Modeling Analysis and Parameters Calculation of Permanent Magnet Linear Synchronous Motor. Permanent magnet linear synchronous motor (PMLSM) has several advantages, such as simple structure, small volume, high force, and so on. It is a research focus and difficulties in the research field of motor. Because PMLSM is different from the traditional rotary motors in structure and running state, it is difficult for to build its precise mathematical model by analytical method. The asymmetry of the three-phase winding electromagnetic and electrical parameters of open PMLSM in a low-speed working zone is impossible to take into account. In this paper, the difference between the permanent magnet linear synchronous motor and traditional motors are analyzed. And then, a non-linear, asymmetric and variable parameter space state model is built, considering the structural and running characteristics of PMLSM. The proposed model needs all kinds of parameters of PMLSM, including selfinductance, mutual inductance, flux linkage, etc. So, a finite element calculation and actual measurement of the required parameters of a prototype PMLSM are done, respectively. The influence of air-gap on the inductance parameters was analyzed.

Question: How to calculate the inductance of a PMLSM?


Effects of adaptive cruise control and highly automated driving on workload and situation awareness : A review of the empirical evidence. Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands Centre for Transport Studies, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands c TNO Human Factors, Kampweg 5, 3769 DE Soesterberg, The Netherlands d Transportation Research Group, Civil, Maritime, Environmental Engineering and Science, Engineering and the Environment, University of Southampton, United Kingdom

Question: What is the name of the university in the Netherlands?


Conscious and unconscious perception: Experiments on visual masking and word recognition. Five experiments are presented which explore the relation of masking to consciousness and visual word processing. In Experiment 1 a single word or blank field was followed by a pattern mask. Subjects had to make one of three decisions: Did anything precede the mask? To which of two probe words was what preceded the mask more similar graphically? To which of two probe words was it more similar semantically? As word-mask stimulus onset asynchrony (SOA) was reduced, subjects reached chance performance on the detection, graphic, and semantic decisions in that order. In Experiment 2, subjects again had to choose which of two words was more similar either graphically or semantically to a nondetectable masked word, but the forced-choice stimuli now covaried negatively on graphic and semantic similarity. Subjects were now unable to choose selectively on each dimension, suggesting that their ability to choose in Experiment 1 was passively rather than intentionally mediated. In Experiment 3 subjects had to make manual identification responses to color patches which were either accompanied or preceded by words masked to prevent awareness. Color-congruent words facilitated reaction time (RT), color-incongruent words delayed RT. Experiment 4 used a lexical decision task where a trial consisted of the critical letter string following another not requiring a response. When both were words they were either semantically associated or not. The first letter string was either left unmasked, energy masked monoptically, or pattern masked dichoptically to prevent awareness. The effect of association was equal in the unmasked and pattern masked cases, but absent with energy masking. In Experiment 5 repeating a word-plus-mask (where the SOA precluded detection) from 1 to 20 times (a) increased the association effect on a subsequent lexical decision, but had no effect on (b) detectability or(c) the sema

Question: What is the difference between a mask and a pattern mask?


Domain Adaptation via Pseudo In-Domain Data Selection. We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large generaldomain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are not themselves identical to the in-domain data, we call them pseudo in-domain subcorpora. These subcorpora – 1% the size of the original – can then used to train small domain-adapted Statistical Machine Translation (SMT) systems which outperform systems trained on the entire corpus. Performance is further improved when we use these domain-adapted models in combination with a true in-domain model. The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining inand general-domain systems during decoding.

Question: How to select pseudo in-domain data?


'Strong is the new skinny': A content analysis of #fitspiration images on Instagram.. 'Fitspiration' is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. This study provides a content analysis of fitspiration imagery on the social networking site Instagram. A set of 600 images were coded for body type, activity, objectification and textual elements. Results showed that the majority of images of women contained only one body type: thin and toned. In addition, most images contained objectifying elements. Accordingly, while fitspiration images may be inspirational for viewers, they also contain a number of elements likely to have negative effects on the viewer's body image.

Question: What is the content analysis of fitspiration images on Instagram?


A study of the uniqueness of source code. This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we call syntactic redundancy. We parameterized our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.

Question: What is the uniqueness of source code?


Vulnerability , risk and adaptation : A conceptual framework. The purpose of this paper is to present a tentative conceptual framework for studies of vulnerability and adaptation to climate variability and change, generally applicable to a wide range of contexts, systems and hazards. Social vulnerability is distinguished from biophysical vulnerability, which is broadly equivalent to the natural hazards concept of risk. The IPCC definition of vulnerability is discussed within this context, which helps us to reconcile apparently contradictory definitions of vulnerability. A concise typology of physically defined hazards is presented; the relationship between the vulnerability and adaptive capacity of a human system depends critically on the nature of the hazard faced. Adaptation by a system may be inhibited by process originating outside the system; it is therefore important to consider " external " obstacles to adaptation, and links across scales, when assessing adaptive capacity.

Question: What is the difference between vulnerability and risk?


Inductive Learning of Answer Set Programs from Noisy Examples. In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.

Question: What is the difference between answer set programming and inductive logic programming?


LabelMe: A Database and Web-Based Tool for Image Annotation. We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.

Question: What is the size of the dataset?


Preliminary Guidelines for Empirical Research in Software Engineering. Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers and meta-analysts in designing, conducting and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection and analysis and reporting of empirical studies.

Question: What is empirical software engineering research?


"GrabCut": interactive foreground extraction using iterated graph cuts.

Question: What is GrabCut?


An Artificial Neural Network based Intrusion Detection System and Classification of Attacks. Network security is becoming an issue of paramount importance in the information technology era. Nowadays with the dramatic growth of communication and computer networks, security has become a critical subject for computer system. Intrusion detection is the art of detecting computer abuse and any attempt to break the networks. Intrusion detection system is an effective security tool that helps to prevent unauthorized access to network resources by analyzing the network traffic. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. One of the most promising areas of research in the area of Intrusion Detection deals with the applications of the Artificial Intelligence (AI) techniques. This proposed system presents a new approach of intrusion detection system based on artificial neural network. Multi Layer Percepron (MLP) architecture is used for Intrusion Detection System. The performance and evaluations are performed by using the set of benchmark data from a KDD (Knowledge discovery in Database) dataset. The proposed system detects the attacks and classifies them in six groups.

