Effect of rule weights in fuzzy rule-based classification systems. This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF–THEN rules with certainty grades (i.e., rule weights), the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF–THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF–THEN rules with certainty grades.

Question: What is the effect of rule weights in fuzzy rule-based classification systems?


Facebook use, envy, and depression among college students: Is facebooking depressing?. It is not—unless it triggers feelings of envy. This study uses the framework of social rank theory of depression and conceptualizes Facebook envy as a possible link between Facebook surveillance use and depression among college students. Using a survey of 736 college students, we found that the effect of surveillance use of Facebook on depression is mediated by Facebook envy. However, when Facebook envy is controlled for, Facebook use actually lessens depression. 2014 Elsevier Ltd. All rights reserved.

Question: Is facebooking depressing?


Network forensic frameworks: Survey and research challenges. Network forensics is the science that deals with capture, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper makes an exhaustive survey of various network forensic frameworks proposed till date. A generic process model for network forensics is proposed which is built on various existing models of digital forensics. Definition, categorization and motivation for network forensics are clearly stated. The functionality of various Network Forensic Analysis Tools (NFATs) and network security monitoring tools, available for forensics examiners is discussed. The specific research gaps existing in implementation frameworks, process models and analysis tools are identified and major challenges are highlighted. The significance of this work is that it presents an overview on network forensics covering tools, process models and framework implementations, which will be very much useful for security practitioners and researchers in exploring this upcoming and young discipline. a 2010 Elsevier Ltd. All rights reserved.

Question: What is a network forensic framework?


Mining User Mobility Features for Next Place Prediction in Location-Based Services. Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.

Question: What is the next venue a user will visit?


Powering 3 Dimensional Microrobots : Power Density Limitations ∗. Many types of electrostatic and electromagnetic microactuators have been developed. It is important to understand the fundamental performance limitations of these actuators for use in micro-robotic systems. The most important consideration for micro mobile robots is the effective power density of the actuator. As inertia and gravitional forces become less significant for small robots, typical metrics for macro-robots, such as torque-to-weight ratio, are not appropriate. A very significant problem with micro-actuators for robotics is the need for efficient transmissions to obtain large forces and torques at low speeds from inherently high speed low force actuators.

Question: What is the power density of a micro-robot?


Fracture detection in x-ray images through stacked random forests feature fusion. Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone fracture detection method that is applicable to multiple bone fracture types and multiple bone structures throughout the body. The method uses features extracted from candidate patches in X-ray images in a novel discriminative learning framework called the Stacked Random Forests Feature Fusion. This is a multilayer learning formulation in which the class probability labels, produced by random forests learners at a lower level, are used to derive the refined class distribution labels at the next level. The candidate patches themselves are selected using an efficient subwindow search algorithm. The outcome of the method is a number of fracture bounding-boxes ranked from the most likely to the least likely to contain a fracture. We evaluate the proposed method on a set of 145 X-rays images. When the top ranking seven fracture bounding-boxes are considered, we are able to capture 81.2% of the fracture findings reported by a radiologist. The proposed method outperforms other fracture detection frameworks that use local features, and single layer random forests and support vector machine classification.

Question: What is the most common fracture type in musculoskeletal injuries?


Differences in energy expenditure during high-speed versus standard-speed yoga: A randomized sequence crossover trial.. OBJECTIVES To compare energy expenditure and volume of oxygen consumption and carbon dioxide production during a high-speed yoga and a standard-speed yoga program.   DESIGN Randomized repeated measures controlled trial.   SETTING A laboratory of neuromuscular research and active aging.   INTERVENTIONS Sun-Salutation B was performed, for eight minutes, at a high speed versus and a standard-speed separately while oxygen consumption was recorded. Caloric expenditure was calculated using volume of oxygen consumption and carbon dioxide production.   MAIN OUTCOME MEASURES Difference in energy expenditure (kcal) of HSY and SSY.   RESULTS Significant differences were observed in energy expenditure between yoga speeds with high-speed yoga producing significantly higher energy expenditure than standard-speed yoga (MD=18.55, SE=1.86, p<0.01). Significant differences were also seen between high-speed and standard-speed yoga for volume of oxygen consumed and carbon dioxide produced.   CONCLUSIONS High-speed yoga results in a significantly greater caloric expenditure than standard-speed yoga. High-speed yoga may be an effective alternative program for those targeting cardiometabolic markers.

Question: What is the difference between high-speed and standard-speed yoga?


Applications of Educational Data Mining: A survey. Various educational oriented problems are resolved through Educational Data Mining, which is the most prevalent applications of data mining. One of the crucial goals of this paper is to study the most recent works carried out on EDM and analyze their merits and drawbacks. This paper also highlights the cumulative results of the various data mining practices and techniques applied in the surveyed articles, and thereby suggesting the researchers on the future directions on EDM. In addition, an experiment was also conducted to evaluate, certain classification and clustering algorithms to observe the most reliable algorithms for future researches.

Question: What is the most recent works carried out on EDM?


A Multiband Antenna for Satellite Communications on the Move. The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included

Question: What is a multiband antenna?


Decision Support based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study. The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making. KEywoRDS Bio-PEPA Modelling, Data Mining, Decision Support, Decision Tree Induction, Epidemiology, Modelling and Simulation, Optimisation, Refinement, Tuberculosis

Question: What is the difference between a decision tree and a decision support system?


ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations. Understanding clothes from a single image would have huge commercial and cultural impacts on modern societies. However, this task remains a challenging computer vision problem due to wide variations in the appearance, style, brand and layering of clothing items. We present a new database called ModaNet, a large-scale collection of images based on Paperdoll dataset. Our dataset provides 55,176 street images, fully annotated with polygons on top of the 1 million weakly annotated street images in Paperdoll. ModaNet aims to provide a technical benchmark to fairly evaluate the progress of applying the latest computer vision techniques that rely on large data for fashion understanding. The rich annotation of the dataset allows to measure the performance of state-of-the-art algorithms for object detection, semantic segmentation and polygon prediction on street fashion images in detail.

Question: What is the size of the modanet dataset?


