Homotopic path validation using non-holonomic differential drive robot model. In this paper the tracking of collision-free path obtained by using Homotopic Path Planning Method (HPPM) with automatic value assignation of repulsion parameter is presented. This work is center on show the utility of this method to obtain a collision free path for maps with narrow corridors on indoor environments. First, the basis of homotopic path planning method with automatic value assignation of repulsion parameter are explained. Second, the Pure-Pursuit controller is brief explained. Third, the capability of the HPPM is shown by using two experiments that which contain narrow passages and several circular obstacles. Finally, in order to validate the solution paths, a tracking on Robot Operating System by using 3P-DX robot model and on MATLAB using a Pure-Pursuit controller from the Robotics Toolbox is performed.

Question: How to validate the solution paths?


A multi-resonant gate driver for Very-High-Frequency (VHF) resonant converters. This paper presents the design and implementation of a Very-High-Frequency (VHF) multi-resonant gate drive circuit. The design procedure is greatly simplified compared with some VHF self-oscillating multi-resonant gate drivers presented in previous works. The proposed circuit has the potential to reduce long start-up time required in a self-oscillating resonant gate drive circuit and to better utilize fast transient capability of VHF converters. A prototype resonant gate driver is demonstrated and able to reduce 60 % of the gate driving loss of a 20 MHz 32 W Class-E power amplifier with Si MOSFET.

Question: What is a multi-resonant gate driver?


Role of culture in gambling and problem gambling.. There has been a significant gap in the gambling literature regarding the role of culture in gambling and problem gambling (PG). This paper aims to this such gap by presenting a systematic review of the cultural variations in gambling and PG as well as a discussion of the role cultural variables can play in the initiation and maintenance of gambling in order to stimulate further research. The review shows that although studies investigating prevalence rates of gambling and PG among different cultures are not plentiful, evidence does suggest certain cultural groups are more vulnerable to begin gambling and to develop PG. Significant factors including familial/genetic, sociological, and individual factors have been found in the Western gambling literature as playing important roles in the development and maintenance of PG. These factors need to be examined now in other cultural groups so we can better understand the etiological processes involved in PG and design culturally sensitive treatments. In addition, variables, such as cultural values and beliefs, the process of acculturation, and the influence of culturally determined, help-seeking behaviors need to be also examined in relation to the role they could play in the initiation of and maintenance of gambling. Understanding the contribution of cultural variables will allow us to devise better prevention and treatment options for PG. Methodological problems in this area of research are highlighted, and suggestions for future research are included.

Question: What is the role of culture in gambling and problem gambling?


Bias and causal associations in observational research. Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.

Question: What is the difference between internal and external validity?


Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation. This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist.  The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.

Question: What is a variational Bayes solution?


Cryptanalytic Attacks on MIFARE Classic Protocol. MIFARE Classic is the most widely used contactless smart card in the world. It implements a proprietary symmetric-key mutual authentication protocol with a dedicated reader and a proprietary stream cipher algorithm known as CRYPTO1, both of which have been reverse engineered. The existing attacks in various scenarios proposed in the literature demonstrate that MIFARE Classic does not offer the desired 48-bit security level. The most practical scenario is the card-only scenario where a fake, emulated reader has a wireless access to a genuine card in the on-line stage of the attack. The most effective known attack in the card-only scenario is a differential attack, which is claimed to require about 10 seconds of average on-line time in order to reconstruct the secret key from the card. This paper presents a critical comprehensive survey of currently known attacks on MIFARE Classic, puts them into the right perspective in light of the prior art in cryptanalysis, and proposes a number of improvements. It is shown that the differential attack is incorrectly analyzed and is optimized accordingly. A new attack of a similar, differential type is also introduced. In comparison with the optimized differential attack, it has a higher success probability of about 0.906 and a more than halved on-line time of about 1.8 seconds.

Question: What is the most widely used contactless smart card in the world?


Deep Gaussian Processes. In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.

Question: What is a deep belief network?


Selected techniques for data mining in medicine. Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.

Question: What is the importance of the interpretability of results of data analysis?


Crisis prevention: how to gear up your board.. Today's critics of corporate boardrooms have plenty of ammunition. The two crucial responsibilities of boards-oversight of long-term company strategy and the selection, evaluation, and compensation of top management--were reduced to damage control during the 1980s. Walter Salmon, a longtime director, notes that while boards have improved since he began serving on them in 1961, they haven't kept pace with the need for real change. Based on over 30 years of boardroom experience, Salmon recommends against government reform of board practices. But he does prescribe a series of incremental changes as a remedy. To begin with, he suggests limiting the size of boards and increasing the number of outside directors on them. In fact, according to Salmon, only three insiders belong on a board: the CEO, the COO, and the CFO. Changing how committees function is also necessary for gearing up today's boards. The audit committee, for example, can periodically review "high-exposure areas" of a business, perhaps helping to prevent embarrassing drops in future profits. Compensation committees can structure incentive compensation for executives to emphasize long-term rather than short-term performance. And nominating committees should be responsible for finding new, independent directors--not the CEO. In general, boards as a whole must spot problems early and blow the whistle, exercising what Salmon calls, "constructive dissatisfaction." On a revitalized board, directors have enough confidence in the process to vigorously challenge one another, including the company's chief executive.

Question: How to gear up your board?


What is the function of the claustrum?. The claustrum is a thin, irregular, sheet-like neuronal structure hidden beneath the inner surface of the neocortex in the general region of the insula. Its function is enigmatic. Its anatomy is quite remarkable in that it receives input from almost all regions of cortex and projects back to almost all regions of cortex. We here briefly summarize what is known about the claustrum, speculate on its possible relationship to the processes that give rise to integrated conscious percepts, propose mechanisms that enable information to travel widely within the claustrum and discuss experiments to address these questions.

Question: What is the function of the claustrum?


Synthetic aperture radar at millimeter wavelength for UAV surveillance applications. The airborne monitoring of scenes using unmanned aircrafts is becoming increasingly important. Several types of airborne sensors - in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the deployment scenarios, often also demand for the ability to look through dust clouds, smoke, and fog. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR). In this paper we focus on miniaturized SAR systems which were developed and optimized for utilization in a UA V (unmanned aerial vehicle) with a low loading capacity. This not only requires a compact and light radar sensor but the processing also has to cope with the unstable flight conditions of a small aircraft. Therefore, a high-precision inertial measurement unit (IMU) and motion compensating SAR-algorithms are needed. Thanks to the utilization of a high transmit frequency of either 35 GHz or 94 GHz, the sensors are suitable for the detection of small-scale objects. A very high resolution of 15 cm × 15 cm can be achieved when using modern FMCW (frequency modulated continuous wave) generation with a high bandwidth (up to 1 GHz) in combination with small antennas.