Question: What is an intrusion detection system?


Arousal-Biased Competition in Perception and Memory.. Our everyday surroundings besiege us with information. The battle is for a share of our limited attention and memory, with the brain selecting the winners and discarding the losers. Previous research shows that both bottom-up and top-down factors bias competition in favor of high priority stimuli. We propose that arousal during an event increases this bias both in perception and in long-term memory of the event. Arousal-biased competition theory provides specific predictions about when arousal will enhance memory for events and when it will impair it, which accounts for some puzzling contradictions in the emotional memory literature.

Question: How does arousal bias memory?


Differences in procrastination and motivation between undergraduate and graduate students. Procrastination became increasingly prevalent among students in recent years. However, little research was found that directly compares academic procrastination across different academic grade levels.    The    present study used a self-regulated learning perspective to compare procrastination types and associated motivation between undergraduate and graduate students. Sixty-six undergraduate and sixty-eight graduate students responded to a packet of questionnaires concerning their experience in an educational psychology class. The results show that students' beliefs about the usefulness of procrastination were a better predictor of academic procrastination than self-efficacy beliefs and achievement goal orientations. Student age was related to procrastination types. Among the undergraduate procrastinators, the younger students were more likely to engage in active procrastination while the older students tended to engage in passive procrastination. Implications and future research directions are discussed.

Question: What is the difference between undergraduate and graduate students?


Common Sense Knowledge Based Personality Recognition from Text. Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level , emotive words and other lexical clues such as number of first person or sec ond person words carry major role to identify personality associated with the te xt. In this work, we propose a new architecture for the same task using common sen se knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affect iv labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined comm n sense knowledge based features with phycho-linguistic features and freque ncy based features and later the features were employed in supervised classifier . We designed five SMO based supervised classifiers for five personality t raits. We observe that the use of common sense knowledge with affective and sentim ent information enhances the accuracy of the existing frameworks which use o nly psycho-linguistic features and frequency based analysis at lexical le ve .

Question: How to extract common sense knowledge from text?


Power Converter EMI Analysis Including IGBT Nonlinear Switching Transient Model. It is well known that very high dv/dt and di/dt during the switching instant is the major high-frequency electromagnetic interference (EMI) source. This paper proposes an improved and simplified EMI-modeling method considering the insulated gate bipolar transistor switching-behavior model. The device turn-on and turn-off dynamics are investigated by dividing the nonlinear transition by several stages. The real device switching voltage and current are approximated by piecewise linear lines and expressed using multiple dv/dt and di/dt superposition. The derived EMI spectra suggest that the high-frequency noise is modeled with an acceptable accuracy. The proposed methodology is verified by experimental results using a dc-dc buck converter

Question: What is the difference between a dc-dc buck converter and a dc-dc boost converter?


Examining the Contribution of Information Technology Toward Productivity and Profitability in U . S . Retail Banking. There has been much debate on whether or not the investment in Information Technology (IT) provides improvements in productivity and business efficiency. Several studies both at the industry-level and at the firm-level have contributed differing understandings of this phenomenon. Of late, however, firm-level studies, primarily in the manufacturing sector, have shown that there are significant positive contributions from IT investments toward productivity. This study examines the effect of IT investment on both productivity and profitability in the retail banking sector. Using data collected through a major study of retail banking institutions in the United States, this paper concludes that additional investment in IT capital may have no real benefits and may be more of a strategic necessity to stay even with the competition. However, the results indicate that there are substantially high returns to increase in investment in IT labor, and that retail banks need to shift their emphasis in IT investment from capital to labor. Prasad and Harker IT Impact in Retail Banking

Question: What is the contribution of IT to productivity and profitability in retail banking?


Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. The nearest neighbor problem is the follolving: Given a set of n points P = (PI, . . . ,p,} in some metric space X, preprocess P so as to efficiently answer queries which require finding bhe point in P closest to a query point q E X. We focus on the particularly interesting case of the d-dimensional Euclidean space where X = Wd under some Zp norm. Despite decades of effort, t,he current solutions are far from saabisfactory; in fact, for large d, in theory or in practice, they provide litt,le improvement over the brute-force algorithm which compares the query point to each data point. Of late, t,here has been some interest in the approximate newest neighbors problem, which is: Find a point p E P that is an c-approximate nearest neighbor of the query q in t,hat for all p' E P, d(p, q) < (1 + e)d(p', q). We present two algorithmic results for the approximate version t,hat significantly improve the known bounds: (a) preprocessing cost polynomial in n and d, and a truly sublinear query t.ime (for 6 > 1); and, (b) query time polynomial in log-n and d, and only a mildly exponential preprocessing cost* O(n) x 0(1/~)~. Furt.her, applying a classical geometric lemma on random projections (for which we give a simpler proof), we obtain t.he first known algorithm with polynomial preprocessing and query t.ime polynomial in d and log n. Unfortunately, for small E, the latter is a purely theoretical result since bhe e?rponent depends on l/e. Experimental resuits indicate that our tit algori&m offers orders of magnitude improvement on running times over real data sets. Its key ingredient is the notion of locality-sensitive hashing which may be of independent interest; here, we give applications to information ret,rieval, pattern recognition, dynamic closest-pairs, and fast clustering algorithms. *Supported by a Stanford Graduate Fellowship and NSF

Question: What is the approximate nearest neighbor problem?


