT system on mobile devices, providing translation services in case that the Internet is unavailable. So far as we know, this is the first NMT system supporting off-line translation on mobile devices. We also investigate the problem of learning a machine translation model that can simultaneously translate sentences from one source language to multiple target languages (Dong et al., 2015). Our solution is inspired by the recently proposed neural machine translation model which generalizes machine translation as a sequence learning problem. We train a unified neural machine translation model under the multi-task learning framework where the encoder is shared across different language pairs and each target language has a separate decoder. This model gets faster and better convergence for both resource-rich and resourcepoor language pairs under the multi-task learning framework. Based on the above techniques, we have released translation products for multiple platforms, including web translation on PC, APP on mobile devices, as well as free API for the thirdparty developers. Our system now support translations among 27 languages, not only including many frequently-used foreign languages, but also


title: Ecology-driven stereotypes override race stereotypes

abstract: Why do race stereotypes take the forms they do? Life history theory posits that features of the ecology shape individuals’ behavior. Harsh and unpredictable (“desperate”) ecologies induce fast strategy behaviors such as impulsivity, whereas resource-sufficient and predictable (“hopeful”) ecologies induce slow strategy behaviors such as future focus. We suggest that individuals possess a lay understanding of ecology’s influence on behavior, resulting in ecology-driven stereotypes. Importantly, because race is confounded with ecology in the United States, we propose that Americans’ stereotypes about racial groups actually reflect stereotypes about these groups’ presumed home ecologies. Study 1 demonstrates that individuals hold ecology stereotypes, stereotyping people from desperate ecologies as possessing faster life history strategies than people from hopeful ecologies. Studies 2–4 rule out alternative explanations for those findings. Study 5, which independently manipulates race and ecology information, demonstrates that when provided with information about a person’s race (but not ecology), individuals’ inferences about blacks track stereotypes of people from desperate ecologies, and individuals’ inferences about whites track stereotypes of people from hopeful ecologies. However, when provided with information about both the race and ecology of others, individuals’ inferences reflect the targets’ ecology rather than their race: black and white targets from desperate ecologies are stereotyped as equally fast life history strategists, whereas black and white targets from hopeful ecologies are stereotyped as equally slow life history strategists. These findings suggest that the content of several predominant race stereotypes may not reflect race, per se, but rather inferences about how one’s ecology influences behavior.


title: High-Accuracy Range Detection Radar Sensor for Hydraulic Cylinders

abstract: Industrial automation requires highly precise distance measurement sensors. Accurate detection of the piston position is indispensable for the control and monitoring of hydraulic cylinder applications. Known external and integrated solutions are subject to many limitations as for example in accuracy, in measurement length, or in price. A promising approach consists in the use of precise and low-cost radar sensors. The developed K-band frequency modulated continuous wave (FMCW) radar system in this paper detects the piston position in a hydraulic cylinder based on the guided propagation of a radar signal. The dielectric characteristics of the hydraulic oil are obtained for this purpose by permittivity measurement methods. Influences of the hydraulic oil on wave propagation in cylindrical waveguides as well as mechanical requirements associated with this new approach are investigated. The design of an adapted oil- and pressure-resistant transition between the radar sensor and the hydraulic cylinder and a novel calibration method will be described and verified under real measurement conditions. At a measurement repetition rate of 2 kHz, an accuracy of well below 200 μm was achieved for a hydraulic cylinder of 1 m in length.


title: Taming hardware event samples for FDO compilation

abstract: Feedback-directed optimization (FDO) is effective in improving application runtime performance, but has not been widely adopted due to the tedious dual-compilation model, the difficulties in generating representative training data sets, and the high runtime overhead of profile collection. The use of hardware-event sampling to generate estimated edge profiles overcomes these drawbacks. Yet, hardware event samples are typically not precise at the instruction or basic-block granularity. These inaccuracies lead to missed performance when compared to instrumentation-based FDO@. In this paper, we use multiple hardware event profiles and supervised learning techniques to generate heuristics for improved precision of basic-block-level sample profiles, and to further improve the smoothing algorithms used to construct edge profiles. We demonstrate that sampling-based FDO can achieve an average of 78% of the performance gains obtained using instrumentation-based exact edge profiles for SPEC2000 benchmarks, matching or beating instrumentation-based FDO in many cases. The overhead of collection is only 0.74% on average, while compiler based instrumentation incurs 6.8%-53.5% overhead (and 10x overhead on an industrial web search application), and dynamic instrumentation incurs 28.6%-1639.2% overhead.