A Randomized Singular Value Decomposition Algorithm for Image Processing Applications. The main contribution of this paper is to demonstrate that a new randomized SVD algorithm, proposed by Drineas et. al. in [4], is not only of theoretical interest but also a viable and fast alternative to traditional SVD algorithms in applications (e.g. image processing). This algorithm samples a constant number of rows (or columns) of the matrix, scales them appropriately to form a small matrix, say S, and then computes the SVD of S (which is a good approximation to the SVD of the original matrix). We experimentally evaluate the accuracy and speed of this algorithm for image matrices, using various probability distributions to perform the sampling.

Question: What is the main contribution of this paper?


The structure of creative cognition in the human brain. Creativity is a vast construct, seemingly intractable to scientific inquiry-perhaps due to the vague concepts applied to the field of research. One attempt to limit the purview of creative cognition formulates the construct in terms of evolutionary constraints, namely that of blind variation and selective retention (BVSR). Behaviorally, one can limit the "blind variation" component to idea generation tests as manifested by measures of divergent thinking. The "selective retention" component can be represented by measures of convergent thinking, as represented by measures of remote associates. We summarize results from measures of creative cognition, correlated with structural neuroimaging measures including structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS). We also review lesion studies, considered to be the "gold standard" of brain-behavioral studies. What emerges is a picture consistent with theories of disinhibitory brain features subserving creative cognition, as described previously (Martindale, 1981). We provide a perspective, involving aspects of the default mode network (DMN), which might provide a "first approximation" regarding how creative cognition might map on to the human brain.

Question: What is the structure of creative cognition in the human brain?


Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. The primary sensory system requires the integrated function of multiple cell types, although its full complexity remains unclear. We used comprehensive transcriptome analysis of 622 single mouse neurons to classify them in an unbiased manner, independent of any a priori knowledge of sensory subtypes. Our results reveal eleven types: three distinct low-threshold mechanoreceptive neurons, two proprioceptive, and six principal types of thermosensitive, itch sensitive, type C low-threshold mechanosensitive and nociceptive neurons with markedly different molecular and operational properties. Confirming previously anticipated major neuronal types, our results also classify and provide markers for new, functionally distinct subtypes. For example, our results suggest that itching during inflammatory skin diseases such as atopic dermatitis is linked to a distinct itch-generating type. We demonstrate single-cell RNA-seq as an effective strategy for dissecting sensory responsive cells into distinct neuronal types. The resulting catalog illustrates the diversity of sensory types and the cellular complexity underlying somatic sensation.

Question: What is the difference between a mechanoreceptor and a thermosensitive neuron?


Designing inflatable structures. We propose an interactive, optimization-in-the-loop tool for designing inflatable structures. Given a target shape, the user draws a network of seams defining desired segment boundaries in 3D. Our method computes optimally-shaped flat panels for the segments, such that the inflated structure is as close as possible to the target while satisfying the desired seam positions. Our approach is underpinned by physics-based pattern optimization, accurate coarse-scale simulation using tension field theory, and a specialized constraint-optimization method. Our system is fast enough to warrant interactive exploration of different seam layouts, including internal connections, and their effects on the inflated shape. We demonstrate the resulting design process on a varied set of simulation examples, some of which we have fabricated, demonstrating excellent agreement with the design intent.

Question: How do you design inflatable structures?


A framework for efficient network anomaly intrusion detection with features selection. An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.

Question: What is a framework for efficient network anomaly intrusion detection?


3D deep convolutional neural networks for amino acid environment similarity analysis. Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. End-to-end trained deep learni

Question: What is the difference between a deep convolutional neural network and a deep belief network?


From Word To Sense Embeddings: A Survey on Vector Representations of Meaning. Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.

Question: What is the meaning of the word "unambiguous"?


Applications of data mining in robotics. From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.

Question: What is data mining?


Automated detection of vulnerabilities in privileged programs by execution monitoring. We present a method for detecting exploitations of vulnerabilities in privileged programs by monitoring their execution using audit trials, where the monitoring is with respect to specifications of the security-relevant behavior of the programs. Our work is motivated by the intrusion detection paradigm, but is a n attempt to avoid a d hoc approaches to codifying misuse behavior. Our approach as based on the observation that although privileged programs can be exploited (due to errors) to cause security compromise in systems because of the privileges accorded to them, the intended behavior of privileged programs is, of course, limited a n d benign. The key, then i s to specify the intended behavior (Le., the program policy) and to detect any action by privileged program that is outside the intended behavior a n d that imperils security. We describe a program policy specification language, which is based on simple predicate logic and regular expressions. I n addition, we present specifications of privileged programs in Unix, and Q prototype execution monitor for analyzing audit trails with respect to these specifications. The program policies are surprisingly concise and clear, a n d an addition, capable of detecting exploitations of known vulnerabilities in these programs. Although our work has been motivated by the known vulnerabilities in Unix, we believe that by tightly restricting the behavior of all privileged programs, exploitations of unknown vulnerabilities can be detected. A s Q check on the specifications, work is in progress on verifying them with respect to a n abstract security policy. *This work is funded in part by the National Security Agency University Research Program under Contract No. DOD-MDA904-93-C4083 and by ARPA under Contract No. USNN00014-94-1-0065.

Question: What is the canadian military?


Rejecting Motion Outliers for Efficient Crowd Anomaly Detection. Crowd anomaly detection is a key research area in vision-based surveillance. Most of the crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be applicable for battery-powered surveillance cameras. In this paper, we present a new crowd anomaly detection algorithm. The proposed algorithm creates a feature for every superpixel that includes the contribution from the neighboring superpixels only if their direction of motion conforms with the dominant direction of motion in the region. We also propose using univariate Gaussian discriminant analysis with the K-means algorithm for classification. Our method provides superior accuracy over numerous deep learning-based and handcrafted feature-based approaches. We also present a low-power FPGA implementation of the proposed method. The algorithm is developed such that features are extracted over non-overlapping pixels. This allows gating inputs to numerous modules resulting in higher power efficiency. The maximum energy required per pixel is 2.43 nJ in our implementation. 126.65 Mpixels can be processed per second by the proposed implementation. The speed, power, and accuracy performance of our method make it competitive for surveillance applications, especially battery-powered surveillance cameras.