Risk factors for involvement in cyber bullying : Victims , bullies and bully – victims ☆. The use of online technology is exploding worldwide and is fast becoming a preferred method of interacting. While most online interactions are neutral or positive the Internet provides a new means through which children and youth are bullied. The aim of this grounded theory approach was to explore technology, virtual relationships and cyber bullying from the perspectives of students. Seven focus groups were held with 38 students between fifth and eighth grades. The participants considered cyber bullying to be a serious problem and some characterized online bullying as more serious than  ̳traditional' bullying because of the associated anonymity. Although the students depicted anonymity as integral to cyber bullying, the findings suggest that much of the cyber bullying occurred within the context of their social groups and relationships. Findings revealed five major themes: technology embraced at younger ages and becoming the dominant medium for communication; definitions and views of cyber bullying; factors unique to cyber bullying; types of cyber bullying; and telling adults. The findings highlight the complexity of the perceived anonymity provided by the Internet and how AC C EP TE D M AN U SC R IP T ACCEPTED MANUSCRIPT 3 this may impact cyber bullying. The study offers greater awareness of the meanings of online relationships for children and youth.

Question: What is cyber bullying?


Virtual reality vs. reality in engineering education. Virtual reality has become significantly popular in recent years. It is also widely used and more frequently implemented in education, training, and research. This article discusses the potential and the current state of virtual reality, and its tools in engineering education. The focus is put on some opportunities, challenges and dead ends of implementation faced by the authors. In this point of view, virtual reality is the future of creative learning, however it has its limits in terms of practical experiments, learning by doing, which is still more effective as virtual ones.

Question: What is virtual reality?


Cooperative learning in neural networks using particle swarm optimizers. This paper presents a method to employ particle swarms optim izers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each w hich is optimized cooperatively in its own swarm. The applic ation of this technique to neural network training is investigate d, with promising results.

Question: What is a neural network?


Web 3.0 Emerging. While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.

Question: What is the base of web 3.0 applications?


Intent Mining for the Good , Bad & Ugly Use of Social Web : Concepts , Methods , and Challenges. The social web has empowered us to easily share information, express opinions, and engage in discussions on events around the world. While users of social media platforms often o↵er help and emotional support to others (the good), they also spam (the bad) and harass others as well as even manipulate others via fake news (the ugly). In order to both leverage the positive e↵ects and mitigate the negative e↵ects of using social media, intent mining provides a computational approach to proactively analyze social media data. This chapter introduces an intent taxonomy of social media usage with examples and describes methods and future challenges to mine the intentional uses of social media.

Question: What is the intent of social media?


LSTM Model to Forecast Time Series for EC2 Cloud Price. With the widespread use of spot instances in Amazon EC2, which utilize unused capacity with unfixed price, predicting price is important for users. In this paper, we try to forecast spot instance price by using long short-term memory (LSTM) algorithm to predict time series of the prices. We apply cross validation technique to our data set, and extract some features; this help our model to learn deeply. We make the prediction for 10 regions, and measure the performance using root-mean-square error (RMSE). We apply our data to other statistical models to compare their performance; we found that our model works more efficiently, also the error is decreasing more with cross validation and the result is much faster. Based on our result we try to find the availability zone that less become over on-demand price and less changing price over time, which help users to choose the most stable availability zone.

Question: How to predict the price of spot instances?


Compilers: Principles, Techniques, and Tools.

Question: What is the name of the book?


Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability. Due to enormous progress in the areas of auto-stereoscopic 3D displays, digital video broadcast and computer vision algorithms, 3D television (3DTV) has reached a high technical maturity and many people now believe in its readiness for marketing. Experimental prototypes of entire 3DTV processing chains have been demonstrated successfully during the last few years, and the motion picture experts group (MPEG) of ISO/IEC has launched related ad hoc groups and standardization efforts envisaging the emerging market segment of 3DTV. In this context the paper discusses an advanced approach for a 3DTV service, which is based on the concept of video-plus-depth data representations. It particularly considers aspects of interoperability and multi-view adaptation for the case that different multi-baseline geometries are used for multi-view capturing and 3D display. Furthermore it presents algorithmic solutions for the creation of depth maps and depth image-based rendering related to this framework of multi-view adaptation. In contrast to other proposals, which are more focused on specialized configurations, the underlying approach provides a modular and flexible system architecture supporting a wide range of multi-view structures. r 2007 Elsevier B.V. All rights reserved.

Question: How to create a depth map?


Characterizing affective instability in borderline personality disorder.. OBJECTIVE This study sought to understand affective instability among patients with borderline personality disorder by examining the degree of instability in six affective domains. The authors also examined the subjective intensity with which moods are experienced and the association between instability and intensity of affect.   METHOD In a group of 152 patients with personality disorders, subjective affective intensity and six dimensions of affective instability were measured. The mean scores for lability and intensity for each affective domain for patients with borderline personality disorder were compared with those of patients with other personality disorders through analyses that controlled for other axis I affective disorders, age, and sex.   RESULTS Greater lability in terms of anger and anxiety and oscillation between depression and anxiety, but not in terms of oscillation between depression and elation, was associated with borderline personality disorder. Contrary to expectation, the experience of an increase in subjective affective intensity was not more prominent in patients with borderline personality disorder than in those with other personality disorders.   CONCLUSIONS By applying a finer-grained perspective on affective instability than those of previous personality disorder studies, this study points to patterns of affective experience characteristic of patients with borderline personality disorder.

Question: What is the difference between borderline personality disorder and other personality disorders?


Gossip along the way : Order-Optimal Consensus through Randomized Path Averaging. Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges very slowly. A recently proposed algorithm called geographic gossip improves gossip efficiency by a p n/ log n factor for random geometric graphs, by exploiting geographic information of node locations. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional p n/ log n factor and is order optimal for grids and random geometric graphs. Our analysis provides some general techniques and can be used to provide bounds on the performance of randomized message passing algorithms operating over various graph topologies.

Question: What is the order of the gossip algorithm?


Cyber-Crimes and their Impacts : A Review 1. In the current era of online processing, maximum of the information is online and prone to cyber threats. There are a huge number of cyber threats and their behavior is difficult to early understanding hence difficult to restrict in the early phases of the cyber attacks. Cyber attacks may have some motivation behind it or may be processed unknowingly. The attacks those are processed knowingly can be considered as the cyber crime and they have serious impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber crimes is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Therefore, the current manuscript provides the understanding of cyber crimes and their impacts over society with the future trends of cyber crimes.

