Cluster-based Web Summarization. We propose a novel approach to abstractive Web summarization based on the observation that summaries for similar URLs tend to be similar in both content and structure. We leverage existing URL clusters and construct per-cluster word graphs that combine known summaries while abstracting out URL-specific attributes. The resulting topology, conditioned on URL features, allows us to cast the summarization problem as a structured learning task using a lowest cost path search as the decoding step. Early experimental results on a large number of URL clusters show that this approach is able to outperform previously proposed Web summarizers.

Question: How to cluster web pages?


Automatic detection of player's identity in soccer videos using faces and text cues. In soccer videos, most significant actions are usually followed by close--up shots of players that take part in the action itself. Automatically annotating the identity of the players present in these shots would be considerably valuable for indexing and retrieval applications. Due to high variations in pose and illumination across shots however, current face recognition methods are not suitable for this task. We show how the inherent multiple media structure of soccer videos can be exploited to understand the players' identity without relying on direct face recognition. The proposed method is based on a combination of interest point detector to "read" textual cues that allow to label a player with its name, such as the number depicted on its jersey, or the superimposed text caption showing its name. Players not identified by this process are then assigned to one of the labeled faces by means of a face similarity measure, again based on the appearance of local salient patches. We present results obtained from soccer videos taken from various recent games between national teams.

Question: How to detect the identity of players in soccer videos?


Cryptocurrencies, smart contracts, and artificial intelligence. Recent developments in "cryptocurrencies" and "smart contracts" are creating new opportunities for applying AI techniques. These economic technologies would benefit from greater real world knowledge and reasoning as they become integrated with everyday commerce. Cryptocurrencies and smart contracts may also provide an infrastructure for ensuring that AI systems follow specified legal and safety regulations as they become more integrated into human society.

Question: What are cryptocurrencies?


A Solid-State Marx Generator With a Novel Configuration. A new pulsed-power generator based on a Marx generator (MG) is proposed in this paper with a reduced number of semiconductor components and with a more efficient load-supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take advantage of the resonant phenomenon in charging each capacitor up to twice as the input-voltage level. In each resonant half-cycle, one of those capacitor groups is charged; eventually, the charged capacitors will be connected in series, and the summation of the capacitor voltages are appeared at the output of a pulsed-power converter. This topology can be considered as a modified MG, which works based on the resonant concept. The simulated models of this converter have been investigated in a MATLAB/Simulink platform, and a laboratory prototype has been implemented in a laboratory. The simulation and test results verify the operation of the proposed topology in different switching modes.

Question: What is a solid-state Marx generator?


A novel 8T SRAM with minimized power and delay. In this paper, a novel 8T SRAM cell is proposed which aims at decreasing the delay and lowering the total power consumption of the cell. The threshold voltage variations in the transistor affect the read and write stability of the cell. Also, power dissipation increases with the number of transistors which in turn affects the read and write stability. The proposed 8T SRAM bitcell is designed using 180 nm CMOS, n-well technology with a supply voltage of 1.8 V. The results show that the average delay has been improved by 80 % compared to the conventional 6T cell. The total power is improved by 14.5 % as compared to conventional 6T SRAM cell.

Question: How much power does a 8T SRAM cell consume?


Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments. Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, s

Question: What is the difference between a line segment and a line?


A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents. This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit

Question: What is the difference between a cognitive architecture and a cognitive system?


Feasibility of a DC network for commercial facilities. This paper analyzes the feasibility of direct current for the supply of offices and commercial facilities. This is done by analyzing a case study, i.e. the supply to a university department. Voltage drop calculations have been carried out for different voltage levels. A back-up system for reliable power supply is designed based on commercially available batteries. Finally, an economic evaluation of AC vs. DC is performed.

Question: What is the feasibility of a DC network for commercial facilities?


Heroin epidemics, treatment and ODE modelling.. The UN [United Nations Office on Drugs and Crime (UNODC): World Drug Report, 2005, vol. 1: Analysis. UNODC, 2005.], EU [European Monitoring Centre for Drugs and Drug Addiction (EMCDDA): Annual Report, 2005.http://annualreport.emcdda.eu.int/en/home-en.html.] and WHO [World Health Organisation (WHO): Biregional Strategy for Harm Reduction, 2005-2009. HIV and Injecting Drug Use. WHO, 2005.] have consistently highlighted in recent years the ongoing and persistent nature of opiate and particularly heroin use on a global scale. While this is a global phenomenon, authors have emphasised the significant impact such an epidemic has on individual lives and on society. National prevalence studies have indicated the scale of the problem, but the drug-using career, typically consisting of initiation, habitual use, a treatment-relapse cycle and eventual recovery, is not well understood. This paper presents one of the first ODE models of opiate addiction, based on the principles of mathematical epidemiology. The aim of this model is to identify parameters of interest for further study, with a view to informing and assisting policy-makers in targeting prevention and treatment resources for maximum effectiveness. An epidemic threshold value, R(0), is proposed for the drug-using career. Sensitivity analysis is performed on R(0) and it is then used to examine the stability of the system. A condition under which a backward bifurcation may exist is found, as are conditions that permit the existence of one or more endemic equilibria. A key result arising from this model is that prevention is indeed better than cure.

Question: What is the heroin epidemic?


Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization. Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.

Question: What is the difference between stochastic gradient descent and dual coordinate ascent?


On partial least squares in head pose estimation: How to simultaneously deal with misalignment. Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors.

Question: How to simultaneously deal with misalignment.


Reinforcement Learning for Spoken Dialogue Systems. Recently, a number of authors have proposed treating dialog ue systems as Markov decision processes (MDPs). However, the practical applica tion of MDP algorithms to dialogue systems faces a number of severe technical chall enges. We have built a general software tool (RLDS, for Reinforcement Learning fo r Dialogue Systems) based on the MDP framework, and have applied it to dialogue co rpora gathered from two dialogue systems built at AT&T Labs. Our experiment s demonstrate that RLDS holds promise as a tool for "browsing" and understandin g correlations in complex, temporally dependent dialogue corpora.