title: Robust Top-k Multiclass SVM for Visual Category Recognition

abstract: Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow the learning algorithm to make mistakes on similar classes, as long as the true class is still among the top-k (say) predictions. Likewise, in applications such as search engine or ad display, we are allowed to present k predictions at a time and the customer would be satisfied as long as her interested prediction is included. Inspired by the recent work of [15], we propose a very generic, robust multiclass SVM formulation that directly aims at minimizing a weighted and truncated combination of the ordered prediction scores. Our method includes many previous works as special cases. Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an efficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms). We conduct extensive experiments on four real large-scale visual category recognition datasets, and obtain very promising performances.


title: Medical Text Classification using Convolutional Neural Networks

abstract: We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.


title: Text Detection and Recognition on Traffic Panels From Street-Level Imagery Using Visual Appearance

abstract: Traffic sign detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to intelligent transportation systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a “bag of visual words” and classified using Naive Bayes or support vector machines. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.


title: A general equilibrium model for transportation systems with e-hailing services and flow congestion

abstract: Abstract Passengers are increasingly using e-hailing as a means to request transportation services. Adoption of these types of services has the potential to impact the travel behavior of individuals as well as increase congestion and vehicle miles driven since extra deadhead miles must be added to the trip (e.g., the extra miles from the driver location to the pick-up location of the customer). The objective of this paper is to develop a basic mathematical model to help transportation planners understand the relationship between the wide-scale use of e-hailing transportation services and deadhead miles and resulting impact on congestion. Specifically, this paper develops a general economic equilibrium model at the macroscopic level to describe the equilibrium state of a transportation system composed of solo drivers and the e-hailing service providers (e-HSPs). The equilibrium model consists of three interacting sub-models: e-HSP choice, customer choice, and network congestion; the model is completed with a “market clearance” condition describing the waiting costs in the customer’s optimization problem in terms of the e-HSPs’ decisions, thereby connecting the supply and demand sides of the equilibrium. We show the existence of an equilibrium of a certain kind under some mild assumptions. In numerical experiments, we illustrate the sensitivity on the usage of these modes to various parameters representing cost, value of time, safety, and comfort level, as well as the resulting relationship between usage of these services and vehicle miles.


title: High-Speed FPGA Implementation of Secure Hash Algorithm for IPSec and VPN Applications

abstract: Hash functions are special cryptographic algorithms, which are applied wherever message integrity and authentication are critical. Implementations of these functions are cryptographic primitives widely used in common cryptographic schemes and security protocols such as Internet Protocol Security (IPSec) and Virtual Private Network (VPN). In this paper, a novel FPGA implementation of the Secure Hash Algorithm 1 (SHA-1) is proposed. The proposed architecture exploits the benefits of pipeline and re-timing of execution through pre-computation of intermediate temporal values. Pipeline allows division of the calculation of the hash value in four discreet stages, corresponding to the four required rounds of the algorithm. Re-timing is based on the decomposition of the SHA-1 expression to separate information dependencies and independencies. This allows pre-computation of intermediate temporal values in parallel to the calculation of other independent values. Exploiting the information dependencies, the fundamental operational block of SHA-1 is modified so that maximum operation frequency is increased by 30% approximately with negligible area penalty compared to other academic and commercial implementations. The proposed SHA-1 hash function was prototyped and verified using a XILINX FPGA device. The implementation's characteristics are compared to alternative implementations proposed by the academia and the industry, which are available in the international IP market. The proposed implementation achieved a throughput that exceeded 2,5 Gbps, which is the highest among all similar IP cores for the targeted XILINX technology.