Question: How to detect anomalies in crowd videos?


A Morphable Model for the Synthesis of 3D Faces. In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an "unlikely" appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

Question: How to model a 3D face?


Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting "machines reading regression data" in reach.

Question: What is a logical inference task?


LLC resonant converter burst mode control with constant burst time and optimal switching pattern. In this paper, a novel burst mode control solution for LLC resonant converters is proposed for the highest efficiency and small output voltage ripple. The burst time is set to be constant, the off time is modulated by load conditions. During the burst time, three pulse switching pattern is implemented to keep output voltage low frequency ripple minimal. The switch first turned on is controlled to be last turned off to reduce the initial hard switching turn on loss when burst starts. The pulse width of first switch is optimized to settle LLC to the steady-state under the highest efficiency load condition in one pulse. Zero current detection (ZCD) of the resonant current is implemented to achieve first switch partial ZVS, further increasing light load efficiency.

Question: How to control LLC resonant converter burst mode?


Understanding the Spatial and Temporal Activity Patterns of Subway Mobility Flows. In urban transportation systems, mobility flows in the subway system reflect the spatial and temporal dynamics of working days. To investigate the variability of mobility flows, we analyse the spatial community through a series of snapshots of subway stations over sequential periods. Using Shanghai as a case study, we find that the spatial community snapshots reveal dynamic passenger activities. Adopting a dual-perspective, we apply spatial and temporal models separately to explore where and when individuals travel for entertainment. In the two models, microblog topics and spatial facilities such as food venues and entertainment businesses are used to characterise the spatial popularity of each station and people's travelling perceptions. In the studied case, the city centre is characterised by greater social influence, and it is better described by the spatial model. In the temporal model, shorter travel distances motivate individuals to start their trips earlier. Interestingly, as the number of food-related facilities near the starting station increases, until it exceeds 1563, the speed of people's journeys slows down. This study provides a method for modelling the effects of social features on mobility flows and for predicting the spatial-temporal mobility flows of newly built subway stations.

Question: What is the difference between the spatial and temporal models?


Hashcash - Amortizable Publicly Auditable Cost-Functions. We present a distributed efficiently amortizable CPU cost-function with no trap–door. The absence of a trap–door allows us to avoid needing to trust any party. Applications for such cost-functions are in distributed document popularity estimation, and metering of web advertising. None of the servers involved have any advantage over users in computing the cost function. The amortized token has a small fixed sized representation independent of the amortized value. The valuation function is efficient. Limits can be placed on the resources which can be expended in computing the cost-function to prevent the user inflating the value of his contribution by using more CPU time than the expected token value. The amortized part of the cost result can be blinded so that clients can not obtain service but avoid contributing to the amortization by expending more resources than the expected token value. Interactive and non-interactive variants of the cost-function can be constructed which can be used in situations where the server can issue a challenge (connection oriented interactive protocol), and where it can't (where the communication is store–and–forward, or packet oriented) respectively.

Question: What is a hashcash?


RCS Reduction of Ridged Waveguide Slot Antenna Array Using EBG Radar Absorbing Material. This letter investigates the application of EBG radar absorbing material (RAM) to asymmetric ridged waveguide slot antenna array to reduce its backward RCS. The EBG RAM is based on the mushroom-like EBG structure loaded with lumped resistances. A ridged waveguide slot antenna array with 4 times 10 slot elements was designed and built, part of the metal ground plane of the array is replaced with this EBG RAM. The measured results show that performance of the antenna array is preserved when EBG RAM is used. At working frequency the backward RCS of antenna array with EBG RAM has dramatically reduced than that of the common antenna array.

Question: What is the backward RCS of an antenna array?


Advertising Keyword Recommendation based on Supervised Link Prediction in Multi-Relational Network. In the sponsored search system, advertisers bid on keywords that are related to their products or services to participate in repeated auctions and display their ads on the search result pages. Since there is a huge inventory of possible terms, instructive keyword recommendation is an important component to help advertisers optimize their campaigns and improve ad monetization. In this paper, by constructing a heterogeneous network which contains four types of links between advertisers and keywords based on different data resources and mining complex representations of network structure and task-guided attributes of nodes, we propose an approach to keyword recommendation based on supervised link prediction in multi-relational network. This method can retrieve ample candidates and provide informative ranking scores for recommended list of keywords, experimental results with real sponsored search data validate the effectiveness of the proposed algorithm in producing valuable keywords for advertisers.

Question: What is the canadian military?


Extracting Opinion Features in Sentiment Patterns. Due to the increasing amount of opinions and reviews on the Internet, opinion mining has become a hot topic in data mining, in which extracting opinion features is a key step. Most of existing methods utilize a rule-based mechanism or statistics to extract opinion features, but they ignore the structure characteristics of reviews. The performance has hence not been promising. A new approach of OFESP (Opinion Feature Extraction based on Sentiment Patterns) is proposed in this paper, which takes into account the structure characteristics of reviews for higher values of precision and recall. With a self-constructed database of sentiment patterns, OFESP matches each review sentence to obtain its features, and then filters redundant features regarding relevance of the domain, statistics and semantic similarity. Experimental studies on real-world data demonstrate that as compared with the traditional method based on a window mechanism, OFESP outperforms it on precision, recall and F-score. Meanwhile, in comparison to the approach based on syntactic analysis, OFESP performs better on recall and F-score.

Question: What is the self-constructed database of sentiment patterns?


NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison. Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as 'Big Data' that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can't be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as NoSQL, NewSQL and Search-based systems. This paper motivation is to provide classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.

Question: What is the difference between NoSQL and RDBMS?


Grid-Connected Photovoltaic Generation System. This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system.

Question: What is the difference between a grid-connected photovoltaic generation system and a stand-alone photovoltaic generation system?


Dynamic path planning and replanning for mobile robots using RRT. It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT∗ is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method.

Question: What is a robot?