Question: What is the difference between a dialogue system and a dialogue manager?


On the bipolarity in argumentation frameworks. In this paper, we propose a survey of the use of bipolarity in argumentation frameworks, i.e. the presence of two kinds of entities (a positive entity and a negative entity). An argumentation process follows three steps: building the arguments and the interactions between them, valuating the arguments using or not the interactions and finally defining the acceptability of the arguments. This paper shows on various applications and with some formal definitions that bipolarity appears (in some cases since always) and can be used in each step of this process under different forms.

Question: What is the bipolarity in argumentation frameworks?


Towards Security as a Service (SecaaS): On the modeling of Security Services for Cloud Computing. The security of software services accessible via the Internet has always been a crosscutting non-functional requirement of uttermost importance. The recent advent of the Cloud Computing paradigm and its wide diffusion has given birth to new challenges towards the securing of existing Cloud services, by properly accounting the issues related to their delivery models and their usage patterns, and has opened the way to the new concept of Security as a Service(SecaaS), i.e. the ability of developing reusable software services which can be composed with standard Cloud services in order to offer them the suitable security features. In this context, there is a strong need for methods and tools for the modeling of security concerns, as well as for evaluation techniques, for supporting both the comparison of different design choices and the analysis of their impact on the behavior of new services before their actual realization. This paper proposes a meta-model for supporting the modeling of Security Services in a Cloud Computing environment as well as an approach for guiding the identification and the integration of security services within the standard Cloud delivery models. The proposal is exemplified through a case study.

Question: What is the difference between a meta-model and a model?


Illuminating cell signalling with optogenetic tools. The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.

Question: What is the optogenetic toolkit?


Dynamic mode decomposition of numerical and experimental data. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamic mode decomposition of numerical and experimental data Peter Schmid

Question: What is dynamic mode decomposition?


Fully Statistical Neural Belief Tracking. This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.

Question: What is the difference between a neural belief tracker and a neural belief tracker?


A Hypercube-Based Indirect Encoding for Evolving Large-Scale Neural Networks. Research in neuroevolution, i.e. evolving artificial neural networks (ANNs) through evolutionary algorithms, is inspired by the evolution of biological brains. Because natural evolution discovered intelligent brains with billions of neurons and trillions of connections, perhaps neuroevolution can do the same. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. This paper presents a method called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective Compositional Pattern Producing Networks (connective CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. The advantage of this approach is that it can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution . HyperNEAT is demonstrated through visual discrimination and food gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.

Question: How many neurons does a neural network have to have to solve a visual discrimination task?


Deriving optimal weights in deep neural networks. Training deep neural networks generally requires massive amounts of data and is very computation intensive. We show here that it may be possible to circumvent the expensive gradient descent procedure and derive the parameters of a neural network directly from properties of the training data. We show that, near convergence, the gradient descent equations for layers close to the input can be linearized and become stochastic equations with noise related to the covariance of data for each class. We derive the distribution of solutions to these equations and discover that it is related to a "supervised principal component analysis." We implement these results on image datasets MNIST, CIFAR10 and CIFAR100 and find that, indeed, pretrained layers using our findings performs comparable or superior to neural networks of the same size and architecture trained with gradient descent. Moreover, our pretrained layers can often be calculated using a fraction of the training data, owing to the quick convergence of the covariance matrix. Thus, our findings indicate that we can cut the training time both by requiring only a fraction of the data used for gradient descent, and by eliminating layers in the costly backpropagation step of the training. Additionally, these findings partially elucidate the inner workings of deep neural networks and allow us to mathematically calculate optimal solutions for some stages of classification problems, thus significantly boosting our ability to solve such problems efficiently.

Question: How to derive optimal weights in deep neural networks?


Lyapunov-based control for switched power converters. The fundamental properties, such as passivity or incremental passivity, of the network elements making up a switched power converter are examined. The nominal open-loop operation of a broad class of such converters is shown to be stable in the large via a Lyapunov argument. The obtained Lyapunov function is then shown to be useful for designing globally stabilizing controls that include adaptive schemes for handling uncertain nominal parameters. Numerical simulations illustrate the application of this control approach in DC-DC converters.<<ETX>>

Question: What is ETX?


, GT-GOLEM-2011-001 , 2011 Ach : IPC for Real-Time Robot Control. We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license.

Question: What is the source code for Ach?


Do Personal Ethics Influence Corporate Ethics ?. In this paper, we introduce a new measure of personal ethics in the form of marital cheating to examine the relationship between personal ethics and corporate misconduct. Firms with CEOs and CFOs who use a marital infidelity website are more than twice as likely to engage in two forms of corporate misconduct. The relationship is not explained by a wide range of regional, firm, and executive characteristics or by the infidelity website usage of other executives. Additionally, white-collar SEC defendants also have elevated levels of infidelity website usage. Our findings suggest that personal and professional ethics are closely related. (JEL G30, M14)

Question: Do personal ethics influence corporate ethics?


A real-time object detecting and tracking system for outdoor night surveillance. Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking. 2007 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

Question: What is the difference between a real-time object detecting and tracking system for outdoor night surveillance and a real-time object detecting and tracking system for outdoor night surveillance?


Neural Machine Translation for Query Construction and Composition. Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.

Question: What is the difference between neural machine translation and question answering?


Discovering evolutionary theme patterns from text: an exploration of temporal text mining. Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.

Question: What is the evolutionary theme pattern of the following text?


Performance evaluation of pattern classifiers for handwritten character recognition. This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.

Question: What is the performance evaluation of pattern classifiers for handwritten character recognition?