Question: What is cyber crime?


AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security. Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data. © 2017 Elsevier B.V. All rights reserved.

Question: What is the approximate value of a rank of a number?


Neuroevolutionary reinforcement learning for generalized control of simulated helicopters. This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain's complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks.

Question: What is neuroevolution?


Binarization-free OCR for historical documents using LSTM networks. A primary preprocessing block of almost any typical OCR system is binarization, through which it is intended to remove unwanted part of the input image, and only keep a binarized and cleaned-up version for further processing. The binarization step does not, however, always perform perfectly, and it can happen that binarization artifacts result in important information loss, by for instance breaking or deforming character shapes. In historical documents, due to a more dominant presence of noise and other sources of degradations, the performance of binarization methods usually deteriorates; as a result the performance of the recognition pipeline is hindered by such preprocessing phases. In this paper, we propose to skip the binarization step by directly training a 1D Long Short Term Memory (LSTM) network on gray-level text lines. We collect a large set of historical Fraktur documents, from publicly available online sources, and form train and test sets for performing experiments on both gray-level and binarized text lines. In order to observe the impact of resolution, the experiments are carried out on two identical sets of low and high resolutions. Overall, using gray-level text lines, the 1D LSTM network can reach 24% and 19% lower error rates on the low- and high-resolution sets, respectively, compared to the case of using binarization in the recognition pipeline.

Question: How to binarize historical documents?


Vectorization for SIMD architectures with alignment constraints. When vectorizing for SIMD architectures that are commonly employed by today's multimedia extensions, one of the new challenges that arise is the handling of memory alignment. Prior research has focused primarily on vectorizing loops where all memory references are properly aligned. An important aspect of this problem, namely, how to vectorize misaligned memory references, still remains unaddressed.This paper presents a compilation scheme that systematically vectorizes loops in the presence of misaligned memory references. The core of our technique is to automatically reorganize data in registers to satisfy the alignment requirement imposed by the hardware. To reduce the data reorganization overhead, we propose several techniques to minimize the number of data reorganization operations generated. During the code generation, our algorithm also exploits temporal reuse when aligning references that access contiguous memory across loop iterations. Our code generation scheme guarantees to never load the same data associated with a single static access twice. Experimental results indicate near peak speedup factors, e.g., 3.71 for 4 data per vector and 6.06 for 8 data per vector, respectively, for a set of loops where 75% or more of the static memory references are misaligned.

Question: How to vectorize loops with misaligned memory references?


Trust and Recommendations. Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.

Question: What is the difference between recommendation and trust?


Habits — A Repeat Performance. Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experiencesampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation. KEYWORDS—habit; automaticity; motivation; goals; behavior change; behavior prediction; self-regulation From self-help guru Anthony Robbins to the religion of Zen Buddhism, received wisdom exhorts people to be mindful, deliberative, and conscious in all they do. In contrast, contemporary research in psychology shows that it is actually people's unthinking routines—or habits—that form the bedrock of everyday life. Without habits, people would be doomed to plan, consciously guide, and monitor every action, from making that first cup of coffee in the morning to sequencing the finger movements in a Chopin piano concerto. But what is a habit? The cognitive revolution radically reshaped the behaviorist view that habits rely on simple stimulus– response associations devoid of mental representation. Emerging is a more nuanced construct that includes roles for consciousness, goals, and motivational states. Fundamental questions persist, however, especially when comparing evidence across neuropsychology, animal-learning, and social-cognition literatures. Data from these fields support three views of habit, which we term the direct-context-cuing, implicit-goal, and motivated-context models.

Question: What is a habit?


Markov Logic Networks for Optical Chemical Structure Recognition. Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.

Question: What is the chemical structure of caffeine?


Gender and Tenure Diversity in GitHub Teams. Software development is usually a collaborative venture. Open Source Software (OSS) projects are no exception; indeed, by design, the OSS approach can accommodate teams that are more open, geographically distributed, and dynamic than commercial teams. This, we find, leads to OSS teams that are quite diverse. Team diversity, predominantly in offline groups, is known to correlate with team output, mostly with positive effects. How about in OSS? Using GitHub, the largest publicly available collection of OSS projects, we studied how gender and tenure diversity relate to team productivity and turnover. Using regression modeling of GitHub data and the results of a survey, we show that both gender and tenure diversity are positive and significant predictors of productivity, together explaining a sizable fraction of the data variability. These results can inform decision making on all levels, leading to better outcomes in recruiting and performance.

Question: How diverse is the github team?


Automatic construction of multifaceted browsing interfaces. Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Interfaces that use multifaceted hierarchies represent a new powerful browsing paradigm which has been proven to be a successful complement to keyword searching. Thus far, multifaceted hierarchies have been created manually or semi-automatically, making it difficult to deploy multifaceted interfaces over a large number of databases. We present automatic and scalable methods for creation of multifaceted interfaces. Our methods are integrated with traditional relational databases and can scale well for large databases. Furthermore, we present methods for selecting the best portions of the generated hierarchies when the screen space is not sufficient for displaying all the hierarchy at once. We apply our technique to a range of large data sets, including annotated images, television programming schedules, and web pages. The results are promising and suggest directions for future research.

Question: How to automatically create a multifaceted browsing interface?


Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction. In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

Question: What is the MEES approach?


Low Power Magnetic Full-Adder Based on Spin Transfer Torque MRAM. Power issues have become a major problem of CMOS logic circuits as technology node shrinks below 90 nm. In order to overcome this limitation, emerging logic-in-memory architecture based on nonvolatile memories (NVMs) are being investigated. Spin transfer torque (STT) magnetic random access memory (MRAM) is considered one of the most promising NVMs thanks to its high speed, low power, good endurance, and 3-D back-end integration. This paper presents a novel magnetic full-adder (MFA) design based on perpendicular magnetic anisotropy (PMA) STT-MRAM. It provides advantageous power efficiency and die area compared with conventional CMOS-only full adder (FA). Transient simulations have been performed to validate this design by using an industrial CMOS 40 nm design kit and an accurate STT-MRAM compact model including physical models and experimental measurements.