Fundamentals of Artificial Neural Networks. As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have Historically the data pairs and logical, fashion transition while associative memories commonly? This organization of such as a, clear and algorithms search simulated annealing learning algorithm. After the utility of how to, discover form. More than three different levels of the cost function will. Dean pomerleau in reinforcement and they, discovered two chapters on. This view the application for handwriting recognition this approach by arrows originating.

Question: What is the cost function?


Cannabis, a complex plant: different compounds and different effects on individuals.. Cannabis is a complex plant, with major compounds such as delta-9-tetrahydrocannabinol and cannabidiol, which have opposing effects. The discovery of its compounds has led to the further discovery of an important neurotransmitter system called the endocannabinoid system. This system is widely distributed in the brain and in the body, and is considered to be responsible for numerous significant functions. There has been a recent and consistent worldwide increase in cannabis potency, with increasing associated health concerns. A number of epidemiological research projects have shown links between dose-related cannabis use and an increased risk of development of an enduring psychotic illness. However, it is also known that not everyone who uses cannabis is affected adversely in the same way. What makes someone more susceptible to its negative effects is not yet known, however there are some emerging vulnerability factors, ranging from certain genes to personality characteristics. In this article we first provide an overview of the biochemical basis of cannabis research by examining the different effects of the two main compounds of the plant and the endocannabinoid system, and then go on to review available information on the possible factors explaining variation of its effects upon different individuals.

Question: What is the endocannabinoid system?


A three-stage decision framework for multi-subject emotion recognition using physiological signals. This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.

Question: How to recognize multiple emotions of multiple subjects?


Deep convolutional acoustic word embeddings using word-pair side information. Recent studies have been revisiting whole words as the basic modelling unit in speech recognition and query applications, instead of phonetic units. Such whole-word segmental systems rely on a function that maps a variable-length speech segment to a vector in a fixed-dimensional space; the resulting acoustic word embeddings need to allow for accurate discrimination between different word types, directly in the embedding space. We compare several old and new approaches in a word discrimination task. Our best approach uses side information in the form of known word pairs to train a Siamese convolutional neural network (CNN): a pair of tied networks that take two speech segments as input and produce their embeddings, trained with a hinge loss that separates same-word pairs and different-word pairs by some margin. A word classifier CNN performs similarly, but requires much stronger supervision. Both types of CNNs yield large improvements over the best previously published results on the word discrimination task.

Question: What is a hinge loss?


Interpolating and approximating implicit surfaces from polygon soup. This paper describes a method for building interpolating or approximating implicit surfaces from polygonal data. The user can choose to generate a surface that exactly interpolates the polygons, or a surface that approximates the input by smoothing away features smaller than some user-specified size. The implicit functions are represented using a moving least-squares formulation with constraints integrated over the polygons. The paper also presents an improved method for enforcing normal constraints and an iterative procedure for ensuring that the implicit surface tightly encloses the input vertices.

Question: How to build an implicit surface from polygon soup?


Evaluating enterprise resource planning ( ERP ) post-implementation problems in Egypt. The aim of the research is to identify and evaluate the main problems experienced in the ERP postimplementation stage of multinational, privately-owned Egyptian and governmental organizations in Egypt. Data gathering was achieved by means of a set of interviews and online questionnaire conducted to 50 companies implementing ERP in Egypt. The paper presents a descriptive analysis of the difficulties and problems encountered by organizations in Egypt following ERP implementation and how these have contributed to unsuccessful implementation overall.

Question: What is the aim of the research?


Mining long-term search history to improve search accuracy. Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.

Question: What is the best way to mine long-term search history?


Design of dual-polarized waveguide slotted antenna array for Ka-band application. A dual polarized waveguide slotted array for Ka band application is designed. Vertical polarization (VP) is realized by longitudinal slots on broad wall of the ridge waveguide sub array, while horizontal polarization (HP) is realized by tilted slot pairs in the narrow wall of the rectangular waveguide sub array. The feed networks are also designed for each polarization. Especially that for VP, a method to improve the coupling is presented. The antenna has low cross-polarization level and high degree of isolation between two ports.

Question: How to design a dual polarized waveguide slotted antenna array for Ka band application?


Cognitive Neuroscience : implications for education ?. Research into the functioning of the human brain, particularly during the past decade, has greatly enhanced our understanding of cognitive behaviours which are fundamental to education: learning, memory, intelligence, emotion. Here, we argue the case that research findings from cognitive neuroscience hold implications for educational practice. In doing so we advance a bio-psycho-social position that welcomes multi-disciplinary perspectives on current educational challenges. We provide some examples of research implications which support conventional pedagogic wisdom, and others which are novel and perhaps counter-intuitive. As an example, we take a model of adaptive plasticity that relies on stimulus reinforcement and examine possible implications for pedagogy and curriculum depth. In doing so, we reject some popular but over-simplistic applications of neuroscience to education. In sum, the education profession could benefit from embracing rather than ignoring cognitive neuroscience. Moreover, educationists should be actively contributing to the research agenda of future brain research.

Question: What are the implications of cognitive neuroscience for education?


A Kernel Approach to Comparing Distributions. We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

Question: How to compare distributions?


Spark: Cluster Computing with Working Sets. MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

Question: What is the difference between Spark and Hadoop?


Design Procedure of Dual-Stator Spoke-Array Vernier Permanent-Magnet Machines. The dual-stator spoke-array vernier permanent-magnet (DSSA VPM) machines proposed in the previous papers have been proven to be with high torque density and high power factor. However, the design procedure on the DSSA VPM machines has not been well established, and there is little design experience to be followed, which makes the DSSA VPM machine design quite difficult. This paper presents the detailed DSSA VPM machine design procedure including decision of design parameter initial values, analytical sizing equation, geometric size relationship, and so on. In order to get reasonable design parameter initial values which can reduce the number of design iteration loop, the influence of the key parameters, such as rotor/stator pole combination, slot opening, magnet thickness, etc., on the performances is analyzed based on the finite-element algorithm (FEA) in this paper, and the analysis results can be regarded as design experience during the selection process of the initial values. After that, the analytical sizing equation and geometric relationship formulas are derived and can be used to obtain and optimize the size data of the DSSA VPM machines with little time consumption. The combination of the analytical and FEA methods makes the design procedure time-effective and reliable. Finally, the design procedure is validated by experiments on a DSSA VPM prototype with 2000 N·m.