Optimal control of execution costs for portfolios. We derive dynamic optimal trading strategies that minimize the expected cost of trading blocks of securities over a xed time horizon. Given xed blocks si of shares of stock i to be traded within a nite number of periods T , i = 1; : : : ; n, and given price-impact functions that yield the execution price of an individual trade as a function of the shares of stock i traded and current market conditions, we obtain the optimal sequence of trades as a function of market conditions|closed-form expressions in some cases|that minimizes the expected cost of executing si within T periods. We also propose an approximation algorithm for incorporating constraints, a particularly important extension in practice. To illustrate the practical relevance of our methods, we apply them to a hypothetical portfolio of 25 stocks by estimating their price-impact functions using historical trade data from 1996 and deriving the optimal execution strategies for the portfolio, and by performing several Monte Carlo simulation experiments. This research is part of the MIT Laboratory for Financial Engineering's Transactions Costs Project. We are grateful to Investment Technology Group, the National Science Foundation (Grant Nos. DMI{9610486 and SBR{9709976), and the sponsors of the LFE for nancial support. We thank Dave Cushing, Chris Darnell, Robert Ferstenberg, Rohit D'Souza, John Heaton, Leonid Kogan, Bruce Lehmann, Greg Peterson, Jiang Wang, and seminar participants at Columbia, Harvard, London Business School, MIT, Northwestern, the NYSE Conference on Best Execution, the Society of Quantitative Analysts, and Yale for helpful comments and discussion. Boeing Professor of Operations Research, MIT Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, E53-359, Cambridge, MA 02142{1347. Portfolio Strategist, Long Term Capital Management, One East Weaver St., Greenwich, CT 06831. Harris & Harris Group Pro

Question: What is the optimal control of execution costs for portfolios?


Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs. We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.

Question: What is the difference between target extraction and polarity classification?


Design, kinematics and dynamics modeling of a lower-limb walking assistant robot. This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.

Question: How to model a lower limb walking assistant robot?


If You Measure It, Can You Improve It?: Exploring The Value of Energy Disaggregation. Over the past few years, dozens of new techniques have been proposed for more accurate energy disaggregation, but the jury is still out on whether these techniques can actually save energy and, if so, whether higher accuracy translates into higher energy savings. In this paper, we explore both of these questions. First, we develop new techniques that use disaggregated power data to provide actionable feedback to residential users. We evaluate these techniques using power traces from 240 homes and find that they can detect homes that need feedback with as much as 84% accuracy. Second, we evaluate whether existing energy disaggregation techniques provide power traces with sufficient fidelity to support the feedback techniques that we created and whether more accurate disaggregation results translate into more energy savings for the users. Results show that feedback accuracy is very low even while disaggregation accuracy is high. These results indicate a need to revisit the metrics by which disaggregation is evaluated.

Question: How accurate are the energy disaggregation techniques?


Visualizing cyber security: Usable workspaces. The goal of cyber security visualization is to help analysts increase the safety and soundness of our digital infrastructures by providing effective tools and workspaces. Visualization researchers must make visual tools more usable and compelling than the text-based tools that currently dominate cyber analysts' tool chests. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. We describe our study of cyber-security professionals and visualizations in a large, high-resolution display work environment and the analytic tasks this environment can support. We articulate a set of design principles for usable cyber analytic workspaces that our studies have brought to light. Finally, we present prototypes designed to meet our guidelines and a usability evaluation of the environment.

Question: How can we visualize cyber security?


Personal Safety is More Important Than Cost of Damage During Robot Failure. As robots become more common in everyday life it will be increasingly important to understand how non-experts will view robot failure. In this study, we found that severity of failure seems to be tightly coupled with perceived risk to self rather than risk to the robot's task and object. We initially thought perceived severity would be tied to the cost of damage. Instead, participants placed falling drinking glasses above a laptop when rating the severity of the failure. Related results reinforce the primacy of personal safety over the financial cost of damage and suggest the results were tied to proximity to breaking glass.

Question: How do people perceive robot failure?


Metacognitive Beliefs About Procrastination : Development and Concurrent Validity of a Self-Report Questionnaire. This article describes the development of a questionnaire on metacognitive beliefs about procrastination. In Study 1 we performed a principal axis factor analysis that suggested a twofactor solution for the data obtained from the preliminary questionnaire. The factors identified were named positive and negative metacognitive beliefs about procrastination. The factor analysis reduced the questionnaire from 22 to 16 items, with each factor consisting of 8 items. In Study 2 we performed a confirmatory factor analysis that provided support for the twofactor solution suggested by the exploratory factor analysis. Both factors had adequate internal consistency. Concurrent validity was partially established through correlation analyses. These showed that positive metacognitive beliefs about procrastination were positively correlated with decisional procrastination, and that negative metacognitive beliefs about procrastination were positively correlated with both decisional and behavioral procrastination. The Metacognitive Beliefs About Procrastination Questionnaire may aid future research into procrastination and facilitate clinical assessment and case formulation.

Question: What is metacognitive beliefs about procrastination?


Towards an implementation framework for business intelligence in healthcare. As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes. © 2013 Elsevier Ltd. All rights reserved.

Question: What are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities?


Contextual Equivalence for Probabilistic Programs with Continuous Random Variables and Scoring. We present a logical relation for proving contextual equivalence in a probabilistic programming language (PPL) with continuous random variables and with a scoring operation for expressing observations and soft constraints. Our PPL model is based on a big-step operational semantics that represents an idealized sampler with likelihood weighting. The semantics treats probabilistic non-determinism as a deterministic process guided by a source of entropy. We derive a measure on result values by aggregating (that is, integrating) the behavior of the operational semantics over the entropy space. Contextual equivalence is defined in terms of these measures, taking real events as observable behavior. We define a logical relation and prove it sound with respect to contextual equivalence. We demonstrate the utility of the logical relation by using it to prove several useful examples of equivalences, including the equivalence of a βv-redex and its contractum and a general form of expression re-ordering. The latter equivalence is sound for the sampling and scoring effects of probabilistic programming but not for effects like mutation or control.

Question: What is the difference between a βv-redex and its contractum?


Time-Domain Finite-Difference and Finite-Element Methods for Maxwell Equations in Complex Media. Extensions of finite-difference time-domain (FDTD) and finite-element time-domain (FETD) algorithms are reviewed for solving transient Maxwell equations in complex media. Also provided are a few representative examples to illustrate the modeling capabilities of FDTD and FETD for complex media. The term complex media refers here to media with dispersive, (bi)anisotropic, inhomogeneous, and/or nonlinear properties present in the constitutive tensors.