title: Deep Cosine Metric Learning for Person Re-Identification

abstract: Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.


title: A multi task microgrid inverter based instantaneous Power Theory in islanded and grid-connected modes

abstract: This paper develops a flexible control strategy for DG inverters in a microgrid application with load current decomposition based on Instantaneous Power Theory (PQ). For grid connected mode of operation, the control strategy simultaneously supply all or part of the load current components allowing the DG system to inject its available energy, as well as to work as an active power filter, mitigating load current disturbances and improving power quality. Being able to supply the total load current by inverter allows a smooth transition between grid-connected and islanded modes of operation with zero current injection by the grid to guarantee an uninterrupted power supply to the critical loads within the microgrid. For the islanded mode of operation a multi loop control method with preservation of the grid-connected control system is developed to regulate load voltage/frequency in a wide range of load conditions. The control strategy is implemented in abc-frame. The principles supporting the developed control strategy are discussed and analyzed offering a very flexible, selective and powerful strategy for the DG control system, and the effectiveness of the control is demonstrated through digital simulations conducted by PSIM.


title: Virtual reality exposure therapy in anxiety disorders: a systematic review of process-and-outcome studies

abstract: In recent years, virtual reality exposure therapy (VRET) has become an interesting alternative for the treatment of anxiety disorders. Research has focused on the efficacy of VRET in treating anxiety disorders: phobias, panic disorder, and posttraumatic stress disorder. In this systematic review, strict methodological criteria are used to give an overview of the controlled trials regarding the efficacy of VRET in patients with anxiety disorders. Furthermore, research into process variables such as the therapeutic alliance and cognitions and enhancement of therapy effects through cognitive enhancers is discussed. The implications for implementation into clinical practice are considered.


title: DNA methylation of mobile genetic elements in human cancers

abstract: Mobile genetic elements are responsible for half of the human genome, creating the host genomic instability or variability through several mechanisms. Two types of abnormal DNA methylation in the genome, hypomethylation and hypermethylation, are associated with cancer progression. Genomic hypermethylation has been most often observed on the CpG islands around gene promoter regions in cancer cells. In contrast, hypomethylation has been observed on mobile genetic elements in the cancer cells. It is recently considered that the hypomethylation of mobile genetic elements may play a biological role in cancer cells along with the DNA hypermethylation on CpG islands. Growing evidence has indicated that mobile genetic elements could be associated with the cancer initiation and progression through the hypomethylation. Here we review the recent progress on the relationship between DNA methylation and mobile genetic elements, focusing on the hypomethylation of LINE-1 and HERV elements in various human cancers and suggest that DNA hypomethylation of mobile genetic elements could have potential to be a new cancer therapy target in the future.


title: A linear formulation of shape from specular flow

abstract: When a curved mirror-like surface moves relative to its environment, it induces a motion field—or specular flow— on the image plane that observes it. This specular flow is related to the mirror's shape through a non-linear partial differential equation, and there is interest in understanding when and how this equation can be solved for surface shape. Existing analyses of this ‘shape from specular flow equation’ have focused on closed-form solutions, and while they have yielded insight, their critical reliance on externally-provided initial conditions and/or specific motions makes them difficult to apply in practice. This paper resolves these issues. We show that a suitable reparameterization leads to a linear formulation of the shape from specular flow equation. This formulation radically simplifies the reconstruction process and allows, for example, both motion and shape to be recovered from as few as two specular flows even when no externally-provided initial conditions are available. Our analysis moves us closer to a practical method for recovering shape from specular flow that operates under arbitrary, unknown motions in unknown illumination environments and does not require additional shape information from other sources.