Question: How to design a dual-stator spoke-array vernier permanent-magnet machine?


Sego: Pervasive Trusted Metadata for Efficiently Verified Untrusted System Services. Sego is a hypervisor-based system that gives strong privacy and integrity guarantees to trusted applications, even when the guest operating system is compromised or hostile. Sego verifies operating system services, like the file system, instead of replacing them. By associating trusted metadata with user data across all system devices, Sego verifies system services more efficiently than previous systems, especially services that depend on data contents. We extensively evaluate Sego's performance on real workloads and implement a kernel fault injector to validate Sego's file system-agnostic crash consistency and recovery protocol.

Question: What is Sego?


Extreme scale with full SQL language support in microsoft SQL Azure. Cloud SQL Server is an Internet scale relational database service which is currently used by Microsoft delivered services and also offered directly as a fully relational database service known as "SQL Azure". One of the principle design objectives in Cloud SQL Server was to provide true SQL support with full ACID transactions within controlled scale "consistency domains" and provide a relaxed degree of consistency across consistency domains that would be viable to clusters of 1,000's of nodes. In this paper, we describe the implementation of Cloud SQL Server with an emphasis on this core design principle.

Question: What is the cloud sql server?


Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training. Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures. Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks. These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, provi

Question: How does transcranial direct current stimulation affect learning?


Inference and Analysis of Population Structure Using Genetic Data and Network Theory.. Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free

Question: What is the difference between a model-based and a model-free approach?


U-Net: Convolutional Networks for Biomedical Image Segmentation.

Question: What is the u-net?


Abstraction Refinement for Probabilistic Software. ion Refinement for Probabilistic Software Mark Kattenbelt, Marta Kwiatkowska, Gethin Norman, and David Parker Oxford University Computing Laboratory, Parks Road, Oxford, OX1 3QD Abstract. We present a methodology and implementation for verifying ANSI-C programs that exhibit probabilistic behaviour, such as failures or randomisation. We use abstraction-refinement techniques that represent We present a methodology and implementation for verifying ANSI-C programs that exhibit probabilistic behaviour, such as failures or randomisation. We use abstraction-refinement techniques that represent probabilistic programs as Markov decision processes and their abstractions as stochastic two-player games. Our techniques target quantitative properties of software such as "the maximum probability of file-transfer failure" or "the minimum expected number of loop iterations" and the abstractions we construct yield lower and upper bounds on these properties, which then guide the refinement process. We build upon stateof-the-art techniques and tools, using SAT-based predicate abstraction, symbolic implementations of probabilistic model checking and components from GOTO-CC, SATABS and PRISM. Experimental results show that our approach performs very well in practice, successfully verifying actual networking software whose complexity is significantly beyond the scope of existing probabilistic verification tools.

Question: What is the maximum probability of file-transfer failure?


Prediction of Hard Drive Failures via Rule Discovery from AutoSupport Data. The ability to accurately predict an impending hard disk failure is important for reliable storage system design. The facility provided by most hard drive manufacturers, called S.M.A.R.T. (selfmonitoring, analysis and reporting technology), has been shown by current research to have poor predictive value. The problem of finding alternatives to S.M.A.R.T. for predicting disk failure is an area of active research. In this paper, we present a rule discovery methodology, and show that it is possible to construct decision support systems that can detect such failures using information recorded in the AutoSupport database. We demonstrate the effectiveness of our system by evaluating it on disks that were returned to NetApp from the field. Our evaluation shows that our system can be tuned either to have a high failure detection rate (i.e., classify a bad disk as bad) or to have a low false alarm rate (i.e., not classify a good disk as bad). Further, our rule-based classifier generates rules that are intuitive and easy to understand, unlike black box techniques.

Question: How can we predict hard drive failures?


Improved DV-Hop localization algorithm for wireless sensor networks. Wireless sensor networks (WSN) are usually composed of a great number of randomly deployed nodes that communicate among themselves and gather information about the environment. In many applications, it is required to know geographical location of the sensor which detected an event. The existing localization algorithms can be classified into two categories: range-based and range-free. Range-based algorithms measure the actual distances between nodes, while range-free algorithms approximate the distance based on connectivity information. Range-free localization is cost-effective alternative to a more expensive range based approaches since there is no necessity for additional hardware. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is one of the range-free localization algorithms utilizing hop-distance estimation. In this paper, we propose an improvement of the range-free DV-Hop localization algorithm and present simulations results to evaluate it.

Question: What is the difference between range-based and range-free localization algorithms?


Wound microbiology and associated approaches to wound management.. The majority of dermal wounds are colonized with aerobic and anaerobic microorganisms that originate predominantly from mucosal surfaces such as those of the oral cavity and gut. The role and significance of microorganisms in wound healing has been debated for many years. While some experts consider the microbial density to be critical in predicting wound healing and infection, others consider the types of microorganisms to be of greater importance. However, these and other factors such as microbial synergy, the host immune response, and the quality of tissue must be considered collectively in assessing the probability of infection. Debate also exists regarding the value of wound sampling, the types of wounds that should be sampled, and the sampling technique required to generate the most meaningful data. In the laboratory, consideration must be given to the relevance of culturing polymicrobial specimens, the value in identifying one or more microorganisms, and the microorganisms that should be assayed for antibiotic susceptibility. Although appropriate systemic antibiotics are essential for the treatment of deteriorating, clinically infected wounds, debate exists regarding the relevance and use of antibiotics (systemic or topical) and antiseptics (topical) in the treatment of nonhealing wounds that have no clinical signs of infection. In providing a detailed analysis of wound microbiology, together with current opinion and controversies regarding wound assessment and treatment, this review has attempted to capture and address microbiological aspects that are critical to the successful management of microorganisms in wounds.