Question: How to solve Maxwell equations in complex media?


Human Activity Recognition from Sensor Data. Human Activity Recognition (HAR) refers to the problem of automatically identifying human activities by learning from data collected either from the individual or from the environment surrounding that individual. The form of the data might be inertial measurements, physiological signals from devices like wearable sensors or those such as image, audio and video data collected from environmental devices/sensors. HAR has been a well studied problem for its potential capability to mine human behavioral patterns and for monitoring human activity for security and health purposes.

Question: What is the difference between human activity recognition and human activity monitoring?


Game private networks performance: analytical models for very-large scale simulation. The WTFast's Gamers Private Network (GPN®) is a client/server solution that makes online games faster. GPN® connects online video-game players with a common game service across a wide-area network. Online games are interactive competitions by individual players who compete in a virtual environment. Response time, latency and its predictability are keys to GPN® success and runs against the vast complexity of internet-wide systems. We have built an experimental network of virtualized GPN® components so as to carefully measure the statistics of latency for distributed Minecraft games and to do so in a controlled laboratory environment. This has led to a better understanding of the coupling between parameters such as: the number of players, the subset of players that are idle or active, the volume of packets exchanged, the size of packets, latency to and from the game servers, and time-series for most of those parameters. In this paper we present a mathematical model of those system game network parameters and show how it leads to: (1) realistic simulation of each of those network or game parameters, without relying on the experimental setup; (2) very large-scale numerical simulation of the game setup so as to explore various internet-wide performance scenarios that: (a) are impossible to isolate from internet "noise" in their real environment and; (b) would require vast supercomputing resources if they were to be simulated exhaustively. We motivate all elements of our mathematical model and estimate the savings in computational costs they will bring for very large-scale simulation of the GPN®. Such simulations will improve quality of service for GPN® systems and their reliability.

Question: What is the difference between a game server and a game client?


Analysis and Application of Discrete Halbach Magnet Array With Unequal Arc Lengths and Unequally Changed Magnetization Directions. This paper presents a general process to analyze and optimize the performance for an arbitrary discrete Halbach magnet array which is "unnormal" on magnetization direction and arc length of each segment. Besides the original type, a new arrangement of Halbach array is proposed, which does not have main PM segments. Two-dimensional field analytical models for predicting the air-gap field distribution of both arrangements are offered and validated by an FEA method. Then, a multi-objective optimizer was applied for different requirements, and comparison between normal and optimized 3-segment Halbach array is given to verify the effect of optimization. At last, some potential applications and follow-up study are discussed.

Question: What is the difference between normal and optimized Halbach array?


Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network. Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.

Question: What is the multi-turn response selection task?


Learning Joint Multilingual Sentence Representations with Neural Machine Translation. In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new crosslingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.

Question: What is the aim of this paper?


Development Emails Content Analyzer: Intention Mining in Developer Discussions (T). Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting source code of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics.

Question: What is the purpose of development emails?


Expert and exceptional performance: evidence of maximal adaptation to task constraints.. Expert and exceptional performance are shown to be mediated by cognitive and perceptual-motor skills and by domain-specific physiological and anatomical adaptations. The highest levels of human performance in different domains can only be attained after around ten years of extended, daily amounts of deliberate practice activities. Laboratory analyses of expert performance in many domains such as chess, medicine, auditing, computer programming, bridge, physics, sports, typing, juggling, dance, and music reveal maximal adaptations of experts to domain-specific constraints. For example, acquired anticipatory skills circumvent general limits on reaction time, and distinctive memory skills allow a domain-specific expansion of working memory capacity to support planning, reasoning, and evaluation. Many of the mechanisms of superior expert performance serve the dual purpose of mediating experts' current performance and of allowing continued improvement of this performance in response to informative feedback during practice activities.

Question: What is the difference between expert and non-expert performance?


Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments. Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.

Question: What is the difference between a fine-grained sentiment and a coarse-grained sentiment?


Design of Electronic Throttle Valve Position Control System using Nonlinear PID Controller. In recent years, the use of electronic throttle valve systems has been very popular in the automotive industry. It is used to regulate the amount of air flow into the engine. Due to the existence of multiple nonsmooth nonlinearities, the controller design to the electronic throttle valve becomes difficult task. These nonlinearities including stick–slip friction, backlash, and a discontinuous nonlinear spring involved in the system. In the first part of this paper the electronic throttle valve system is presented first, and then the model is derived for each components of the throttle valve system. Later, the system dimension is reduced to two by ignoring the motor inductance at the end of this part of work. Actually this step enables us to design a nonlinear PID controller electronic throttle valve system. The simulation results, of applying a nonlinear PID controller to the electronic throttle valve system, show the effectiveness of the proposed controller in forcing the angle of the throttle valve to the desired opening angle in presence of nonlinearities and disturbances in throttle valve system model.

Question: How to design a nonlinear PID controller for an electronic throttle valve system?


Factors affecting the acceptance of information systems supporting emergency operations centres. Despite the recognition that information system acceptance is an important antecedent of effective emergency management, there has been comparatively very little research examining this aspect of technology acceptance. The current research responded to this gap in literature by adapting and integrating existing models of technology acceptance. This was done in order to examine how a range of technology acceptance factors could affect the acceptance of emergency operations centre information systems. Relationships between several of these factors were also examined. Questionnaire data from 383 end-users of four different emergency operations centre information systems were analysed using structural equation modelling. This analysis concluded that technology acceptance factors of performance expectancy, effort expectancy, social influence and information quality explained 65 percent of variance in symbolic adoption, which is a combination of mental acceptance and psychological attachment towards an information system. A number of moderating effects of age, gender, experience of use and domain experience were also identified. A mediating component, of performance expectancy, explained 49 percent of variance between facilitating conditions, information quality, effort expectancy, and resulting symbolic adoption. These findings highlight a need to re-focus technology acceptance research on both mediating and moderating effects and the importance of considering domain specific factors. Applied recommendations are also made, for successfully implementing relevant information

Question: What is the difference between a mediating component and a moderating effect?