Product Lifecycle Management for a Global Market


Business Process Management


title: Online Localization and Prediction of Actions and Interactions

abstract: This paper proposes a person-centric and online approach to the challenging problem of localization and prediction of actions and interactions in videos. Typically, localization or recognition is performed in an offline manner where all the frames in the video are processed together. This prevents timely localization and prediction of actions and interactions - an important consideration for many tasks including surveillance and human-machine interaction. ::: In our approach, we estimate human poses at each frame and train discriminative appearance models using the superpixels inside the pose bounding boxes. Since the pose estimation per frame is inherently noisy, the conditional probability of pose hypotheses at current time-step (frame) is computed using pose estimations in the current frame and their consistency with poses in the previous frames. Next, both the superpixel and pose-based foreground likelihoods are used to infer the location of actors at each time through a Conditional Random. The issue of visual drift is handled by updating the appearance models, and refining poses using motion smoothness on joint locations, in an online manner. For online prediction of action (interaction) confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time progresses. Lastly, we quantify the performance of both detection and prediction together, and analyze how the prediction accuracy varies as a time function of observed action (interaction) at different levels of detection performance. Our experiments on several datasets suggest that despite using only a few frames to localize actions (interactions) at each time instant, we are able to obtain competitive results to state-of-the-art offline methods.


title: Tradeoffs in fine-grained heap memory protection

abstract: Different uses of memory protection schemes have different needs in terms of granularity. For example, heap security can benefit from chunk separation (by using protected "padding" boundaries) and meta-data protection. However, such protection can be done at different granularity (eg. per-word, per-block, or per-page), with different performance, cost and memory overhead tradeoffs for different applications. In this paper, we explore these tradeoffs for the purpose of heap security in order to discover whether the "right" granularity exists and how the granularity of protection affects design decisions.We evaluate such tradeoffs based on the current heap-security approaches in a single address spare operating system. The access control granularities we use are word, 8-byte, 16-byte, 32-byte, and page. We find that none of these schemes is optimal across all applications. In some applications, excessive padding degrades caching performance for coarse-granularity schemes, while in others, large-block permission changes introduce large overheads for finer granularities. To overcome these limitations, we propose a new two-granularity scheme, which uses word- and page-granularity protection to eliminate padding but allow fast page-size permission changes for large memory blocks. On all applications, this new scheme performs as well or better than the best single-granularity scheme. It also performs on par with the more complex Mondrian Memory Protection, which uses a complex trie structure and multiple permissions caching mechanisms to support a hierarchy of protection granularities.


title: L-systems: from the theory to visual models of plants

abstract: In 1968, Aristid Lindenmayer introduced a formalism for simulating the development of multicellular organisms, subsequently named L-systems [18]. This formalism was closely related to abstract automata and formal languages, and attracted the immediate interest of theoretical computer scientists. The vigorous development of the mathematical theory of L-systems was followed by its applications to the modeling of plants. These applications gained momentum after 1984, when Smith introduced state-of-the art computer graphics techniques to visualize the structures and processes being modeled [52]. Smith also attracted attention to the phenomenon of data-base amplification, or the possibility of generating complex structures from compact data sets, which is inherent in L-systems and forms the cornerstone of L-system applications to image synthesis. Subsequent developments (presented here from our personal perspective, without covering the fast-growing array of contributions from many other researchers) included:


title: An ultra-sensitive spintronic strain-gauge sensor with gauge factor of 5000 and demonstration of a Spin-MEMS Microphone

abstract: Here we report the first spintronic strain-gauge sensor (Spin-SGS) based on a magnetic tunnel junction (MTJ) with a high gauge factor in excess of 5000, which was realized by adopting a novel amorphous Fe-B-based sensing layer with high magnetostriction and low coercivity in a high magnetoresistance MgO barrier MTJ. We also demonstrate a novel "Spintronic MEMS (Spin-MEMS) microphone," in which a series of Spin-SGSs are integrated onto a bulk micromachined diaphragm. The Spin-MEMS microphone exhibits a signal-to-noise ratio (SNR) of 57 dB(A) due to the high strain sensitivity of the Spin-SGSs. Furthermore a Spin-MEMS microphone with a first resonance frequency of 74 kHz is also fabricated that exhibits an SNR of 45 dB(A). This demonstrates the feasibility of Spin-SGSs in highly sensitive MEMS sensor applications.