Question: What is the role of microorganisms in wound healing?


Fast efficient algorithm for enhancement of low lighting video. We describe a novel and effective video enhancement algorithm for low lighting video. The algorithm works by first inverting the input low-lighting video and then applying an image de-haze algorithm on the inverted input. To facilitate faster computation and improve temporal consistency, correlations between temporally neighboring frames are utilized. Simulations using naive implementations of the algorithm show good enhancement results and 2x speed-up as compared with frame-wise enhancement algorithms, with further improvements in both quality and speed possible.

Question: What is a naive implementation of an algorithm?


NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval. Words to express relations in natural language (NL) statements may be different from those to represen t properties in knowledge bases (KB). The vocabulary g p becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relati onal pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, t he methodology can be extended to other languages.

Question: How to map a property in KB to a relation in NL?


Intrusion Detection System using K-means , PSO with SVM Classifier : A Survey. Intrusion detection is a process of identifying the Attacks. The main aim of IDS is to identify the Normal and Intrusive activities. In recent years, many researchers are using data mining techniques for building IDS. Here we propose a new approach using data mining technique such as SVM and Particle swarm optimization for attaining higher detection rate. The propose technique has major steps: Preprocessing, Training using PSO, clustering using K-means to generate different training subsets. Then based on the subsequent training subsets a vector for SVM classification is formed and in the end, classification using PSO is performed to detect Intrusion has happened or not. This paper contains summarization study and identification of the drawbacks of formerly surveyed works. Keywords-Intrusion detection system; Neuro-fuzzy; Support Vector Machine (SVM); PSO; K-means

Question: What is the purpose of intrusion detection system?


Polymer Nanoparticles for Smart Drug Delivery. In the recent decades, polymers are widely used as biomaterials due to their favorable properties such as good biocompatibility, easy design and preparation, a variety of structures and interesting bio-mimetic character. Especially in the field of smart drug delivery, polymer played a significant role because it can deliver therapeutic agents directly into the intended site of action, with superior efficacy. The ideal requirements for designing nano-particulate delivery system are to effectively be controlled particle size, surface character; enhance permeation, flexibility, solubility and release of therapeutically active agents in order to attain the target and specific activity at a predetermined rate and time. The smart drug delivery systems have been successfully made by the advances in polymer science in the bio-nano‐ technology field. Recently, these advances have been found in various medical applications for nano-scale structures in smart drug delivery. The smart drug delivery systems should possess some important feature such as pre-scheduled rate, self controlled, targeted, predetermined time and monitor the delivery. The smart drug delivery system enhances the polymer nanoparticle better stage to their therapy regimen. They are drug carriers of natural, semi-synthetic, and synthetic polymeric nature at the nano-scale to micro-scale range. The polymeric particles are collectively named as spheres and capsules. The most of the polymeric nanoparticles with surfactants offer stability of various forms of active drugs and have useful to smart release properties. There are numerous biological applications have been reported for the nano-scale to micro-scale sized particles, such as site-targeted, controlled, and enhanced bioavailability of hydrophobic drugs [1-4]. Due to the nanoparticles size the drugs have been targeting into various applications, such as, various cancers targeting has been shown to be promising

Question: What is the difference between a smart drug delivery system and a drug delivery system?


Parallel Cat Swarm Optimization. We investigate a parallel structure of cat swarm optimization (CSO) in this paper, and we call it parallel cat swarm optimization (PCSO). In the experiments, we compare particle swarm optimization (PSO) with CSO and PCSO. The experimental results indicate that both CSO and PCSO perform well. Moreover, PCSO is an effective scheme to improve the convergent speed of cat swarm optimization in case the population size is small and the whole iteration is less.

Question: What is the difference between parallel cat swarm optimization and cat swarm optimization?


An examination of the determinants of customer loyalty in mobile commerce contexts. While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed. # 2005 Elsevier B.V. All rights reserved.

Question: What is the difference between customer loyalty and customer satisfaction?


An approach to automotive ECG measurement validation using a car-integrated test framework. Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.

Question: How to validate ECG measurement systems in a car?


Improving Design of Systems Supporting Creativity-intensive Processes - A Cross-industry Focus Group Evaluation.

Question: What is a cross-industry focus group evaluation?


Online Anomaly Detection in Crowd Scenes via Structure Analysis. Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural context descriptor (SCD) for describing the crowd individual, which originally introduces the potential energy function of particle's interforce in solid-state physics to intuitively conduct vision contextual cueing. For computing the crowd SCD variation effectively, we then design a robust multi-object tracker to associate the targets in different frames, which employs the incremental analytical ability of the 3-D discrete cosine transform (DCT). By online spatial-temporal analyzing the SCD variation of the crowd, the abnormality is finally localized. Our contribution mainly lies on three aspects: 1) the new exploration of abnormal detection from structure modeling where the motion difference between individuals is computed by a novel selective histogram of optical flow that makes the proposed method can deal with more kinds of anomalies; 2) the SCD description that can effectively represent the relationship among the individuals; and 3) the 3-D DCT multi-object tracker that can robustly associate the limited number of (instead of all) targets which makes the tracking analysis in high density crowd situation feasible. Experimental results on several publicly available crowd video datasets verify the effectiveness of the proposed method.

Question: What is the difference between a structure model and a structure descriptor?


Accelerating Apache Hive with MPI for Data Warehouse Systems. Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.

Question: What is the difference between data warehouse systems and Hadoop?


Counting of cigarettes in cigarette packets using LabVIEW. The proposed work presents a technique for an automated count of cigarette in cigarette packets. Proposed work is based on application of image processing techniques in LabVIEW platform. An objective of the proposed work is to count the number of cigarettes in a packet. National Instrument's Smart camera is used to capture images of cigarette packets moving in packaging line and process the data to fulfill the above objective. The technique was subjected to offline testing on more than 50 number of cigarette packets and the results obtained are found to be satisfactory in all cases.

Question: How to count the number of cigarettes in a packet?