Threat , Authoritarianism , and Selective Exposure to Information. We examined the hypothesis that threat alters the cognitive strategies used by high authoritarians in seeking out new political information from the environment. In a laboratory experiment, threat was manipulated through a "mortality salience" manipulation used in research on terror management theory (Pyszczynski, Solomon, & Greenberg, 2003). Subjects (N = 92) were then invited to read one of three editorial articles on the topic of capital punishment. We found that in the absence of threat, both low and high authoritarians were responsive to salient norms of evenhandedness in information selection, preferring exposure to a two-sided article that presents the merits of both sides of an issue to an article that selectively touts the benefits of the pro or con side of the issue. However, in the presence of threat, high but not low authoritarians became significantly more interested in exposure to an article containing uniformly pro-attitudinal arguments, and significantly less interested in a balanced, two-sided article. Finally, a path analysis indicated that selective exposure to attitude-congruent information led to more internally consistent policy attitudes and inhibited attitude change. Discussion focuses on the role of threat in conditioning the cognitive and attitudinal effects of authoritarianism.

Question: What is the difference between high and low authoritarians?


Adhesive capsulitis: sonographic changes in the rotator cuff interval with arthroscopic correlation. To evaluate the sonographic findings of the rotator interval in patients with clinical evidence of adhesive capsulitis immediately prior to arthroscopy. We prospectively compared 30 patients with clinically diagnosed adhesive capsulitis (20 females, 10 males, mean age 50 years) with a control population of 10 normal volunteers and 100 patients with a clinical suspicion of rotator cuff tears. Grey-scale and colour Doppler sonography of the rotator interval were used. Twenty-six patients (87%) demonstrated hypoechoic echotexture and increased vascularity within the rotator interval, all of whom had had symptoms for less than 1 year. Three patients had hypoechoic echotexture but no increase in vascularity, and one patient had a normal sonographic appearance. All patients were shown to have fibrovascular inflammatory soft-tissue changes in the rotator interval at arthroscopy commensurate with adhesive capsulitis. None of the volunteers or the patients with a clinical diagnosis of rotator cuff tear showed such changes. Sonography can provide an early accurate diagnosis of adhesive capsulitis by assessing the rotator interval for hypoechoic vascular soft tissue.

Question: What is the sonographic appearance of adhesive capsulitis?


Digital Image Enhancement and Noise Filtering by Use of Local Statistics. Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.

Question: How to filter noise in images?


4W1H in mobile crowd sensing. With the rapid proliferation of sensor-rich smartphones, mobile crowd sensing has become a popular research field. In this article, we propose a four-stage life cycle (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and use 4W1H (i.e., what, when, where, who, and how) to sort out the research problems in the mobile crowd sensing domain. Furthermore, we attempt to foresee some new research directions in future mobile crowd sensing research.

Question: What is the four-stage life cycle of mobile crowd sensing?


CDnet 2014: An Expanded Change Detection Benchmark Dataset. Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.

Question: What is the latest release of the CDnet dataset?


Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS. The conventional six-transistor static random access memory (SRAM) cell allows high density and fast differential sensing but suffers from half-select and read-disturb issues. Although the conventional eight-transistor SRAM cell solves the read-disturb issue, it still suffers from low array efficiency due to deterioration of read bit-line (RBL) swing and  $\text{I}_{\mathbf {on}}/\text{I}_{\mathbf {off}}$  ratio with increase in the number of cells per column. Previous approaches to solve these issues have been afflicted by low performance, data-dependent leakage, large area, and high energy per access. Therefore, in this paper, we present three iterations of SRAM bit cells with nMOS-only based read ports aimed to greatly reduce data-dependent read port leakage to enable 1k cells/RBL, improve read performance, and reduce area and power over conventional and 10T cell-based works. We compare the proposed work with other works by recording metrics from the simulation of a 128-kb SRAM constructed with divided-wordline-decoding architecture and a 32-bit word size. Apart from large improvements observed over conventional cells, up to 100-mV improvement in read-access performance, up to 19.8% saving in energy per access, and up to 19.5% saving in the area are also observed over other 10T cells, thereby enlarging the design and application gamut for memory designers in low-power sensors and battery-enabled devices.

Question: What is the area of a 32-bit word?


Automated Evaluation of Out-of-Context Errors. We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.

Question: How to evaluate a machine learning algorithm for text understanding?


Extreme gigantomastia in pregnancy: case report—my experience with two cases in last 5 years. We present an extreme case of gigantomastia in pregnancy during the second gemelar pregnancy of a 30-year-old woman. Her first pregnancy was 8 years ago, was also gemelar and she delivered with caesarean section. From the beginning of her current pregnancy, the patient noted steady growth of both of her breasts that reached enormous dimensions at the end of the pregnancy. This kind of breast changes did not occur during her first pregnancy. The patient also suffered from myasthenia gravis that was in remission during this pregnancy, without any therapy. The patient was in the 38 weeks of gestation, and a delivery with caesarean section was performed in line with the reduction of her breasts. The main reasons that led me to perform these two interventions as one act were the fact that puerperal mastitis could develop on these enormous breasts, further the small regression of these huge breasts during the bromocriptine treatment, as well as the intention to avoid other operative traumas, considering possibility of exacerbation of myasthenia gravis. I had already performed bilateral reduction mammaplasty with free areola-nipple graft, when a tissue with total weight of 20 kg (2 × 10 kg) was removed. The patient had an excellent post-operation recovery course.

Question: What is gigantomastia?


Improving Liver Lesion Detection using CT-to-PET FCN and GAN Networks. We present a brief description of our recent work that was submitted for journal publication introducing a novel system for generation of virtual PET images using CT scans [1]. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to synthesize PET images from given input CT images. Clinically, such solutions may reduce the need for the more expensive and radioactive PET/CT scan. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a reduction in the average false positive per case while keeping the same true positive rate. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.

Question: How to improve liver lesion detection using CT-to-PET FCN and GAN Networks.


3D shape regression for real-time facial animation. We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.