title: Gaussian Conditional Random Field Network for Semantic Segmentation

abstract: In contrast to the existing approaches that use discrete Conditional Random Field (CRF) models, we propose to use a Gaussian CRF model for the task of semantic segmentation. We propose a novel deep network, which we refer to as Gaussian Mean Field (GMF) network, whose layers perform mean field inference over a Gaussian CRF. The proposed GMF network has the desired property that each of its layers produces an output that is closer to the maximum a posteriori solution of the Gaussian CRF compared to its input. By combining the proposed GMF network with deep Convolutional Neural Networks (CNNs), we propose a new end-to-end trainable Gaussian conditional random field network. The proposed Gaussian CRF network is composed of three sub-networks: (i) a CNN-based unary network for generating unary potentials, (ii) a CNN-based pairwise network for generating pairwise potentials, and (iii) a GMF network for performing Gaussian CRF inference. When trained end-to-end in a discriminative fashion, and evaluated on the challenging PASCALVOC 2012 segmentation dataset, the proposed Gaussian CRF network outperforms various recent semantic segmentation approaches that combine CNNs with discrete CRF models.


title: A Bayesian Approach to Generative Adversarial Imitation Learning

abstract: Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks. This paradigm is based on reducing the imitation learning problem to the density matching problem, where the agent iteratively refines the policy to match the empirical state-action visitation frequency of the expert demonstration. Although this approach has shown to robustly learn to imitate even with scarce demonstration, one must still address the inherent challenge that collecting trajectory samples in each iteration is a costly operation. To address this issue, we first propose a Bayesian formulation of generative adversarial imitation learning (GAIL), where the imitation policy and the cost function are represented as stochastic neural networks. Then, we show that we can significantly enhance the sample efficiency of GAIL leveraging the predictive density of the cost, on an extensive set of imitation learning tasks with high-dimensional states and actions.


title: Dimension reduction based on Canonical Correlation Analysis technique to classify sleep stages of sleep apnea disorder using EEG and ECG signals

abstract: Sleep stage scoring is the first step to diagnostic of sleep disorders and it is scored by the conventional method known as the visual sleep stage scoring based on human. To assist the sleep physician in evaluating of patients, a new automatic sleep stage classification system needs to be developed. So this is the aim of this work based on Electroencephalography (EEG) and Electrocardiography (ECG) for Sleep apnea patients. This article proposes two importance topics, the first is the new feature of EEG signal using a simple statistical technique and the results prove that the various sleep stages can be discriminated more clearly at significant levels (p<<0.05). Second, the dimension reduction is proposed based on the Canonical Correlation Analysis (CCA) technique that explores possible correlated multi-sources to improve the sleep stages classification at 95.42% accuracy by using random forest classification. The results show that our proposed method has ability to develop a new sleep stage classification assistance.


title: It Doesn’t Hurt to Ask: Assessing Default Effects in a Brazilian Gas Station

abstract: When an option is set as the default and as a result people are more likely to choose it, we call it a default effect. In this research we investigated whether drivers were more likely to choose a more expensive type of fuel if it was set as the default option by a mere suggestion from the gas station attendant. In the first study, we collected data showing that drivers did not believe they could be affected by default options. The second study, however, refuted these findings: we conducted a field experiment in which a trivial question asked by the gas station attendant, whose effect was the creation of a default option, led drivers to purchase a product they would not otherwise have considered. We thus provide evidence that default effects might be even more pervasive in real situations with real consumers than what one would expect from controlled laboratory experiments.


title: Characterizing Product Lifecycle in Online Marketing: Sales, Trust, Revenue, and Competition Modeling

abstract: Recent researches have seen an upsurge in the analysis of consumer reviews. Although, several dimensions have been explored, less is known on the temporal dynamics of events that happen over the lifecycle of online products. What are the dominant sales patterns? How are they affected by review count, rating, helpfulness and sentiment? How is trust characterized and what are its effects on sales and revenue? What happens during a market competition? When does a takeover/recovery happen and by what percentage do sales increase on a takeover? This work aims to answer these fundamental research questions based on a sales time-series analysis of reviews of over 1 million products from Amazon.com. We discover novel temporal patterns of sales and interesting correlations of sales with the ratings. We find that trust and helpfulness are important for higher revenue. Based on the analyses, we propose a model to forecast sales that significantly outperforms other baselines. We then explore the phenomena of market competition. Particularly, we characterize different factors that govern survival/death of a product under competition and a model for competition forecast. Experimental results on large-scale reviews demonstrate the effectiveness of the proposed approaches.