Principles of Robot Motion: Theory, Algorithms, and Implementations [Book Review]. introduction to autonomous mobile robots intelligent robotics and autonomous agents series PDF disaster robotics intelligent robotics and autonomous agents series PDF probabilistic robotics intelligent robotics and autonomous agents series PDF self-reconfigurable robots an introduction intelligent robotics and autonomous agents series PDF mechanics of robotic manipulation intelligent robotics and autonomous agents PDF strategic negotiation in multiagent environments intelligent robotics and autonomous agents PDF random finite sets for robot mapping & slam new concepts in autonomous robotic map representations springer tracts in advanced robotics PDF

Question: What is the book about?


The Intersecting Roles of Consumer and Producer : A Critical Perspective on Co-production , Co-creation and Prosumption. The terms 'co-creation', 'co-production', and 'prosumption' refer to situations in which consumers collaborate with companies or with other consumers to produce things of value. These situations sometimes appear to blur the traditional roles of 'producer' and 'consumer'. Building on Marx's distinction between 'use value' and 'exchange value', we argue that, when consumers perform tasks normally handled by the company, this does not necessarily represent a fundamental change in exchange roles or economic organization. We then argue that, when individuals who are traditionally defined as 'consumers' produce exchange value for companies, this does represent a fundamental change. Thanks to recent advances in information technology, customers today are contributing to organizational processes in ways that would not have been possible 10 years ago. For example, at Threadless.com, customers can not only vote for the clothing designs that they would like to see produced but also submit their own designs for voting (Walker 2007). Similarly, at National Instruments, a company that makes measurement software and sensors, nearly half of the company's research and development activity is done by an on-line community of customers who collaborate to discover how NI's products can solve members' problems (Seybold 2007). Another example is Proctor & Gamble's 'Vocalpoint' program, an on-line community of product enthusiasts who are rewarded with coupons and samples in return for talking about products with their friends. The program has more than 500,000 members and is being used by other companies such as WD40 and the Discovery Channel to promote their products (Neff 2006). Some have argued that examples like these are symptomatic of a fundamental and dramatic shift that is occurring in relationships between organizations and their customers – a change that calls

Question: What is the difference between 'use value' and 'exchange value'?


S3: A Symbolic String Solver for Vulnerability Detection in Web Applications. Motivated by the vulnerability analysis of web programs which work on string inputs, we present S3, a new symbolic string solver. Our solver employs a new algorithm for a constraint language that is expressive enough for widespread applicability. Specifically, our language covers all the main string operations, such as those in JavaScript. The algorithm first makes use of a symbolic representation so that membership in a set defined by a regular expression can be encoded as string equations. Secondly, there is a constraint-based generation of instances from these symbolic expressions so that the total number of instances can be limited. We evaluate S3 on a well-known set of practical benchmarks, demonstrating both its robustness (more definitive answers) and its efficiency (about 20 times faster) against the state-of-the-art.

Question: What is a symbolic string solver?


Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction. In robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it, and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of graphics processing unit (GPU) computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real time. In this letter, we improved and extended the state-of-the-art incremental manifold mesh algorithm proposed by Litvinov and Lhuillier and extended by Romanoni and Matteucci. While these algorithms do not reconstruct the map in real time and they embed points from SLAM or structure from motion only when their position is fixed, in this letter, we propose the first incremental algorithm able to reconstruct a manifold mesh in real time through single core CPU processing, which is also able to modify the mesh according to three-dimensional points updates from the underlying SLAM algorithm. We tested our algorithm against two state-of-the-art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.

Question: What is the state of the art in real-time mesh reconstruction?


A Tandem Algorithm for Singing Pitch Extraction and Voice Separation From Music Accompaniment. Singing pitch estimation and singing voice separation are challenging due to the presence of music accompaniments that are often nonstationary and harmonic. Inspired by computational auditory scene analysis (CASA), this paper investigates a tandem algorithm that estimates the singing pitch and separates the singing voice jointly and iteratively. Rough pitches are first estimated and then used to separate the target singer by considering harmonicity and temporal continuity. The separated singing voice and estimated pitches are used to improve each other iteratively. To enhance the performance of the tandem algorithm for dealing with musical recordings, we propose a trend estimation algorithm to detect the pitch ranges of a singing voice in each time frame. The detected trend substantially reduces the difficulty of singing pitch detection by removing a large number of wrong pitch candidates either produced by musical instruments or the overtones of the singing voice. Systematic evaluation shows that the tandem algorithm outperforms previous systems for pitch extraction and singing voice separation.

Question: How to separate a singing voice from music accompaniments?


Feasibility Study on the Use of Biometrics in an Entitlement Scheme. This report examines the feasibility of using biometrics as a means of establishing a unique identity, to support the proposed entitlement scheme under development by the United Kingdom Passport Service (UKPS) and Driver and Vehicle Licensing Agency (DVLA). Biometrics identification systems measure physiological and behavioural characteristics of a person, and use these measurements to reliably distinguish one person from another. The main examples considered in this study are fingerprint, iris and face image recognition. Biometric identification can assist in the issue of entitlement cards, passports, driving licences and other identity documents in two ways. On the initial issue of such documents, biometrics can be used to check that applicants are not erroneously issued documents using two different identity details; this is the main focus of this study. Secondly, when an entitlement card, passport or driving licence is being used, biometrics can help confirm that it is being used by the correct person. The purpose of the study is to assess the feasibility of fingerprint, iris and face recognition technologies for these applications, to identify unknowns and the risks associated with the use of biometrics in such a national identity scheme, and to make recommendations for how some of these risks might be addressed should such a scheme proceed. Biometric methods do not offer 100% certainty of authentication of individuals. The success of any deployment of a system using biometric methods depends, therefore, on many factors such as: the degree of the 'uniqueness' of the biometric measure, technical and social factors, user interfaces, and provision of secure backup systems for those situations and individual s where the biometric will not work effectively. The main findings of the study are: a In principle, fingerprint or iris recognition can provide the identification performance required for uni

Question: What is the purpose of the study?


Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor. This paper presents an embedded vision system for object tracking applications based on a 128times128 pixel CMOS temporal contrast vision sensor. This imager asynchronously responds to relative illumination intensity changes in the visual scene, exhibiting a usable dynamic range of 120 dB and a latency of under 100 mus. The information is encoded in the form of address-event representation (AER) data. An algorithm for object tracking with 1 millisecond timestamp resolution of the AER data stream is presented. As a real-world application example, vehicle tracking for a traffic-monitoring is demonstrated in real time. The potential of the proposed algorithm for people tracking is also shown. Due to the efficient data pre-processing in the imager chip focal plane, the embedded vision system can be implemented using a low-cost, low-power digital signal processor

Question: What is the potential of the proposed algorithm for people tracking?


Design and Implementation of the LogicBlox System. The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.  In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.

Question: What is the logicblox system?


A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives. BACKGROUND Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.   OBJECTIVE The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.   METHODS From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.   RESULTS Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction i

Question: What is the best model for predicting influenza cases?


Deep Convolutional Neural Network Design Patterns. Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet). Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures. In addition, we describe several architectural innovations, including Fractal of FractalNet network, Stagewise Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt files is available at https://github.com/iPhysicist/CNNDesignPatterns). We hope others are inspired to build on our preliminary work.

Question: What is deep learning?


Modeling and Simulation of Electric and Hybrid Vehicles. This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed

Question: What is the need for modeling and simulation of electric and hybrid vehicles?


Linear codes over Z4+uZ4: MacWilliams identities, projections, and formally self-dual codes. Linear codes are considered over the ring Z 4 + uZ 4 , a non-chain extension of Z 4. Lee weights, Gray maps for these codes are defined and MacWilliams identities for the complete, symmetrized and Lee weight enumer-ators are proved. Two projections from Z 4 + uZ 4 to the rings Z 4 and F 2 + uF 2 are considered and self-dual codes over Z 4 +uZ 4 are studied in connection with these projections. Finally three constructions are given for formally self-dual codes over Z 4 + uZ 4 and their Z 4-images together with some good examples of formally self-dual Z 4-codes obtained through these constructions.

Question: What is a non-chain extension of Z 4?


Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control.. Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.

Question: What is the renin-angiotensin system?


A systematic literature review on Enterprise Architecture Implementation Methodologies. http://dx.doi.org/10.1016/j.infsof.2015.01.012 0950-5849/ 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail addresses: drbabak2@live.utm.my (B.D. Rouhani), mdnazrim@utm.my (M.N. Mahrin), fa.nikpay@siswa.um.edu.my (F. Nikpay), rodina@um.edu.my (R.B. npourya2@live.utm.my (P. Nikfard). Babak Darvish Rouhani a,⇑, Mohd Naz'ri Mahrin , Fatemeh Nikpay , Rodina Binti Ahmad , Pourya Nikfard c

Question: What is a systematic literature review?


An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes. In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques. Appl. Sci. 2015, 5 1135

Question: What is the objective function of the optimization model?


A Hybrid Document Feature Extraction Method Using Latent Dirichlet Allocation and Word2Vec. Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this paper, we propose a hybrid approach to extract features from documents with bag-of-distances in a semantic space. By using both Word2Vec and LDA, our hybrid method not only generates the relationships between documents and topics, but also integrates the contextual relationships among words. Experimental results indicate that document features generated by our hybrid method are useful to improve classification performance by consolidating both global and local relationships.

Question: What is a hybrid approach to extract features from documents?


A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.

Question: What is the difference between a triaxial accelerometer and a uniaxial accelerometer?


How does communication heal? Pathways linking clinician-patient communication to health outcomes.. OBJECTIVE Although prior research indicates that features of clinician-patient communication can predict health outcomes weeks and months after the consultation, the mechanisms accounting for these findings are poorly understood. While talk itself can be therapeutic (e.g., lessening the patient's anxiety, providing comfort), more often clinician-patient communication influences health outcomes via a more indirect route. Proximal outcomes of the interaction include patient understanding, trust, and clinician-patient agreement. These affect intermediate outcomes (e.g., increased adherence, better self-care skills) which, in turn, affect health and well-being. Seven pathways through which communication can lead to better health include increased access to care, greater patient knowledge and shared understanding, higher quality medical decisions, enhanced therapeutic alliances, increased social support, patient agency and empowerment, and better management of emotions.   CONCLUSION Future research should hypothesize pathways connecting communication to health outcomes and select measures specific to that pathway.   PRACTICE IMPLICATIONS Clinicians and patients should maximize the therapeutic effects of communication by explicitly orienting communication to achieve intermediate outcomes (e.g., trust, mutual understanding, adherence, social support, self-efficacy) associated with improved health.

Question: How does communication heal?


Ka-Band Linear to Circular Polarization Converter Based on Multilayer Slab With Broadband Performance. In this paper, a Ka-band polarization converter is presented, which is based on multilayer slab. In order to improve impedance matching, metallic circular traces are printed periodically on each dielectric multilayer slab. Simulated results of the polarizer show that it can transform linearly polarized (LP) to circularly polarized (CP) fields over a frequency band from 23 to 35GHz (42%) with an insertion loss less than 0.5 dB. The transmitted CP wave by the polarizer is approximately robust under oblique illuminations. The polarizer is fabricated and measured by a wideband horn antenna satisfying the simulated results. Next, in order to design a high-gain CP structure around 30 GHz, an 8-element LP array antenna with Chebyshev tapered distribution is designed and integrated with the polarizer. Obviously, the antenna limits the overall bandwidth (nearly 28 to 31.5 GHz) due to the narrowband nature of the LP antenna array. When the polarizer is illuminated by an incident LP wave, the two linear components of the transmitted wave with approximately equal amplitudes and 90° phase difference on the frequency band of interest are produced. Experimental results of the proposed structure show a pure CP with a gain of 13 dBi at 30 GHz, which can be suitable for millimeter wave communication.

Question: What is the Ka-band?


Bayesian Biosurveillance of Disease Outbreaks. Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.

Question: What is a Bayesian network?


Decision Trees for Mining Data Streams Based on the Gaussian Approximation. Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.

Question: What is the best attribute to split the considered node?