Question: How to animate a face?


Hand Gesture Recognition System. The task of gesture recognition is highly challenging due to complex background, presence of nongesture hand motions, and different illumination environments. In this paper, a new technique is proposed which begins by detecting the hand and determining its canter, tracking the hands trajectory and analyzing the variations in the hand locations, and finally recognizing the gesture. The proposed technique overcomes background complexity and distance from camera which is up to 2.5 meters. Experimental results show that the proposed technique can recognize 12 gestures with rate above 94%. Keywords— Hand gesture recognition, skin detection, Hand tracking.

Question: How to recognize hand gestures?


System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems. If your wanted solutions manual ins't on this list, also can ask me if _ is available. Theory of Applied Robotics: Kinematics, Dynamics and Control (Reza N. _ Jazar) System Dynamics: Modeling and Simulation of Mechatronic Systems (4th _. Solution Manual Dynamics of Mechanical Systems (C.T.F. Ross) Solution Manual Thermodynamics : An Integrated Learning System (Schmidt, Ezekoye, System Dynamics : Modeling, Simulation, and Control of Mechatronic Systems (5th.

Question: What is the name of the book?


An Improved PSFB PWM DC–DC Converter for High-Power and Frequency Applications. In the phase shifted full bridge (PSFB) pulse width modulation (PWM) converter, external snubber capacitors are connected in parallel to insulated gate bipolar transistors (IGBTs) in order to decrease turn-off losses. The zero voltage transition (ZVT) condition is not provided at light loads, thus the parallel capacitors discharge through IGBTs at turn on which causes switching losses and failure risk of the IGBTs. Capacitor discharge through IGBT restricts the use of high-value snubber capacitors, and turn-off loss of the IGBT increases at high currents. This problematic condition occurs especially at the lagging leg. In this study, a new technique enabling the use of high-value snubber capacitors with the lagging leg of the PSFB PWM converter is proposed. As advantages of the proposed technique, high-capacitive discharge current through IGBT is prevented at light loads, the turn-off switching losses of the IGBTs are decreased, and the performance of the converter is improved at high currents. The proposed PSFB PWM converter includes an auxiliary circuit, and it has a simple structure, low cost, and ease of control as well. The operation principle and detailed design procedure of the converter are presented. The theoretical analysis is verified exactly by a prototype of 75 kHz and 10 kW converter.

Question: How to improve the performance of the converter at high currents?


The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Self-regulated learning is recognized as a critical factor for successful online learning, and students' perceived academic control and academic emotions are important antecedents of self-regulated learning. Because emotions and cognition are interrelated, investigating the joint relationship between perceived academic control and academic emotions on self-regulated learning would be valuable to understanding the process of self-regulated learning. Therefore, this study examined the role of academic emotions (enjoyment, anxiety, and boredom) in the relationship between perceived academic control and selfregulated learning in online learning. The path model was proposed to test the mediating and moderating effects of academic emotions. Data were collected from 426 Korean college students registered in online courses, and a path analysis was conducted. The results demonstrated that enjoyment mediated the relationship between perceived academic control and self-regulated learning, but the moderating effect of enjoyment was not significant. Boredom and anxiety did not have significant mediating effects on self-regulated learning, whereas they showed significant moderating effects in the relationship between perceived academic control and self-regulated learning. The role of academic emotions in learning and their implications for facilitating students' self-regulated learning in online learning were discussed based on the findings. 2014 Elsevier Ltd. All rights reserved.

Question: What is the role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning?


Early versus late fusion in semantic video analysis. Semantic analysis of multimodal video aims to index segments of interest at a conceptual level. In reaching this goal, it requires an analysis of several information streams. At some point in the analysis these streams need to be fused. In this paper, we consider two classes of fusion schemes, namely early fusion and late fusion. The former fuses modalities in feature space, the latter fuses modalities in semantic space. We show by experiment on 184 hours of broadcast video data and for 20 semantic concepts, that late fusion tends to give slightly better performance for most concepts. However, for those concepts where early fusion performs better the difference is more significant.

Question: What is the difference between early and late fusion?


Order patterns recurrence plots in the analysis of ERP data. Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.

Question: How to use recurrence plots in the analysis of ERP data?


The TangibleK Robotics Program: Applied Computational Thinking for Young Children.. This article describes the TangibleK robotics program for young children. Based on over a decade of research, this program is grounded on the belief that teaching children about the human-made world, the realm of technology and engineering, is as important as teaching them about the natural world, numbers, and letters. The TangibleK program uses robotics as a tool to engage children in developing computational thinking and learning about the engineering design process. It includes a research-based, developmentally appropriate robotics kit that children can use to make robots and program their behaviors. The curriculum has been piloted in kindergarten classrooms and in summer camps and lab settings. The author presents the theoretical framework that informs TangibleK and the "powerful ideas" from computer science and robotics on which the curriculum is based, linking them to other disciplinary areas and developmental characteristics of early childhood. The article concludes with a description of classroom pedagogy and activities, along with assessment tools used to evaluate learning outcomes.

Question: What is the TangibleK robotics program?


An integrated framework for optimizing automatic monitoring systems in large IT infrastructures. The competitive business climate and the complexity of IT environments dictate efficient and cost-effective service delivery and support of IT services. These are largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides an effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes an integrated framework for minimizing false positive tickets and maximizing the monitoring coverage for system faults.  In particular, the integrated framework defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems. Moreover, a part of misconfigured monitoring conditions can be corrected according to false negative tickets that are discovered by another text classification algorithm in this framework. This paper also provides implementation details of a program product that uses this framework and shows some illustrative examples of successful results.

Question: What is the name of the framework?