Question: What is the size of the canadian military?


An Alpha-wave-based binaural beat sound control system using fuzzy logic and autoregressive forecasting model. We are developing a new real-time control system for customizing auditory stimulus (the binaural beat sound) by judging user alpha waves to entrain a userpsilas feeling in the most relaxed way. Since brainwave activity provides the necessary predictive information for arousal states, we use an autoregressive forecasting model to estimate the frequency response series of the alpha frequency bands and the inverted-U concept to determine the userpsilas arousal state. A fuzzy logic controller is also employed to regulate the binaural beat control signal on a forecasting error signal. Our system allows comfortable user self-relaxation. The results of experiments confirm the constructed systempsilas effectiveness and necessity.

Question: How does a binaural beat sound control system work?


Knowledge , Strategy , and the Theory of the Firm Author ( s ) :. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Wiley-Blackwell and John Wiley & Sons are collaborating with JSTOR to digitize, preserve and extend access to Strategic Management Journal. This paper argues that firms have particular institutional capabilities that allow them to protect knowledge from expropriation and imitation more effectively than market contracting. I argue that it is these generalized institutional capabilities that allow firms to generate and protect the unique resources and capabilities that are central to the strategic theory of the firm.

Question: What is the difference between knowledge and strategy?


Tendinous insertion of semimembranosus muscle into the lateral meniscus. Forty-two cadaver knees were used for morphologic and MRI observations of the tendinous distal expansions of the semimembranosus m. and the posterior capsular structures of the knee. A tendinous branch of the semimembranosus m. inserting into the posterior horn of the lateral meniscus was found in 43.2% of the knees dissected, besides five already known insertional branches; capsular, direct, anterior and inferior, as well as the oblique popliteal ligament. The tendon had three morphologic types; thin, broad and round. All three types moved the lateral meniscus posteriorly when pulled on. Thus, the semimembranosus m. may also have a protective function for the lateral meniscus as well as the already well established function of protecting the medial meniscus in knee flexion. When a semimembranosus tendon attachment to the posterior horn of the lateral meniscus is present, its normal insertion is difficult to differentiate from a lateral meniscus tear in MRI and this may cause misdiagnosis.

Question: What is the function of the semimembranosus muscle?


The promising future of microalgae: current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products. Microalgae have recently attracted considerable interest worldwide, due to their extensive application potential in the renewable energy, biopharmaceutical, and nutraceutical industries. Microalgae are renewable, sustainable, and economical sources of biofuels, bioactive medicinal products, and food ingredients. Several microalgae species have been investigated for their potential as value-added products with remarkable pharmacological and biological qualities. As biofuels, they are a perfect substitute to liquid fossil fuels with respect to cost, renewability, and environmental concerns. Microalgae have a significant ability to convert atmospheric CO2 to useful products such as carbohydrates, lipids, and other bioactive metabolites. Although microalgae are feasible sources for bioenergy and biopharmaceuticals in general, some limitations and challenges remain, which must be overcome to upgrade the technology from pilot-phase to industrial level. The most challenging and crucial issues are enhancing microalgae growth rate and product synthesis, dewatering algae culture for biomass production, pretreating biomass, and optimizing the fermentation process in case of algal bioethanol production. The present review describes the advantages of microalgae for the production of biofuels and various bioactive compounds and discusses culturing parameters.

Question: What is the future of microalgae?


Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively1.

Question: What is the state-of-the-art performance of joint intent detection and slot filling?


Temporal Pattern Attention for Multivariate Time Series Forecasting. Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its "frequency domain". Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in all of these with a single exception. Our source code is available at https://github.com/gantheory/TPA-LSTM. * indicates equal contribution. This work was financially supported by the Ministry of Science and Technology of Taiwan. Shun-Yao Shih National Taiwan University E-mail: shunyaoshih@gmail.com Fan-Keng Sun National Taiwan University E-mail: b03901056@ntu.edu.tw Hung-yi Lee National Taiwan University E-mail: hungyilee@ntu.edu.tw ar X iv :1 80 9. 04 20 6v 2 [ cs .L G ] 2 7 N ov 2 01 8 2 Shun-Yao Shih* et al. Fig. 1 Historical prices of crude oil, gasoline, and lumber. Units are omitted and scales are normalized for simplicity.

Question: How to predict the price of crude oil?


Efficient Character Skew Rectification in Scene Text Images. We present an efficient method for character skew rectification in scene text images. The method is based on a novel skew estimators, which exploit intuitive glyph properties and which can be efficiently computed in a linear time. The estimators are evaluated on a synthetically generated data (including Latin, Cyrillic, Greek, Runic scripts) and real scene text images, where the skew rectification by the proposed method improves the accuracy of a state-of-the-art scene text recognition pipeline.

Question: What is the efficiency of the proposed method?


Crime Scene Reconstruction: Online Gold Farming Network Analysis. Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or "real money trading" through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

Question: What is a gold farming group?


Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique. The aim of study is to examine whether social influence affects medical professionals' behavioral intention to use while introducing a new Clinical Decision Support System (CDSS). The series of Technology Acceptance Models (TAMs) have been widely applied to examine new technology acceptance by scholars; nevertheless, these models omit system diversity and the user's profession. On the other hand, causal analysis greatly affects the efficiency of decision-making, and it is usually analyzed by Structural Equation Modeling (SEM); however, the method is often misapplied. This research applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to explore the causal relationship between the significant Unified Theory of Acceptance and Use of Technology (UTAUT) variables. Fuzzy concept is applied to illustrate human vague judgment. It is significant that, in contrary with UTAUT, this study found that social influence does not matter in the behavioral intention to use the CDSS for medical professionals. 2011 Elsevier Ltd. All rights reserved.

Question: What is the Unified Theory of Acceptance and Use of Technology?


Approximation capabilities of multilayer feedforward networks.

Question: What is the approximation capability of a multilayer feedforward network?


Forecasting short-term taxi demand using boosting-GCRF. It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution using historical time-series demand over the study area. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to five other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best modified mean absolute percentage error being 10.4%. The approach is observed to be robust based on its prediction performance on anomaly taxi demand data. In addition, the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.

Question: How to forecast short-term taxi demand using boosting-GCRF?


Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks. Emotions are very important for human intelligence. For example, emotions are closely related to the appraisal of the internal bodily state and external stimuli. This helps us to respond quickly to the environment. Another important perspective in human intelligence is the role of emotions in decision-making. Moreover, the social aspect of emotions is also very important. Therefore, if the mechanism of emotions were elucidated, we could advance toward the essential understanding of our natural intelligence. In this study, a model of emotions is proposed to elucidate the mechanism of emotions through the computational model. Furthermore, from the viewpoint of partner robots, the model of emotions may help us to build robots that can have empathy for humans. To understand and sympathize with people's feelings, the robots need to have their own emotions. This may allow robots to be accepted in human society. The proposed model is implemented using deep neural networks consisting of three modules, which interact with each other. Simulation results reveal that the proposed model exhibits reasonable behavior as the basic mechanism of emotion.

Question: What is the mechanism of emotions?


Embedding new data points for manifold learning via coordinate propagation. In recent years, a series of manifold learning algorithms have been proposed for nonlinear dimensionality reduction. Most of them can run in a batch mode for a set of given data points, but lack a mechanism to deal with new data points. Here we propose an extension approach, i.e., mapping new data points into the previously learned manifold. The core idea of our approach is to propagate the known coordinates to each of the new data points. We first formulate this task as a quadratic programming, and then develop an iterative algorithm for coordinate propagation. Tangent space projection and smooth splines are used to yield an initial coordinate for each new data point, according to their local geometrical relations. Experimental results and applications to camera direction estimation and face pose estimation illustrate the validity of our approach.

Question: How to embed new data points into a manifold?


A flexible architecture integrating monitoring and analytics for managing large-scale data centers. To effectively manage large-scale data centers and utility clouds, operators must understand current system and application behaviors. This requires continuous, real-time monitoring along with on-line analysis of the data captured by the monitoring system, i.e., integrated monitoring and analytics -- Monalytics [28]. A key challenge with such integration is to balance the costs incurred and associated delays, against the benefits attained from identifying and reacting to, in a timely fashion, undesirable or non-performing system states. This paper presents a novel, flexible architecture for Monalytics in which such trade-offs are easily made by dynamically constructing software overlays called Distributed Computation Graphs (DCGs) to implement desired analytics functions. The prototype of Monalytics implementing this flexible architecture is evaluated with motivating use cases in small scale data center experiments, and a series of analytical models is used to understand the above trade-offs at large scales. Results show that the approach provides the flexibility needed to meet the demands of autonomic management at large scale with considerably better performance/cost than traditional and brute force solutions.

Question: What is a data center?


Towards an intelligent textbook: eye gaze based attention extraction on materials for learning and instruction in physics. In this paper, we propose the attention extraction method on a textbook of physics by using eye tracking glasses. We prepare a document including text and tasks in physics, and record reading behavior on the document from 6-grade students. The result confirms that students pay attention to a different region depending on situations (using the text as a material to learn the content, or using it as hints to solve tasks) and their comprehension.

Question: What is the difference between the text and tasks?


Robust techniques for background subtraction in urban traffic video. Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.

Question: What is the difference between a simple and a complicated technique?


A Multi-View Fusion Neural Network for Answer Selection. Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications. Previous neural network-based methods consider several different aspects of information through calculating attentions. These different kinds of attentions are always simply summed up and can be seen as a "single view", causing severe information loss. To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a "view" of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation. In this fusion RNN method, a filter gate collects important information of input and directly adds it to the output, which borrows the idea of residual networks. Experimental results on the WikiQA and SemEval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods.

Question: What is the difference between a neural network and a multi-view fusion neural network?


Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts. Social commerce has taken the e‐tailing world by storm. Business‐to‐consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among like‐ minded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (eWOM) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e‐books, which are digital mi‐ croproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via eWOM becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that eWOM can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, eWOM technolo‐ gies should be considered by both e‐tailers and shoppers as the first and perhaps primary source of social buying experience.

Question: What is the effect of electronic word of mouth on sales of digital microproducts?


Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.. What chiefly distinguishes cerebral cortex from other parts of the central nervous system is the great diversity of its cell types and inter-connexions. It would be astonishing if such a structure did not profoundly modify the response patterns of fibres coming into it. In the cat's visual cortex, the receptive field arrangements of single cells suggest that there is indeed a degree of complexity far exceeding anything yet seen at lower levels in the visual system. In a previous paper we described receptive fields of single cortical cells, observing responses to spots of light shone on one or both retinas (Hubel & Wiesel, 1959). In the present work this method is used to examine receptive fields of a more complex type (Part I) and to make additional observations on binocular interaction (Part II). This approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours. In the past, the technique of recording evoked slow waves has been used with great success in studies of functional anatomy. It was employed by Talbot & Marshall (1941) and by Thompson, Woolsey & Talbot (1950) for mapping out the visual cortex in the rabbit, cat, and monkey. Daniel & Whitteiidge (1959) have recently extended this work in the primate. Most of our present knowledge of retinotopic projections, binocular overlap, and the second visual area is based on these investigations. Yet the method of evoked potentials is valuable mainly for detecting behaviour common to large populations of neighbouring cells; it cannot differentiate functionally between areas of cortex smaller than about 1 mm2. To overcome this difficulty a method has in recent years been developed for studying cells separately or in small groups during long micro-electrode penetrations through nervous tissue. Responses are correlated w

Question: What is the difference between receptive fields and receptive fields?


A 3D object detection and pose estimation pipeline using RGB-D images. 3D object detection and pose estimation has been studied extensively in recent decades for its potential applications in robotics. However, there still remains challenges when we aim at detecting multiple objects while retaining low false positive rate in cluttered environments. This paper proposes a robust 3D object detection and pose estimation pipeline based on RGB-D images, which can detect multiple objects simultaneously while reducing false positives. Detection begins with template matching and yields a set of template matches. A clustering algorithm then groups templates of similar spatial location and produces multiple-object hypotheses. A scoring function evaluates the hypotheses using their associated templates and non-maximum suppression is adopted to remove duplicate results based on the scores. Finally, a combination of point cloud processing algorithms are used to compute objects' 3D poses. Existing object hypotheses are verified by computing the overlap between model and scene points. Experiments demonstrate that our approach provides competitive results comparable to the state-of-the-arts and can be applied to robot random bin-picking.

Question: What is the difference between a robot and a robot?


Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users. The goal in designing an ionospheric time-delay correctionalgorithm for the single-frequency global positioning system userwas to include the main features of the complex behavior of theionosphere, yet require a minimum of coefficients and usercomputational time, while still yielding an rms correction of at least50 percent. The algorithm designed for this purpose, andimplemented in the GPS satellites, requires only eight coefficientssent as part of the satellite message, contains numerousapproximations designed to reduce user computationalrequirements, yet preserves the essential elements required to obtaingroup delay values along multiple satellite viewing directions.