title: A systematic review of modafinil : Potential clinical uses and mechanisms of action

abstract: BACKGROUND: Modafinil is a novel wake-promoting agent that has U.S. Food and Drug Administration approval for narcolepsy and shift work sleep disorder and as adjunctive treatment of obstructive sleep apnea/hypopnea syndrome. Modafinil has a novel mechanism and is theorized to work in a localized manner, utilizing hypocretin, histamine, epinephrine, gamma-aminobutyric acid, and glutamate. It is a well-tolerated medication with low propensity for abuse and is frequently used for off-label indications. The objective of this study was to systematically review the available evidence supporting the clinical use of modafinil. DATA SOURCES: The search term modafinil OR Provigil was searched on PubMed. Selected articles were mined for further potential sources of data. Abstracts from major scientific conferences were reviewed. Lastly, the manufacturer of modafinil in the United States was asked to provide all publications, abstracts, and unpublished data regarding studies of modafinil. DATA SYNTHESIS: There have been 33 double-blind, placebo-controlled trials of modafinil. Additionally, numerous smaller studies have been performed, and case reports of modafinil's use abound in the literature. CONCLUSIONS: Modafinil is a promising drug with a large potential for many uses in psychiatry and general medicine. Treating daytime sleepiness is complex, and determining the precise nature of the sleep disorder is vital. Modafinil may be an effective agent in many sleep conditions. To date, the strongest evidence among off-label uses exists for the use of modafinil in attention-deficit disorder, postanesthetic sedation, and cocaine dependence and withdrawal and as an adjunct to antidepressants for depression.


Wild Rice Taxonomy


title: Design of an Accurate Yet Low-Cost Distributed Module for Vehicular Relative Positioning: Hardware Prototype Design and Algorithms

abstract: In this paper, we design a modular prototype to provide relative position and orientation (RPO), with higher accuracy yet low-cost implementations. First, we design the hardware module as two distributed sub-modules: the beacon module and the receiver module. The beacon module is designed to have the rotational laser-emitting function and the wireless-broadcasting function, and the receiver module can receive both the laser signals and wireless broadcasting. Then, with these functions, we design the unique operating mechanisms for the whole module. Finally, we provide the event-triggered algorithms for the RPO measurements, which are tightly incorporated with the hardware design. Compared via experiments with other related methods, our design has more measurement capabilities, higher accuracy, and low-cost implementations on both hardware and computations. This module has potential vehicular applications, e.g., in a factory for tasks assistance.


Interaction Design - Beyond Human-Computer Interaction, 3rd Edition


title: Antibiotic Persistence as a Metabolic Adaptation: Stress, Metabolism, the Host, and New Directions

abstract: Persistence is a phenomenon during which a small fraction of a total bacterial population survives treatment with high concentrations of antibiotics for an extended period of time. In conjunction with biofilms, antibiotic persisters represent a major cause of recalcitrant and recurring infections, resulting in significant morbidity and mortality. In this review, we discuss the clinical significance of persister cells and the central role of bacterial metabolism in their formation, specifically with respect to carbon catabolite repression, sugar metabolism, and growth regulation. Additionally, we will examine persister formation as an evolutionary strategy used to tolerate extended periods of stress and discuss some of the response mechanisms implicated in their formation. To date, the vast majority of the mechanistic research examining persistence has been conducted in artificial in vitro environments that are unlikely to be representative of host conditions. Throughout this review, we contextualize the existing body of literature by discussing how in vivo conditions may create ecological niches that facilitate the development of persistence. Lastly, we identify how the development of next-generation sequencing and other “big data” tools may enable researchers to examine persistence mechanisms within the host to expand our understanding of their clinical importance.