Intrinsic Motivation in Museums : Why Does One Want to Learn ?. One often meets successful adults, professionals, or scientists who recall that their lifelong vocational interest was first sparked by a visit to a museum. In these accounts the encounter with a real, concrete object from a different world an exotic animal, a strange dress, a beautiful artifact is the kernel from which a n entire career of learning grew For others with an already developed curiosity about some field such as zoology, anthropology, or art, the museum provided an essential link in the cultivation of knowledge a place where information lo'it its abstractness and became concrete. In either case, many people ascribe powerful motivation to a museum visit, claiming that their desire to learn more about some aspect of the world was directly caused by it. Granted that these accounts of "crystallizing experiences" (Waiters and Gaidner 1986) attributed to museums might often be embellished and exaggerated in retrospect, i t would be rash to dismiss them entirely, for the fascination of museums seems to be a very real psychological phenomenon. The question rather becomes. How do museums motivate viewers to learn? Is there a unique, sui generis "museum experience" that helps viewers start on the long journey of learning? How do museums present information in a meaningful way, a way that deepens a person's experience and promotes further learning? To begin answering these questions, it will be useful to review what we know about human motivation in relation to learning. Children are born with a desire for knowledge, and some of the most stupendous feats of learning to walk, talk, get along with others, to take care of oneself are accomplished without seeming effort in the first few years of life. It would be difficult to see how a species as dependent on learning as we are could have

Question: What is the difference between intrinsic and extrinsic motivation?


Photorealistic Scene Reconstruction by Voxel Coloring. A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward-facing and outward-facing cameras.

Question: How to reconstruct a scene from multiple images?


ArnetMiner: extraction and mining of academic social networks. This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.

Question: What is the arnetminer system?


Hierarchical data-driven vehicle dispatch and ride-sharing. Modern transportation system suffers from increasing passenger demand, limited vehicle supply and inefficient mobility service. Towards building an intelligent transportation system to address these issues, we propose a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply. In the higher hierarchy, we optimize idle mileage induced by rebalancing vehicles across regions using receding horizon control towards current and predicted future requests. In addition, we design a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties. In the lower hierarchy, within each region, pick-up and drop-off schedules for real-time requests are obtained for each vehicle by solving mixed-integer linear programs (MILP). The objective of the MILP is to minimize total mileage delay due to ride-sharing while serving as many requests as possible. We illustrate the validity of our framework via numerical simulations on taxi trip data from New York City.

Question: How to solve the problem of ride-sharing?


A compact filtering power divider based on SIW triangular cavities. A compact substrate-integrated waveguide (SIW) filtering power divider with high isolation is presented in this paper based on SIW triangular cavities. An isolation resistance is exploited to achieve high isolation between two output ports. The design parameters can be realized by determining proper offset positions of the feeding ports and coupling windows, which can be fixed by the synthesized Qe and Mij. By using three triangular cavities as the basic elements, a filtering power divider is designed and fabricated on a single-layer substrate with the standard printed circuit boards (PCB) process. This filtering power divider operates at the center frequency of 11.4 GHz with the bandwidths of 0.75 GHz. There are good agreements in the simulated and measured results.

Question: What is a compact filtering power divider based on SIW triangular cavities?


Image Segmentation by Cascaded Region Agglomeration. We propose a hierarchical segmentation algorithm that starts with a very fine over segmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense over segmentation (super pixel) regime under boundary-based measures.

Question: How to segment an image?


Barriers to Providing Physical Education and Physical Activity in Victorian State Secondary Schools. An on-line questionnaire was completed by 115 physical education teachers to establish the barriers to their implementation of physical education in Victorian state secondary schools. In addition, the barriers perceived by teachers to impact on students' participation in school-based physical education and physical activity were examined. The barriers to the provision of physical education were found to be largely institutional, although two-thirds of respondents recognised their own difficulties in engaging students when teaching as potential obstacles to student participation. Students were also perceived to be influenced by their own (45 per cent) and their peers (62 per cent) low levels of interest when choosing to participate. An awareness of these barriers has implications for physical education teaching, curriculum design, teacher training and adolescent participation in the school environment.

Question: What are the barriers to providing physical education in Victorian state secondary schools?


Automated Pyramid Scoring of Summaries using Distributional Semantics. The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students' summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.

Question: What is the best method for automated pyramid scoring?


Neural Monkey: The Current State and Beyond. Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development.

Question: What is a neural monkey?


KYPO Cyber Range: Design and Use Cases. The physical and cyber worlds are increasingly intertwined and exposed to cyber attacks. The KYPO cyber range provides complex cyber systems and networks in a virtualized, fully controlled and monitored environment. Time-efficient and cost-effective deployment is feasible using cloud resources instead of a dedicated hardware infrastructure. This paper describes the design decisions made during it's development. We prepared a set of use cases to evaluate the proposed design decisions and to demonstrate the key features of the KYPO cyber range. It was especially cyber training sessions and exercises with hundreds of participants which provided invaluable feedback for KYPO platform development.

Question: What is the KYPO cyber range?


A Diversity-Promoting Objective Function for Neural Conversation Models. Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g.,I don't know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (responses) given input (messages) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as objective function in neural models. Experimental results demonstrate that the proposed objective function produces more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets.

Question: What is the objective function for neural conversation models?


A random graph model of kidney exchanges: efficiency, individual-rationality and incentives. In kidney exchanges, hospitals share patient lists and receive transplantations. A kidney-paired donation (KPD) mechanism needs to promote full sharing of information about donor-patient pairs, and identify a Pareto efficient outcome that also satisfies participation constraints of hospitals. We introduce a random graph model of the KPD exchange and then fully characterize the structure of the efficient outcome and the expected number of transplantations that can be performed. Random graph theory allows early experimental results to be explained analytically, and enables the study of participation incentives in a methodological way. We derive a square-root law between the welfare gains from sharing patient-donor pairs in a central pool and the individual sizes of hospitals, illustrating the urgent need for the nationwide expansion of such programs. Finally, we establish through theoretical and computational analysis that enforcing simple individual rationality constraints on the outcome can mitigate the negative impact of strategic behavior by hospitals.

Question: What is a random graph model of kidney exchanges?


Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding. The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.

Question: How to scale slot filling models for natural language understanding?