Question: What is a full adder?


Local Rademacher Complexity for Multi-Label Learning. We analyze the local Rademacher complexity of empirical risk minimization-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound. Compared with methods that minimize over all singular values, concentrating on the tail singular values results in better recovery of the low-rank structure of the multi-label predictor, which plays an important role in exploiting label correlations. We propose a new conditional singular value thresholding algorithm to solve the resulting objective function. Moreover, a variance control strategy is employed to reduce the variance of variables in optimization. Empirical studies on real-world data sets validate our theoretical results and demonstrate the effectiveness of the proposed algorithm for multi-label learning.

Question: What is the local Rademacher complexity?


Of, for, and by the people: the legal lacuna of synthetic persons. Conferring legal personhood on purely synthetic entities is a very real legal possibility, one under consideration presently by the European Union. We show here that such legislative action would be morally unnecessary and legally troublesome. While AI legal personhood may have some emotional or economic appeal, so do many superficially desirable hazards against which the law protects us. We review the utility and history of legal fictions of personhood, discussing salient precedents where such fictions resulted in abuse or incoherence. We conclude that difficulties in holding "electronic persons" accountable when they violate the rights of others outweigh the highly precarious moral interests that AI legal personhood might protect.

Question: What is the legal lacuna of synthetic persons?


Unikernels: library operating systems for the cloud. We present unikernels, a new approach to deploying cloud services via applications written in high-level source code. Unikernels are single-purpose appliances that are compile-time specialised into standalone kernels, and sealed against modification when deployed to a cloud platform. In return they offer significant reduction in image sizes, improved efficiency and security, and should reduce operational costs. Our Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty. The architecture combines static type-safety with a single address-space layout that can be made immutable via a hypervisor extension. Mirage contributes a suite of type-safe protocol libraries, and our results demonstrate that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.

Question: What is a unikernel?


American Sign Language Translation through Sensory Glove ; SignSpeak. To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.

Question: How does the glove work?


Using Summarization to Discover Argument Facets in Online Idealogical Dialog. More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue; what are the abstract objects under discussion that are central to a speaker's argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.

Question: What is the central proposition of the argument?


The ARTEMIS European driving cycles for measuring car pollutant emissions.. In the past 10 years, various work has been undertaken to collect data on the actual driving of European cars and to derive representative real-world driving cycles. A compilation and synthesis of this work is provided in this paper. In the frame of the European research project: ARTEMIS, this work has been considered to derive a set of reference driving cycles. The main objectives were as follows: to derive a common set of reference real-world driving cycles to be used in the frame of the ARTEMIS project but also in the frame of on-going national campaigns of pollutant emission measurements, to ensure the compatibility and integration of all the resulting emission data in the European systems of emission inventory; to ensure and validate the representativity of the database and driving cycles by comparing and taking into account all the available data regarding driving conditions; to include in three real-world driving cycles (urban, rural road and motorway) the diversity of the observed driving conditions, within sub-cycles allowing a disaggregation of the emissions according to more specific driving conditions (congested and free-flow urban). Such driving cycles present a real advantage as they are derived from a large database, using a methodology that was widely discussed and approved. In the main, these ARTEMIS driving cycles were designed using the available data, and the method of analysis was based to some extent on previous work. Specific steps were implemented. The study includes characterisation of driving conditions and vehicle uses. Starting conditions and gearbox use are also taken into account.

Question: What is the ARTEMIS project?


Where Do Rewards Come From ?. Reinforcement learning has achieved broad and successful application in cognitive science in part because of its general formulation of the adaptive control problem as the maximization of a scalar reward function. The computational reinforcement learning framework is motivated by correspondences to animal reward processes, but it leaves the source and nature of the rewards unspecified. This paper advances a general computational framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a fitness function and some distribution of environments. Novel results from computational experiments show how traditional notions of extrinsically and intrinsically motivated behaviors may emerge from such optimal reward functions. In the experiments these rewards are discovered through automated search rather than crafted by hand. The precise form of the optimal reward functions need not bear a direct relationship to the fitness function, but may nonetheless confer significant advantages over rewards based only on fitness.

Question: What is reinforcement learning?


A question answering system based on VerbNet frames. The precision of the answer is now essential for a question answering system, because of the large amount of free texts on the Internet. Attempting to achieve a high precision, we propose a question answering system supported by case grammar theory and based on VerbNet frames. It extracts the syntactic, thematic and semantic information from the question to filter out unmatched sentences in semantic level and to extract answer chunk (a phrase or a word that can answer the question) from the answer sentence. VerbNet is applied in our system to detect the verb frames in question and candidate sentences, so that the syntactic and thematic information as well as semantic information can be therefore obtained. Our question answering system works well especially for answering factoid questions. The experiments show that our approach is able to filter out semantically unmatched sentences effectively and therefore rank the correct answer (s) higher in the result list.

Question: What is a question answering system based on VerbNet frames?


LTE-advanced in 3GPP Rel -13/14: an evolution toward 5G. As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.

Question: What is LTE-advanced?


A 5.8-GHz radar sensor chip in 0.18-μm CMOS for non-contact vital sign detection. This paper presents a 5.8-GHz radar sensor chip for non-contact vital sign detection. The sensor chip is designed and fabricated in TSMC 0.18 μm CMOS 1P6M process. Except for the low-noise amplifier, all the active and passive components of the radar system are fully integrated in a single CMOS chip. With packaging on the printed-circuit board and connecting transmitting and receiving antennas, the radar sensor chip has been successfully demonstrated to detect the respiration and heart beat rates of a human adult.

Question: What is a radar sensor chip?