Question: What is the ionosphere?


Behavior of Machine Learning Algorithms in Adversarial Environments. Behavior of Machine Learning Algorithms in Adversarial Environments

Question: How to deal with adversarial environments?


The Iron Cage Revisited : Institutional Isomorphism and Collective Rationality in Organizational Fields Author ( s ) :. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to use JSTOR?


NASARI: a Novel Approach to a Semantically-Aware Representation of Items. The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.

Question: What is the semantic representation of items?


VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations. Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/.

Question: What is the size of the canadian military?


Approximate Arc Length Parametrization. Current approaches to compute the arc length of a parametric curve rely on table lookup schemes. We present an approximate closed-form solution to the problem of computing an arc length parametrization for any given parametric curve. Our solution outputs a one or two-span Bézier curve which relates the length of the curve to the parametric variable. The main advantage of our approach is that we obtain a simple continuous function relating the length of the curve and the parametric variable. This allows the length to be easily computed given the parametric values. Tests with our algorithm on several thousand curves show that the maximum error in our approximation is 8.7% and that the average of maximum errors is 1.9%. Our algorithm is fast enough to compute the closed-form solution in a fraction of a second. After that a user can interactively get an approximation of the arc length for an arbitrary parameter value.

Question: How to compute the arc length of a parametric curve?


Representation Learning of Knowledge Graphs with Entity Descriptions. Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous lowdimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.

Question: What is the source code of this paper?


Multi-session PLDA scoring of i-vector for partially open-set speaker detection. This paper advocates the use of probabilistic linear discriminant analysis (PLDA) for partially open-set detection task with multiple i-vectors enrollment condition. Also referred to as speaker verification, the speaker detection task has always been considered under an open-set scenario. In this paper, a more general partially open-set speaker detection problem in considered, where the imposters might be one of the known speakers previously enrolled to the system. We show how this could be coped with by modifying the definition of the alternative hypothesis in the PLDA scoring function. We also look into the impact of the conditionalindependent assumption as it was used to derive the PLDA scoring function with multiple training i-vectors. Experiments were conducted using the NIST 2012 Speaker Recognition Evaluation (SRE'12) datasets to validate various points discussed in the paper.

Question: How to score a partially open-set speaker detection system?


A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA. ses a facial expression recognition system based on Gabor feature using a novel r bank. Traditionally, a global Gabor filter bank with 5 frequencies and 8 ten used to extract the Gabor feature. A lot of time will be involved to extract mensions of such Gabor feature vector are prohibitively high. A novel local Gabor art of frequency and orientation parameters is proposed. In order to evaluate the he local Gabor filter bank, we first employed a two-stage feature compression LDA to select and compress the Gabor feature, then adopted minimum distance nize facial expression. Experimental results show that the method is effective for eduction and good recognition performance in comparison with traditional entire . The best average recognition rate achieves 97.33% for JAFFE facial expression abor filter bank, feature extraction, PCA, LDA, facial expression recognition. deliver rich information about human emotion and play an essential role in human In order to facilitate a more intelligent and natural human machine interface of new cts, automatic facial expression recognition [1][18][20] had been studied world en years, which has become a very active research area in computer vision and n. There are many approaches have been proposed for facial expression analysis ages and image sequences [12][18] in the literature. we focus on the recognition of facial expression from single digital images with feature extraction. A number of approaches have been developed for extracting by: Motorola Labs Research Foundation (No.303D804372), NSFC (No.60275005), GDNSF 105938).

Question: What is the difference between the traditional global Gabor filter bank and the novel local Gabor filter bank?


Anisotropic Ambient Volume Shading. We present a novel method to compute anisotropic shading for direct volume rendering to improve the perception of the orientation and shape of surface-like structures. We determine the scale-aware anisotropy of a shading point by analyzing its ambient region. We sample adjacent points with similar scalar values to perform a principal component analysis by computing the eigenvectors and eigenvalues of the covariance matrix. In particular, we estimate the tangent directions, which serve as the tangent frame for anisotropic bidirectional reflectance distribution functions. Moreover, we exploit the ratio of the eigenvalues to measure the magnitude of the anisotropy at each shading point. Altogether, this allows us to model a data-driven, smooth transition from isotropic to strongly anisotropic volume shading. In this way, the shape of volumetric features can be enhanced significantly by aligning specular highlights along the principal direction of anisotropy. Our algorithm is independent of the transfer function, which allows us to compute all shading parameters once and store them with the data set. We integrated our method in a GPU-based volume renderer, which offers interactive control of the transfer function, light source positions, and viewpoint. Our results demonstrate the benefit of anisotropic shading for visualization to achieve data-driven local illumination for improved perception compared to isotropic shading.

Question: How to compute anisotropic shading?


A Temporal Logic of Nested Calls and Returns. Model checking of linear temporal logic (LTL) specifications with respect to pushdown systems has been shown to be a useful tool for analysis of programs with potentially recursive procedures. LTL, however, can specify only regular properties, and properties such as correctness of procedures with respect to pre and post conditions, that require matching of calls and returns, are not regular. We introduce a temporal logic of calls and returns (CARET) for specification and algorithmic verification of correctness requirements of structured programs. The formulas of CARET are interpreted over sequences of propositional valuations tagged with special symbols call and ret. Besides the standard global temporal modalities, CARET admits the abstract-next operator that allows a path to jump from a call to the matching return. This operator can be used to specify a variety of non-regular properties such as partial and total correctness of program blocks with respect to pre and post conditions. The abstract versions of the other temporal modalities can be used to specify regular properties of local paths within a procedure that skip over calls to other procedures. CARET also admits the caller modality that jumps to the most recent pending call, and such caller modalities allow specification of a variety of security properties that involve inspection of the call-stack. Even though verifying context-free properties of pushdown systems is undecidable, we show that model checking CARET formulas against a pushdown model is decidable. We present a tableau construction that reduces our model checking problem to the emptiness problem for a Büchi pushdown system. The complexity of model checking CARET formulas is the same as that of checking LTL formulas, namely, polynomial in the model and singly exponential in the size of the specification. Comments From the 10th International Conference, TACAS 2004, Held as Part of the Joint European Conf

Question: What is the complexity of model checking CARET formulas?