Computer aided inbetweening. The production of inbetweens is a tedious task for animators and a complicated one for algorithms. In this paper, an algorithm for computer aided inbetweening and its integration in a pen-based graphical user interface are presented.The algorithm is layer-based, assuming an invariant layering order. It is applicable to animations in a style similar to paper cut out, in which the drawings on the cut-out pieces are inbetweened as well. The content of each key drawing is analysed and classified into strokes, chains of strokes and relations that hold among them. Rules decide what parts of different drawings may be matched. These rules specify allowed changes between relations in key drawings. A cost function based approach determines the correct matching of strokes. Generated animation paths between corresponding strokes determine the resulting inbetweens.To hold for possible mismatchings and to allow for artistic control over the results, the inbetweening algorithm is embedded in a free form graphic user interface. Thus artists are enabled to focus on the part of the inbetweening task computers are not able to solve.

Question: What is computer aided inbetweening?


EPBC: Efficient Public Blockchain Client for lightweight users. Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.

Question: What is the size of the blockchain?


Toward Detecting Malicious Links in Online Social Networks through User Behavior. It is becoming increasingly difficult to ignore the importance of using online social networks (OSNs) for various purposes such as marketing, education, entertainment, and business. However, OSNs also open the door for harmful activities and behaviors. Committing financial fraud and propagating malware and spam advertisements are very common criminal actions that people engage in by accessing uniform resource locators (URLs). It has been reported that advanced attackers tend to exploit human flaws rather than system flaws, thus, users are targeted in social media threats by hour. This research aims to understand the state of literature on detecting malicious URLs in OSNs, with a focus on two major aspects: URL and OSN objects. Although the literature presents these two aspects in a different context, this paper will mainly focus on their features in relation to malicious URL detection using classification methods. We firstly review three features of URLs: lexical features, hosts, and domains, then we discuss their limitations. We then introduce social spam analytics and detection models using combined features from both URLs and OSNs, particularly the use of user profiles and posts together with URL features, to enhance the detection of malicious behavior. This combination can help in understanding the interests of the user either explicitly, by stating choices in the profile, or implicitly, by analyzing the post behavior, as the spammers do not maintain a regular interest and tend to exploit events or top trend topics.

Question: What is the difference between a URL and an OSN?


A review on fingerprint orientation estimation. Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Question: What is the difference between a review and a critical review?


Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.

Question: How to detect and segment deteriorations by fungal pathogens in natural and plantation forests?


AndroTaint: An efficient android malware detection framework using dynamic taint analysis. Android operating system is widespread in today's smart-phone market due to its open source model, its easy functionality and huge number of Apps and App-Stores (the official and others). The coverage of some 90% of the market, it is also the place-holder for user's personal as well as sensitive information (IMEI, IMSI, etc.). There is a tendency of Apps user to trust on Android OS for securing his/her data but it has been proved that Android OS is more vulnerable to exploitation either for fun or for monetary purpose. Malware detection for Android OS has become an upcoming research problem of interest. Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis. The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform. In order to do this, this paper surveys exhaustively the available literature and works related to dynamic taint analysis. This paper proposes some novel ideas to improve the existing solution with more accuracy. There are two phases of our novel algorithm, the first phase is training phase for feature extraction and the second phase is the analysis phase for automatic tagging and tainting. We have developed a framework named AndroTaint, which is working on Dynamic Taint Analysis. AndroTaint follows a novel supervised and unsupervised Anomaly Detection technique with high precision, recall and harmonic mean. Our Dynamic Taint Analysis algorithm categorizes the App is risky, benign, malicious or aggressive as per their feature and behaviour. We have measured AndroTaint effectiveness on the basis of timeline for building dataset and 10-fold cross validation. AndroTaint covers 90% of malware and benign in analysis phase with less false positive and false negative.

Question: What is the canadian military?


Automatically verifying and reproducing event-based races in Android apps. Concurrency has been a perpetual problem in Android apps, mainly due to event-based races. Several event-based race detectors have been proposed, but they produce false positives, cannot reproduce races, and cannot distinguish be- tween benign and harmful races. To address these issues, we introduce a race verification and reproduction approach named ERVA. Given a race report produced by a race detector, ERVA uses event dependency graphs, event flipping, and replay to verify the race and determine whether it is a false positive, or a true positive; for true positives, ERVA uses state comparison to distinguish benign races from harmful races. ERVA automatically produces an event schedule that can be used to deterministically reproduce the race, so developers can fix it. Experiments on 16 apps indicate that only 3% of the races reported by race detectors are harmful, and that ERVA can verify an app in 20 minutes on average.

Question: How to automatically verify and reproduce event-based races in Android apps?


A framework for automated measurement of the intensity of non-posed Facial Action Units. This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.

Question: How do you measure the intensity of facial actions?


Acceptance and use of electronic library services in ugandan universities. University libraries in Developing Countries (DCs), hampered by developmental problems, find it hard to provide electronic services. Donor communities have come in to bridge this technology gap by providing funds to university libraries for information technology infrastructure, enabling these university libraries to provide electronic library services to patrons. However, for these services to be utilized effectively, library end-users must accept and use them. To investigate this process in Uganda, this study modifies "The Unified Theory of Acceptance and Use of Technology" (UTAUT) by replacing "effort expectancy" and "voluntariness" with "relevancy", "awareness" and "benefits" factors. In so doing, we developed the Service Oriented UTAUT (SOUTAUT) model whose dependent constructs predict 133% of the variances in user acceptance and use of e-library services. The study revealed that relevancy moderated by awareness plays a major factor in acceptance and use of e-library services in DCs.

Question: What is the unified theory of acceptance and use of technology?


Teaching the Basics of NLP and ML in an Introductory Course to Information Science. In this paper we discuss our experience of teaching basic Natural Language Processing (NLP) and Machine Learning (ML) in an introductory course to Information Science. We discuss the challenges we faced while incorporating NLP and ML to the curriculum followed by a presentation of how we met these challenges. The overall response (of students) to the inclusion of this new topic to the curriculum has been positive. Students this semester are pursuing NLP/ML projects, formulating their own tasks (some of which are novel and presented towards the end of the paper), collecting and annotating data and building models for their task.

Question: How to teach NLP and ML in an introductory course to information science?