The Connected Car in the Cloud: A Platform for Prototyping Telematics Services. The Connected-Car Prototyping Platform provides both a back end for applications interacting with connected cars and an abstraction of such connected devices for developers. It also provides services such as identity management and data storage. Its main purposes are experimentation, prototyping, evaluation of ideas, and reduction of time-to-market for successful applications.

Question: What is the purpose of the connected car?


Dynamic adaptive streaming over HTTP dataset. The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.

Question: What is a DASH dataset?


A review of wind power and wind speed forecasting methods with different time horizons. In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.

Question: What is the difference between wind power and wind speed forecasting?


Self-perception: An alternative interpretation of cognitive dissonance phenomena.. A theory of self-perception is proposed to provide an alternative interpretation for several of the major phenomena embraced by Festinger's theory of cognitive dissonance and to explicate some of the secondary patterns of data that have appeared in dissonance experiments. It is suggested that the attitude statements which comprise the major dependent variables in dissonance experiments may be regarded as interpersonal judgments in which the observer and the observed happen to be the same individual and that it is unnecessary to postulate an aversive motivational drive toward consistency to account for the attitude change phenomena observed. Supporting experiments are presented, and metatheoretical contrasts between the "radical" behavioral approach utilized and the phenomenological approach typified by dissonance theory are discussed.

Question: What is the difference between the radical behavioral approach and the phenomenological approach?


Modified reduced switch symmetrical multilevel inverter. Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.

Question: What is the difference between a multilevel inverter and a three-level inverter?


Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions¿a different parameterized function for every image of our training set¿whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.

Question: What is the difference between the learning approach in the paper and the approach in the paper?


SINGO: A single-end-operative and genderless connector for self-reconfiguration, self-assembly and self-healing. Flexible and reliable connection is critical for self-reconfiguration, self-assembly, or self-healing. However, most existing connection mechanisms suffer from a deficiency that a connection would seize itself if one end malfunctions or is out of service. To mitigate this limitation on self-healing, this paper presents a new SINGO connector that can establish or disengage a connection even if one end of the connection is not operational. We describe the design and the prototype of the connector and demonstrate its performance by both theoretical analysis and physical experimentations.

Question: What is a self-healing connector?


The description–experience gap in risky choice. According to a common conception in behavioral decision research, two cognitive processes-overestimation and overweighting-operate to increase the impact of rare events on people's choices. Supportive findings stem primarily from investigations in which people learn about options via descriptions thereof. Recently, a number of researchers have begun to investigate risky choice in settings in which people learn about options by experiential sampling over time. This article reviews work across three experiential paradigms. Converging findings show that when people make decisions based on experience, rare events tend to have less impact than they deserve according to their objective probabilities. Striking similarities in human and animal experience-based choices, ways of modeling these choices, and their implications for risk and precautionary behavior are discussed.

Question: What is the description–experience gap in risky choice?


Correlated Nonideal Effects of Dark and Light I–V Characteristics in a-Si/c-Si Heterojunction Solar Cells. a-Si/c-Si (amorphous Silcon/crystalline Silicon) heterojunction solar cells exhibit several distinctive dark and light I-V nonideal features. The dark I-V of these cells exhibits unusually high ideality factors at low forward-bias and the occurrence of a "knee" at medium forward-bias. Nonidealities under illumination, such as the failure of superposition and the occurrence of an "S-type" curve, are also reported in these cells. However, the origin of these nonidealities and how the dark I-V nonidealities manifest themselves under illumination, and vice versa, have not been clearly and consistently explained in the current literature. In this study, a numerical framework is used to interpret the origin of the dark I-V nonidealities, and a novel simulation technique is developed to separate the photo-current and the contact injection current components of the light I-V. Using this technique, the voltage dependence of photo-current is studied to explain the failure of the superposition principle and the origin of the S-type light I-V characteristics. The analysis provides a number of insights into the correlations between the dark I-V and the light I-V. Finally, using the experimental results from this study and from the current literature, it is shown that these nonideal effects indeed affect the dark I-V and the light I-V in a predictable manner.

Question: What is the origin of the dark I-V nonidealities in a-Si/c-Si heterojunction solar cells?


Generalized Denoising Auto-Encoders as Generative Models. Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying datagenerating distribution when the data are discrete, or using other forms of corruption process and reconstruction errors. Another issue is the mathematical justification which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty).

Question: How to sample from a generative model?


A fast image dehazing algorithm based on negative correction.

Question: What is a fast image dehazing algorithm based on negative correction?


Watch Me Code: Programming Mentorship Communities on Twitch.tv. Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.

Question: How does live streaming support learning?


Deep Boltzmann Machines. We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer "pre-training" phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks.

Question: What is a deep Boltzmann machine?


On the Effectiveness of Minimal Context Selection for Robust Question Answering. Machine learning models for question-answering (QA), where given a question 1 and a passage, the learner must select some span in the passage as an answer, are 2 known to be brittle. By inserting a single nuisance sentence into the passage, an 3 adversary can fool the model into selecting the wrong span. A promising new 4 approach for QA decomposes the task into two stages: (i) select relevant sentences 5 from the passage; and (ii) select a span among those sentences. Intuitively, if 6 the sentence selector excludes the offending sentence, then the downstream span 7 selector will be robust. While recent work has hinted at the potential robustness 8 of two-stage QA, these methods have never, to our knowledge, been explicitly 9 combined with adversarial training. This paper offers a thorough empirical in10 vestigation of adversarial robustness, demonstrating that although the two-stage 11 approach lags behind single-stage span selection, adversarial training improves its 12 performance significantly, leading to an improvement of over 22 points in F1 score 13 over the adversarially-trained single-stage model. 14

Question: What is the difference between a single-stage and two-stage QA?


Activities on Facebook Reveal the Depressive State of Users. BACKGROUND As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform's features and users' depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily.   OBJECTIVE Our goal was to develop a Web application and identify depressive symptom-related features from users of Facebook, a popular social networking platform.   METHODS 55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants' responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist.   RESULTS Facebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants' response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03

Question: What is the difference between the number of friends and the number of friends who are depressed?