Wireless powering for a self-propelled and steerable endoscopic capsule for stomach inspection.. This paper describes the integration of an active locomotion module in a wirelessly powered endoscopic capsule. The device is a submersible capsule optimized to operate in a fluid environment in a liquid-distended stomach. A 3D inductive link is used to supply up to 400mW to the embedded electronics and a set of 4 radio-controlled motor propellers. The design takes advantage of a ferrite-core in the receiving coil-set. This approach significantly improves the coupling with the external field source with respect to earlier work by the group. It doubles the power that can be received with a coreless coil-set under identical external conditions. The upper limit of the received power was achieved complying with the strict regulations for safe exposure of biological tissue to variable magnetic fields. The wireless transferred power was proven to be sufficient to achieve the speed of 7cm/s in any directions. An optimized locomotion strategy was defined which limits the power consumption by running only 2 motors at a time. A user interface and a joystick controller allow to fully drive the capsule in an intuitive manner. The device functionalities were successfully tested in a dry and a wet environment in a laboratory set-up.

Question: How does the capsule work?


The plastic surgery hypothesis. Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalize and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43% graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched.

Question: What is the plastic surgery hypothesis?


Solving dynamic vehicle routing problem via evolutionary search with learning capability. To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.

Question: Solving dynamic vehicle routing problem via evolutionary search with learning capability.


A DTN-Based Sensor Data Gathering for Agricultural Applications. This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.

Question: What is the size of the software?


The Body Appreciation Scale: development and psychometric evaluation.. Body image has been conceptualized and assessed almost exclusively in terms of its negative dimensions. Therefore, a measure reflecting body appreciation, an aspect of positive body image, was developed and evaluated via four independent samples of college women. Study 1 (N = 181) supported the Body Appreciation Scale's (BAS) unidimensionality and construct validity, as it was related as expected to body esteem, body surveillance, body shame, and psychological well-being. Study 2 (N = 327) cross-validated its unidimensionality. Study 3 (N = 424) further upheld the construct validity of the BAS, as it was: (a) related as expected to appearance evaluation, body preoccupation, body dissatisfaction, and eating disorder symptomatology and (b) unrelated to impression management. Studies 1 and 3 also indicated that the BAS predicted unique variance in psychological well-being above and beyond extant measures of body image. Study 4 (N = 177) demonstrated that its scores were stable over a 3-week period. All studies supported the internal consistency reliability of its scores. The BAS should prove useful for researchers and clinicians interested in positive body image assessment.

Question: What is the body appreciation scale?


Executive control in language processing. During communication, speakers and listeners need the mechanisms of executive control to organize thoughts and actions along internal goals. Speakers may use executive functions to select the right word over competing alternatives to refer to the concept in mind. Listeners may use executive functions to coordinate the outputs of multiple linguistic processes to reach a coherent interpretation of what others say. Bilinguals may use executive functions to control which language is to use or to switch from one language to another. The control mechanisms recruited in language processing may be similar to those recruited in perception and attention, supported by a network of frontal, parietal and sub-cortical brain structures. Here we review existing evidences regarding the involvement of domain-general executive control in language processing. We will explain how executive functions are employed to control interference in comprehension and production, within and across languages.

Question: What is the role of executive control in language processing?


Sarcasm detection of tweets: A comparative study. Sarcasm is a nuanced form of communication where the individual states opposite of what is implied. One of the major challenges of sarcasm detection is its ambiguous nature. There is no prescribed definition of sarcasm. Another major challenge is the growing size of the languages. Every day hundreds of new slang words are being created and used on these sites. Hence, the existing corpus of positive and negative sentiments may not prove to be accurate in detecting sarcasm. Also, the recent developments in online social networks allow its users to use varied kind of emoticons with the text. These emoticons may change the polarity of the text and make it sarcastic. Due to these difficulties and the inherently tricky nature of sarcasm it is generally ignored during social network analysis. As a result the results of such analysis are affected adversely. Thus, sarcasm detection poses to be one of the most critical problems which we need to overcome. Detection of sarcastic content is vital to various NLP based systems such as text summarization and sentiment analysis. In this paper we address the problem of sarcasm detection by leveraging the most common expression of sarcasm — "positive sentiment attached to a negative situation". Our work uses two ensemble based approaches — voted ensemble classifier and random forest classifier. Unlike current approaches to sarcasm detection which rely on existing corpus of positive and negative sentiments for training the classifiers, we use a seeding algorithm to generate training corpus. The proposed model also uses a pragmatic classifier to detect emoticon based sarcasm.

Question: What is sarcasm?


Data-Driven Modeling for Chinese Ancient Architecture. The existing 3D modeling studies of Chinese ancient architecture are mostly procedure driven and rely on fixed construction rules. Therefore, these methods have limited applications in virtual reality (VR) engineering. We propose a data-driven approach to synthesize 3D models from existing 3D data that provides more flexibility and fills the gap between academic studies and VR engineering. First, 3D architecture models were preprocessed and decomposed into components, and the components were clustered by their geometric features. Second, a Bayesian network was generated by learning from the dataset to represent the internal relationships between the architectural components. Third, the inference results of the trained network were utilized to generate a reasonable relationship matching to support the synthesis of the structural components. The proposed method can be used in 3D content creation for VR development and directly supports VR applications in practice.

Question: How to generate a 3D model from a 2D image?


On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks. Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.

Question: What is the emulated relay attack test-bed?


A Multiband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE Applications. A multiband dual-polarized omnidirectional antenna for 2G/3G/Long Term Evolution (LTE) mobile communications is proposed in this letter, which consists of horizontal polarization (HP) and vertical polarization (VP) element with separate feeds. The VP element consists of three polygonal radiation patches with three equally spaced legs shorted to the ground plane. The HP element consists of three wideband slot loop structures, which is nested on the top of the VP element. Three slot loop structures provide a 360° coverage for HP and enhance its bandwidth. Both the simulated and measured results indicate that the frequency bands of 1650–2900 MHz for HP and 780–2700 MHz for VP can be achieved. The reflection of VP improves the gain of the HP element at least 1 dBi after nesting. The gain of HP element is more than 3 dBi for LTE, and the gain of VP element is more than 5 dBi in the LTE band and 1.5 dBi in the 2G band. Port isolation larger than 30 dB and low-gain variation levels are also obtained. The proposed antenna can be applied in mobile communications.

Question: What is the frequency band of the proposed antenna?


Photogrammetric Multi-View Stereo and Imaging Network Design.

Question: What is the difference between photogrammetric multi-view stereo and imaging network design?


FACTORS INFLUENCING CITIZENS ' ADOPTION OF AN E-GOVERNMENT SYSTEM : VALIDATION OF THE DECOMPOSED THEORY OF PLANNED BEHAVIOR ( DTPB ). The study explores the adoption of an electronic government (e-government) system called electronic district (e-District) system in context of India. The study validates the decomposed theory of planned behaviour (DTPB) to understand the impact of its factors on the potential adopter's intention to adopt this system. The proposed research model is validated using the data collected from 304 respondents from a state called Bihar of India where this e-government was supposed to be implemented. The data is analysed through regression analysis technique. The empirical findings of the proposed research model indicated the significant relationships of all proposed hypotheses. The study also provides its limitations, future research directions, and implications for theory and practice toward the end.

Question: What is the impact of the factors on the potential adopter's intention to adopt this system?


Examining a hate speech corpus for hate speech detection and popularity prediction. As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.

Question: What is hate speech?


Honeypots : The Need of Network Security. Network forensics is basically used to detect attackers activity and to analyze their behavior. Data collection is the major task of network forensics and honeypots are used in network forensics to collect useful data. Honeypot is an exciting new technology with enormous potential for security communities. This review paper is based upon the introduction to honeypots, their importance in network security, types of honeypots, their advantages disadvantages and legal issues related with them. Research Paper also discuss about the shortcomings of intrusion detection system in a network security and how honeypots improve the security architecture of the organizational network. Furthermore, paper reveals about the different kind of honeypots their level of interaction and risks associated with them. Keywords—honeyd; honeypots; nmap; network forensics

Question: What is the need of honeypots in network security?


A theory of reasoned action: some applications and implications..

Question: What is the theory of reasoned action?


Snakes and Strings: New Robotic Components for Rescue Operations. The Japanese government is establishing an International Rescue Complex to promote research and development of key technologies for realization of practical search-and-rescue robots, anticipating for future large-scale earthquakes and other catastrophic disasters. This paper proposes a new paradigm called "snakes and strings", for developing practical mobile robot systems that may be useful in such situations. "Snakes" stands for snake-like robots, which can skillfully move among the debris of the collapsed buildings. "Strings", on the other hand, means robotic systems using strings or tethers, such as proposed in the "hyper-tether" research [9]. Theters can continuously supply energy, accomplish reliable communication link, and also exhibit high traction force. This paper will present many new mechanical implementations of snake-like robots developed in our lab., and also explain in detail the new paradigm.

Question: What is the new paradigm called?


Robust Keyframe-based Dense SLAM with an RGB-D Camera. In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g. fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.

Question: Robust keyframe-based dense SLAM with an RGB-D camera


Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks. Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks. Previous modelbased and deep learning approaches achieve satisfactory performance for 2D face spoofs, but remain limited for more advanced 3D attacks such as vivid masks. In this paper, we address 3D face anti-spoofing via the proposed Hypergraph Convolutional Neural Networks (HGCNN). Firstly, we construct a computation-efficient and posture-invariant face representation with only a few key points on hypergraphs. The hypergraph representation is then fed into the designed HGCNN with hypergraph convolution for feature extraction, while the depth auxiliary is also exploited for 3D mask anti-spoofing. Further, we build a 3D face attack database with color, depth and infrared light information to overcome the deficiency of 3D face anti-spoofing data. Experiments show that our method achieves the state-of-theart performance over widely used 3D and 2D databases as well as the proposed one under various tests.

Question: How to construct a hypergraph representation for face anti-spoofing?


Interactive Block Games for Assessing Children's Cognitive Skills: Design and Preliminary Evaluation. Background: This paper presents design and results from preliminary evaluation of Tangible Geometric Games (TAG-Games) for cognitive assessment in young children. The TAG-Games technology employs a set of sensor-integrated cube blocks, called SIG-Blocks, and graphical user interfaces for test administration and real-time performance monitoring. TAG-Games were administered to children from 4 to 8 years of age for evaluating preliminary efficacy of this new technology-based approach. Methods: Five different sets of SIG-Blocks comprised of geometric shapes, segmented human faces, segmented animal faces, emoticons, and colors, were used for three types of TAG-Games, including Assembly, Shape Matching, and Sequence Memory. Computational task difficulty measures were defined for each game and used to generate items with varying difficulty. For preliminary evaluation, TAG-Games were tested on 40 children. To explore the clinical utility of the information assessed by TAG-Games, three subtests of the age-appropriate Wechsler tests (i.e., Block Design, Matrix Reasoning, and Picture Concept) were also administered. Results: Internal consistency of TAG-Games was evaluated by the split-half reliability test. Weak to moderate correlations between Assembly and Block Design, Shape Matching and Matrix Reasoning, and Sequence Memory and Picture Concept were found. The computational measure of task complexity for each TAG-Game showed a significant correlation with participants' performance. In addition, age-correlations on TAG-Game scores were found, implying its potential use for assessing children's cognitive skills autonomously.

Question: What is the difference between a block game and a puzzle game?


Improving literacy in developing countries using speech recognition-supported games on mobile devices. Learning to read in a second language is challenging, but highly rewarding. For low-income children in developing countries, this task can be significantly more challenging because of lack of access to high-quality schooling, but can potentially improve economic prospects at the same time. A synthesis of research findings suggests that practicing recalling and vocalizing words for expressing an intended meaning could improve word reading skills - including reading in a second language - more than silent recognition of what the given words mean. Unfortunately, many language learning software do not support this instructional approach, owing to the technical challenges of incorporating speech recognition support to check that the learner is vocalizing the correct word. In this paper, we present results from a usability test and two subsequent experiments that explore the use of two speech recognition-enabled mobile games to help rural children in India read words with understanding. Through a working speech recognition prototype, we discuss two major contributions of this work: first, we give empirical evidence that shows the extent to which productive training (i.e. vocalizing words) is superior to receptive vocabulary training, and discuss the use of scaffolding hints to ""unpack"" factors in the learner's linguistic knowledge that may impact reading. Second, we discuss what our results suggest for future research in HCI.

Question: How can we improve literacy in developing countries?


Real User Evaluation of Spoken Dialogue Systems Using Amazon Mechanical Turk. This paper describes a framework for evaluation of spoken dialogue systems. Typically, evaluation of dialogue systems is performed in a controlled test environment with carefully selected and instructed users. However, this approach is very demanding. An alternative is to recruit a large group of users who evaluate the dialogue systems in a remote setting under virtually no supervision. Crowdsourcing technology, for example Amazon Mechanical Turk (AMT), provides an efficient way of recruiting subjects. This paper describes an evaluation framework for spoken dialogue systems using AMT users and compares the obtained results with a recent trial in which the systems were tested by locally recruited users. The results suggest that the use of crowdsourcing technology is feasible and it can provide reliable results.