Face recognition/detection by probabilistic decision-based neural network. This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.

Question: What is probabilistic decision-based neural network?


Threshold selection using Renyi's entropy.

Question: What is the threshold selection using Renyi's entropy?


Bagging predictors. Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.

Question: What is bagging predictors?


On the Design of Adaptive Automation for Complex Systems. This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation.

Question: What is the design of adaptive automation?


Pathophysiology of human visceral obesity: an update.. Excess intra-abdominal adipose tissue accumulation, often termed visceral obesity, is part of a phenotype including dysfunctional subcutaneous adipose tissue expansion and ectopic triglyceride storage closely related to clustering cardiometabolic risk factors. Hypertriglyceridemia; increased free fatty acid availability; adipose tissue release of proinflammatory cytokines; liver insulin resistance and inflammation; increased liver VLDL synthesis and secretion; reduced clearance of triglyceride-rich lipoproteins; presence of small, dense LDL particles; and reduced HDL cholesterol levels are among the many metabolic alterations closely related to this condition. Age, gender, genetics, and ethnicity are broad etiological factors contributing to variation in visceral adipose tissue accumulation. Specific mechanisms responsible for proportionally increased visceral fat storage when facing positive energy balance and weight gain may involve sex hormones, local cortisol production in abdominal adipose tissues, endocannabinoids, growth hormone, and dietary fructose. Physiological characteristics of abdominal adipose tissues such as adipocyte size and number, lipolytic responsiveness, lipid storage capacity, and inflammatory cytokine production are significant correlates and even possible determinants of the increased cardiometabolic risk associated with visceral obesity. Thiazolidinediones, estrogen replacement in postmenopausal women, and testosterone replacement in androgen-deficient men have been shown to favorably modulate body fat distribution and cardiometabolic risk to various degrees. However, some of these therapies must now be considered in the context of their serious side effects. Lifestyle interventions leading to weight loss generally induce preferential mobilization of visceral fat. In clinical practice, measuring waist circumference in addition to the body mass index could be helpful for the identificati

Question: What is the difference between visceral and subcutaneous fat?


Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.

Question: What is a LSTM?


Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography.. OBJECTIVE Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements.   APPROACH Eight human subjects were asked to imagine either 'left' or 'right' hand movement during one recording session to control the computer cursor using TCRE and disc electrodes.   MAIN RESULTS The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%).   SIGNIFICANCE This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

Question: How accurate is the BCI?


Efficient and Robust Automated Machine Learning. The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN.

Question: What is the best way to preprocess data?


Visualization in Meteorology—A Survey of Techniques and Tools for Data Analysis Tasks. This article surveys the history and current state of the art of visualization in meteorology, focusing on visualization techniques and tools used for meteorological data analysis. We examine characteristics of meteorological data and analysis tasks, describe the development of computer graphics methods for visualization in meteorology from the 1960s to today, and visit the state of the art of visualization techniques and tools in operational weather forecasting and atmospheric research. We approach the topic from both the visualization and the meteorological side, showing visualization techniques commonly used in meteorological practice, and surveying recent studies in visualization research aimed at meteorological applications. Our overview covers visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility. We discuss demands and challenges for visualization research targeting meteorological data analysis, highlighting aspects in demonstration of benefit, interactive visual analysis, seamless visualization, ensemble visualization, 3D visualization, and technical issues.

Question: What is the history of visualization in meteorology?


WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER. A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual's (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.

Question: What is job embeddedness?


Football analysis using spatio-temporal tools. Analysing a football match is without doubt an important task for coaches, talent scouts, players and even media; and with current technologies more and more match data is collected. Several companies offer the ability to track the position of the players and the ball with high accuracy and high resolution. They also offer software that include basic analysis tools, for example straight-forward statistics about distance run and number of passes. It is, however, a non-trivial task to perform more advanced analysis. We present a collection of tools that we developed specifically for analysing the performance of football players and teams.

Question: What is a spatio-temporal tool?


Effects of a 7-Day Meditation Retreat on the Brain Function of Meditators and Non-Meditators During an Attention Task. Meditation as a cognitive enhancement technique is of growing interest in the field of health and research on brain function. The Stroop Word-Color Task (SWCT) has been adapted for neuroimaging studies as an interesting paradigm for the understanding of cognitive control mechanisms. Performance in the SWCT requires both attention and impulse control, which is trained in meditation practices. We presented SWCT inside the MRI equipment to measure the performance of meditators compared with non-meditators before and after a meditation retreat. The aim of this study was to evaluate the effects of a 7-day Zen intensive meditation training (a retreat) on meditators and non-meditators in this task on performance level and neural mechanisms. Nineteen meditators and 14 non-meditators were scanned before and after a 7-day Zen meditation retreat. No significant differences were found between meditators and non-meditators in the number of the correct responses and response time (RT) during SWCT before and after the retreat. Probably, due to meditators training in attention, their brain activity in the contrast incongruent > neutral during the SWCT in the anterior cingulate, ventromedial prefrontal cortex/anterior cingulate, caudate/putamen/pallidum/temporal lobe (center), insula/putamen/temporal lobe (right) and posterior cingulate before the retreat, were reduced compared with non-meditators. After the meditation retreat, non-meditators had reduced activation in these regions, becoming similar to meditators before the retreat. This result could be interpreted as an increase in the brain efficiency of non-meditators (less brain activation in attention-related regions and same behavioral response) promoted by their intensive training in meditation in only 7 days. On the other hand, meditators showed an increase in brain activation in these regions after the same

Question: What is the difference between meditators and non-meditators in the Stroop Word-Color Task?