Question: How to evaluate a spoken dialogue system?


Ontology and Information Systems. Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. 'Ontology' is often used by philosophers as a synonym for 'metaphysics' (literally: 'what comes after the Physics'), a term which was used by early students of Aristotle to refer to what Aristotle himself called 'first philosophy'. The term 'ontology' (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey's dictionary of 1721, which defines ontology as 'an Account of being in the Abstract'.

Question: What is ontology?


A comparative analysis of common YouTube comment spam filtering techniques. Ever since its development in 2005, YouTube has been providing a vital social media platform for video sharing. Unfortunately, YouTube users may have malicious intentions, such as disseminating malware and profanity. One way to do so is using the comment field for this purpose. Although YouTube provides a built-in tool for spam control, yet it is insufficient for combating malicious and spam contents within the comments. In this paper, a comparative study of the common filtering techniques used for YouTube comment spam is conducted. The study deploys datasets extracted from YouTube using its Data API. According to the obtained results, high filtering accuracy (more than 98%) can be achieved with low-complexity algorithms, implying the possibility of developing a suitable browser extension to alleviate comment spam on YouTube in future.

Question: What is a comparative analysis of common YouTube comment spam filtering techniques?


Persistence Diagrams with Linear Machine Learning Models. Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features of persistence diagrams is significantly important for practical applications. In this paper, we propose a unified method for the inverse analysis by combining linear machine learning models with persistence images. The method is applied to point clouds and cubical sets, showing the ability of the statistical inverse analysis and its advantages. This work is partially supported by JSPS KAKENHI Grant Number JP 16K17638, JST CREST Mathematics15656429, JST "Materials research by Information Integration" Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, Structural Materials for Innovation Strategic Innovation Promotion Program D72, and New Energy and Industrial Technology Development Organization (NEDO). Ippei Obayashi Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan Tel.: +81-22-217-6320 Fax: +81-22-217-5129 E-mail: ippei.obayashi.d8@tohoku.ac.jp Y. Hiraoka Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. Center for Materials research by Information Integration (CMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS). E-mail: hiraoka@tohoku.ac.jp

Question: What is the difference between persistence diagrams and persistence images?


A spectrogram-based audio fingerprinting system for content-based copy detection. This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.

Question: What is a spectrogram?


On-line Trend Analysis with Topic Models: \#twitter Trends Detection Topic Model Online. We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.

Question: How to detect trending topics in twitter?


Designing Twister 2 : Efficient Programming Environment Toolkit for Big Data. Data-driven applications are required to adapt to the everincreasing volume, velocity and veracity of data generated by a variety of sources including the Web and Internet of Things devices. At the same time, an event-driven computational paradigm is emerging as the core of modern systems designed for both database queries, data analytics and on-demand applications. MapReduce has been generalized to Map Collective and shown to be very effective in machine learning. However one often uses a dataflow computing model, which has been adopted by most major big data processing runtimes. The HPC community has also developed several asynchronous many tasks (AMT) systems according to the dataflow model. From a different point of view, the services community is moving to an increasingly event-driven model where (micro)services are composed of small functions driven by events in the form of Function as a Service(Faas) and serverless computing. Such designs allow the applications to scale quickly as well as be cost effective in cloud environments. An event-driven runtime designed for data processing consists of well-understood components such as communication, scheduling, and fault tolerance. One can make different design decisions for these components that will determine the type of applications a system can support efficiently. We find that modern systems are designed in a monolithic approach with a fixed set of choices that cannot be changed easily afterwards. Because of these design choices their functionality is limited to specific sets of applications. In this paper we study existing systems (candidate event-driven runtimes), the design choices they have made for each component, and how this affects the type of applications they can support. Further we propose a loosely coupled component-based approach for designing a big data toolkit where each component can have different implementations to su

Question: What is the difference between a monolithic and a loosely coupled approach?


Smart Health Care: An Edge-Side Computing Perspective. Increasing health awareness and rapid technological advancements have resulted in significant growth in an emerging sector: smart health care. By the end of 2020, the total number of smart health-care devices is expected to reach 808.9 million (646 million devices without wearables and the remaining with wearables) [1], [2]. This proliferation of devices is expected to revolutionize health care by speeding up treatment and diagnostic processes, decreasing physician visit costs, and enhancing patient care quality.

Question: What is the total number of smart health-care devices?


The immediate effect of kinesiology taping on muscular imbalance in the lateral flexors of the neck in infants: a randomized masked study.. OBJECTIVE To investigate the immediate effect of kinesiology taping (KT) on muscular imbalance in the lateral flexors of the neck.   DESIGN Randomized controlled trial.   PARTICIPANTS Twenty-nine infants with congenital muscular torticollis and muscular imbalance in the lateral flexors of the neck were chosen consecutively. In addition, 5 healthy infants with no signs of muscular imbalance in the neck were tested.   METHOD The infants were randomly allocated to either an intervention group or a control group. The intervention group had kinesiology taping applied on the affected side using the muscle-relaxing technique. The healthy infants were tested both with and without kinesiology taping. The evaluator was blinded to whether the infants were or were not taped.   RESULTS There was a significant difference in the change of Muscle Function Scale (MFS) scores between the groups (P < .0001). In the intervention group, there were significantly lower scores on the affected side that had been taped (P < .0001) and also significantly higher scores on the unaffected side (P = .01). There were no significant differences in the control group. For the healthy infants, with no imbalance in the lateral flexors of the neck, there were no changes to the MFS scores regardless of whether the kinesiology tape was applied.   CONCLUSIONS For infants with congenital muscular torticollis, kinesiology taping applied on the affected side had an immediate effect on the MFS scores for the muscular imbalance in the lateral flexors of the neck.

Question: What is the immediate effect of kinesiology taping on muscular imbalance in the lateral flexors of the neck in infants?


Airport Security Screener Competency : A Cross-Sectional and Longitudinal Analysis. The performance of 5,717 aviation security screeners in detecting prohibited items in x-ray images of passenger bags was analyzed over a period of 4 years. The measure used in this study was detection performance on the X-Ray Competency Assessment Test (X-Ray CAT) and performance on this measure was obtained on an annual basis. Between tests, screeners performed varying amounts of training in the task of prohibited item detection using an adaptive computer-based training system called X-Ray Tutor (XRT). For both XRT and X-Ray CAT, prohibited items are categorized into guns, knives, improvised explosive devices (IEDs), and other prohibited items. Cross-sectional and longitudinal analyses of the training and test data were performed. Both types of analysis revealed a strong improvement of X-Ray CAT performance as a result of XRT training for all 4 item categories. The results of the study indicate that screener competency in detecting prohibited items in x-ray images of passenger bags can be greatly improved through routine XRT training.