Psychophysiological contributions to phantom limbs.. Recent studies of amputees reveal a remarkable diversity in the qualities of experiences that define the phantom limb, whether painless or painful. This paper selectively reviews evidence of peripheral, central and psychological processes that trigger or modulate a variety of phantom limb experiences. The data show that pain experienced prior to amputation may persist in the form of a somatosensory memory in the phantom limb. It is suggested that the length and size of the phantom limb may be a perceptual marker of the extent to which sensory input from the amputation stump have re-occupied deprived cortical regions originally subserving the amputated limb. A peripheral mechanism involving a sympathetic-efferent somatic-afferent cycle is presented to explain fluctuations in the intensity of paresthesias referred to the phantom limb. While phantom pain and other sensations are frequently triggered by thoughts and feelings, there is no evidence that the painful or painless phantom limb is a symptom of a psychological disorder. It is concluded that the experience of a phantom limb is determined by a complex interaction of inputs from the periphery and widespread regions of the brain subserving sensory, cognitive, and emotional processes.

Question: What is the difference between a phantom limb and a phantom pain?


Adversarial examples: A survey. Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important. A survey of findings connected with adversarial examples is presented and discussed in this paper.

Question: What is an adversarial example?


An ultracapacitor integrated power conditioner for intermittency smoothing and improving power quality of distribution grid. Summary form only given. Penetration of various types of distributed energy resources (DERs) like solar, wind and PHEVs onto the distribution grid is on the rise. There is a corresponding increase in power quality problems and intermittencies on the distribution grid. In order to reduce the intermittencies and improve the power quality of the distribution grid an Ultracapacitor (UCAP) integrated power conditioner is proposed in this paper. UCAP integration gives the power conditioner active power capability which is useful in tackling the grid intermittencies and in improving the voltage sag and swell compensation. UCAPs have low energy density, high power density and fast charge/discharge rates which are all ideal characteristics for meeting high power low energy events like grid intermittencies, sags/swells. In this paper UCAP is integrated into dc-link of the power conditioner through a bi-directional dc-dc converter which helps in providing a stiff dc-link voltage. The integration helps in providing active/reactive power support, intermittency smoothing and sag/swell compensation. Design and control of both the dc-ac inverters and the dc-dc converter are discussed. The simulation model of the overall system is developed and compared to the experimental hardware setup.

Question: What is a dc-dc converter?


Biomedical Image Segmentation Using Fully Convolutional Networks on TrueNorth. With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor — TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.

Question: How to segment glial cells in brain microscopy images?


"Preventative" vs. "Reactive": How Parental Mediation Influences Teens' Social Media Privacy Behaviors. Through an empirical, secondary analysis of 588 teens (ages 12 - 17) and one of their parents living in the USA, we present useful insights into how parental privacy concerns for their teens and different parental mediation strategies (direct intervention versus active mediation) influence teen privacy concerns and privacy risk-taking and risk-coping privacy behaviors in social media. Our results suggest that the use of direct intervention by itself may have a suppressive effect on teens, reducing their exposure to online risks but also their ability to engage with others online and to learn how to effectively cope with online risks. Therefore, it may be beneficial for parents to combine active mediation with direct intervention so that they can protect their teens from severe online risks while empowering teens to engage with others online and learn to make good online privacy choices.

Question: How can parents protect their teens from online risks?


GA3C: GPU-based A3C for Deep Reinforcement Learning. We introduce and analyze the computational aspects of a hybrid CPU/GPU implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. Our analysis concentrates on the critical aspects to leverage the GPU's computational power, including the introduction of a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. We also show the potential for the use of larger DNN models on a GPU. Our TensorFlow implementation achieves a significant speed up compared to our CPU-only implementation, and it will be made publicly available to other researchers.

Question: What is the state-of-the-art method in reinforcement learning for various gaming tasks?


A Taxonomy for Artificial Embryogeny. A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define artificial embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.

Question: What is the difference between artificial embryogeny and artificial life?


Dual-Band and Wideband Dual-Polarized Cylindrical Dielectric Resonator Antennas. This letter investigates dual-band and wideband designs of dual-polarized cylindrical dielectric resonator antennas (DRAs). These designs make use of the fundamental HEM111 mode and higher-order HEM113 mode of the DRA. The strip- and slot-fed excitation methods are used for Ports 1 and 2 of the antennas, respectively. For demonstration, a dual-band dual-polarized DRA for DCS (1.71-1.88 GHz) and WLAN (2.4-2.48 GHz) bands and a wideband version that covers the 2.4-GHz WLAN band were designed. The S-parameters, radiation patterns, antenna gains, antenna efficiencies, and envelope correlations of the two designs are studied. It was found that the dual-band and wideband designs have port isolations of higher than 36 and 37 dB, respectively. Good agreement between the measured and simulated results is observed.

Question: What is the difference between a dual-band and a wideband antenna?


Ping-pong trajectory perception and prediction by a PC based High speed four-camera vision system. A high speed vision system is vital for a robot player to play table tennis successfully. The three dimensional ping-pong trajectory should be perceived in high precision at a high frame rate. What's more, with the beginning parts of the trajectory, the following trajectory should be predicted to save enough time for successful planning and control.

Question: What is a high speed vision system?


Minimum elastic bounding box algorithm for dimension detection of 3D objects: a case of airline baggage measurement. Motivated by the interference of appendages in airline baggage dimension detection using three-dimensional (3D) point cloud, a minimum elastic bounding box (MEBB) algorithm for dimension detection of 3D objects is developed. The baggage dimension measurements using traditional bounding box method or shape fitting method can cause large measurements due to the interference of appendages. Starting from the idea of 'enclosing', an elastic bounding box model with the deformable surface is established. On the basis of using principal component analysis to obtain the main direction of the bounding box, the elastic rules for deformable surfaces are developed so as to produce a large elastic force when it comes into contact with the main body part and to produce a small elastic force when it comes into contact with the appendages part. The airline baggage measurement shows how to use MEBB for dimension detection, especially for the processing of isotropic density distribution, the elasticity computing and the adaptive adjustment of elasticity. Results on typical baggage samples, comparisons to other methods, and error distribution experiments with different algorithm parameters show that the authors' method can reliably obtain the size of the main body part of the object under the interference of appendages.

Question: What is the minimum elastic bounding box algorithm for dimension detection of 3D objects?


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