Question: What is the difference between a cross-sectional and a longitudinal analysis?


Decision Forests, Convolutional Networks and the Models in-Between. This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters.

Question: What is the difference between a decision forest and a convolutional neural network?


Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification. The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with LogEuclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the matrix logarithms from the original tangent space to a new tangent space of more discriminability. Under the tangent map framework, the novel metric learning can then be formulated as an optimization problem of seeking a Mahalanobis-like matrix, which can take the advantage of traditional metric learning techniques. Extensive evaluations on several image set classification tasks demonstrate the effectiveness of our proposed metric learning method.

Question: What is the logarithm of a symmetric positive definite matrix?


HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private "clouds". At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.

Question: What is the difference between a parallel database and a mapreduce database?


Recursive circle packing problems. This paper presents a class of packing problems where circles may be placed either inside or outside other circles, the whole set being packed in a rectangle. This corresponds to a practical problem of packing tubes in a container; before being inserted in the container, tubes may be put inside other tubes in a recursive fashion. A variant of the greedy randomized adaptive search procedure is proposed for tackling this problem, and its performance assessed in a set of benchmark instances.

Question: What is a recursive circle packing problem?


Ultra-wide bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communications. Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.

Question: What is the transmission rate of a time-hopping spread-spectrum multiple-access communications system?


Equation of State Calculations by Fast Computing Machines. 1087 instead, only water molecules with different amounts of excitation energy. These may follow any of three paths: (a) The excitation energy is lost without dissociation into radicals (by collision, or possibly radiation, as in aromatic hydrocarbons). (b) The molecules dissociate, but the resulting radicals recombine without escaping from the liquid cage. (c) The molecules dissociate and escape from the cage. In this case we would not expect them to move more than a few molecular diameters through the dense medium before being thermalized. paths (a) and (b) can be designated H 2 0* and those following path (c) can be designated H 2 0t. It seems reasonable to assume for the purpose of these calculations that the ionized H 2 0 molecules will become the H 20 t molecules, but this is not likely to be a complete correspondence. In conclusion we would like to emphasize that the qualitative result of this section is not critically dependent on the exact values of the physical parameters used. However, this treatment is classical, and a correct treatment must be wave mechanical; therefore the result of this section cannot be taken as an a priori theoretical prediction. The success of the radical diffusion model given above lends some plausibility to the occurrence of electron capture as described by this crude calculation. Further work is clearly needed. A general method, suitable for fast computing machines, for investigatiflg such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two-dimensional rigid-sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four-term virial coefficient expansion.

Question: What is the equation of state of water?


NetSTAT: A Network-Based Intrusion Detection Approach. Network-based attacks have become common and sophisticated. For this reason, intrusion detection systems are now shifting their focus from the hosts and their operating systems to the network itself. Network-based intrusion detection is challenging because network auditing produces large amounts of data, and different events related to a single intrusion may be visible in different places on the network. This paper presents NetSTAT, a new approach to network intrusion detection. By using a formal model of both the network and the attacks, NetSTAT is able to determine which network events have to be monitored and where they can be monitored.

Question: What is a formal model of the network?


Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers. An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.

Question: What is the contactless dc connector?


Modeling, parameter identification and model-based control of a lightweight robotic manipulator. Nowadays many robotic tasks require close and compliant tracking of desired positions, paths, or forces. To achieve these goals, model-based control schemes provide a possible solution, allowing to directly consider the nonlinear dynamics. One of the key challenges, however, is the derivation of suitable models, which allow sufficiently fast evaluation, as well as the parameterization of these models based on measurements. In this work we outline and review a structured approach for model-based controller design for robots. In a first step we derive suitable models for multi-link robots. In a second step we review how such models can be parameterized and how optimal identification experiments can be designed. Based on the model we then derive a simple model based controller to experimentally validate the results considering a lightweight robot. The single steps of the derivation and controller design are supported by a newly developed freely available model toolbox for the considered lightweight robot.

Question: How to derive a model for a robot?


Descriptive versus interpretive phenomenology: their contributions to nursing knowledge.. A number of articles in the nursing literature discuss the differences between descriptive and interpretive approaches to doing phenomenology. A review of studies demonstrates, however, that many researchers do not articulate which approach guides the study, nor do they identify the philosophical assumptions on which the study is based. Such lack of clarity makes it difficult for the reader to obtain a sense of how the knowledge produced by the study is to be evaluated and used. In this article, the authors compare the philosophical components of descriptive and interpretive approaches to doing phenomenology and illustrate types of knowledge produced by each through reviewing specific studies. They focus on the various uses of phenomenology in generating useful knowledge for health care practice.

Question: What is the difference between descriptive and interpretive phenomenology?


The knowledge pyramid: a critique of the DIKW hierarchy. The paper evaluates the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon of information science and management. The paper considers whether the hierarchy, also known as the 'Knowledge Hierarchy', is a useful and intellectually desirable construct to introduce, whether the views expressed about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as 'weak knowledge', that knowledge also is 'weak knowledge' and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.

Question: What is the DIKW hierarchy?


A survey of graphical languages for monoidal categories. This article is intended as a reference guide to various notions of monoidal categories and their associated string diagrams. It is hoped that this will be useful not just to mathematicians, but also to physicists, computer scientists, and others who use diagrammatic reasoning. We have opted for a somewhat informal treatment of topological notions, and have omitted most proofs. Nevertheless, the exposition is sufficiently detailed to make it clear what is presently known, and to serve as a starting place for more in-depth study. Where possible, we provide pointers to more rigorous treatments in the literature. Where we include results that have only been proved in special cases, we indicate this in the form of caveats.

Question: What is a monoidal category?


The TaSSt: Tactile sleeve for social touch. In this paper we outline the design process of the TaSST (Tactile Sleeve for Social Touch), a touch-sensitive vibrotactile arm sleeve. The TaSST was designed to enable two people to communicate different types of touch over a distance. The touch-sensitive surface of the sleeve consists of a grid of 4×3 sensor compartments filled with conductive wool. Each compartment controls the vibration intensity of a vibration motor, located in a grid of 4×3 motors beneath the touch-sensitive layer. An initial evaluation of the TaSST revealed that it was mainly suitable for communicating protracted (e.g. pressing), and simple (e.g. poking) touches.

Question: How does the TaSSt work?