Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving. To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

Question: How can we improve trust calibration in autonomous driving?


Meta-learning with backpropagation. This paper introduces gradient descent methods applied to meta-leaming (leaming how to leam) in Neural Networks. Meta-leaning has been of interest in the machine leaming field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved leaming algorithms. Many previous neural network-based approaches toward meta-leaming have been based on evolutionary methods. We show how to use gradient descent for meta-leaming in recurrent neural networks. Based on previous work on FixedWeight Leaming Neural Networks, we hypothesize that any recurrent network topology and its corresponding leaming algorithm(s) is a potential meta-leaming system. We tested several recurrent neural network topologies and their corresponding forms of Backpropagation for their ability to meta-leam. One of our systems, based on the Long Short-Term Memory neural network developed a leaming algorithm that could leam any two-dimensional quadratic function (from a set of such functions} after only 30 training examples.

Question: What is meta-learning?


Effects of Platform and Content Attributes on Information Dissemination on We Media: A Case Study on WeChat Platform. The emergence of the WeChat has brought some new changes to people's ways of life and reading. Thanks to the WeChat Public Platform, many people from all walks of life have become We Media operators, creating their own brands and products. In a case study of WeChat Public Platform, this paper uses NLPIR, a tool for Chinese word segmentation, and SPSS, a data analysis software, to conduct an analysis of actual data captured by Web Spider about WeChat posts and the platform itself, exploring the effects of post content and platform attributes on users' reading and dissemination behaviors. Our regression analysis found that some content attributes, including parts of speech like adverb, punctuation marks such as question mark, and headline length and position, as well as platform attributes, such as the platform seniority, have significant impacts on page views; the genres of advertisement, joke and warning, among other content attributes, and follower count as a platform attribute, have a very significant effect on the dissemination index. This paper is helpful for operators to gain a better understanding of factors influencing the spread of information published by WeChat Official Accounts and providing valuable revelations on achieving fast and efficient dissemination of posts.

Question: What is the difference between a platform and a content attribute?


Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. This case study examines 18 elementary school teachers' perceptions of the barriers to technology integration (access, vision, professional development, time, and beliefs) and instructional practices with technology after two years of situated professional development. Months after transitioning from mentoring to teacher-led communities of practice, teachers continued to report positive perceptions of several barriers and were observed engaging in desirable instructional practices. Interviews suggest that the situated professional development activities helped create an environment that supported teachers' decisions to integrate technology. Implications for teacher professional development and technology integration are discussed in conjunction with the strengths and limitations of the study. 2012 Elsevier Ltd. All rights reserved.

Question: What are the barriers to technology integration?


Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach. This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.

Question: How to optimize the operating altitude of an airborne wind energy system?


Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's TENSORFLOW primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge comple-

Question: What is the name of the paper?


An analysis of accelerator coupling in heterogeneous architectures. Existing research on accelerators has emphasized the performance and energy e ciency improvements they can provide, devoting little attention to practical issues such as accelerator invocation and interaction with other on-chip components (e.g. cores, caches). In this paper we present a quantitative study that considers these aspects by implementing seven high-throughput accelerators following three design models: tight coupling behind a CPU, loose out-of-core coupling with Direct Memory Access (DMA) to the LLC, and loose out-of-core coupling with DMA to DRAM. A salient conclusion of our study is that working sets of non-trivial size are best served by loosely-coupled accelerators that integrate private memory blocks tailored to their needs.

Question: What is the best accelerator design model?


Neuroticism as a mediator of treatment response to SSRIs in major depressive disorder.. BACKGROUND Serotonin function has been implicated in both major depressive disorder and neuroticism. In the current investigation, we examined the hypothesis that any change in depression severity is mediated through the reduction of neuroticism, but only for those compounds which target serotonin receptors.   METHODS Ninety-three outpatients in the midst of a major depressive episode received one of three antidepressant medications, classified into two broad types: selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs (i.e. reversible monoamine oxidase inhibitors [RIMAs] and noradrenergic and dopaminergic reuptake blockers [NDMs]). Patients completed the Hamilton Rating Scale for Depression, Beck Depression Inventory II and Revised NEO Personality Inventory prior to and following approximately 16 weeks of treatment. Structural equation modeling was used to test two models: a mediation model, in which neuroticism change is the mechanism by which SSRIs exert a therapeutic effect upon depressive symptoms, and a complication model, in which neuroticism change is a mere epiphenomenon of depression reduction in response to SSRIs.   RESULTS The mediation model provided a good fit to the data; the complication model did not. Patients treated with SSRIs demonstrated greater neuroticism change than those treated with non-SSRIs, and greater neuroticism change was associated with greater depressive symptom change. These effects held for both self-reported and clinician-rated depressive symptom severity.   LIMITATIONS Replication within a randomized control trial with multiple assessment periods is required.   CONCLUSION Neuroticism mediates changes in depression in response to treatment with SSRIs, such that any treatment effect of SSRIs occurs through neuroticism reduction.

Question: What is the difference between a serotonin reuptake inhibitor and a non-serotonin reuptake inhibitor?


Hierarchical Gaussian Descriptors with Application to Person Re-Identification. Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). In this paper, we present novel meta-descriptors based on a hierarchical distribution of pixel features. Although hierarchical covariance descriptors have been successfully applied to image classification, the mean information of pixel features, which is absent from the covariance, tends to be the major discriminative information for person re-id. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, the region is modeled as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, we embed the parameters of the Gaussian into a point of Symmetric Positive Definite (SPD) matrix manifold. By changing the way to handle mean information in this embedding, we develop two hierarchical Gaussian descriptors. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the descriptors. The experimental results conducted on five public datasets indicate that the proposed descriptors achieve remarkably high performance on person re-id.

Question: What is the mean information of a pixel?


Recommendations For Streaming Data. Recommender systems have become increasingly popular in recent years because of the broader popularity of many web-enabled electronic commerce applications. However, most recommender systems today are designed in the context of an offline setting. The online setting is, however, much more challenging because the existing methods do not work very effectively for very large-scale systems. In many applications, it is desirable to provide real-time recommendations in large-scale scenarios. The main problem in applying streaming algorithms for recommendations is that the in-core storage space for memory-resident operations is quite limited. In this paper, we present a probabilistic neighborhood-based algorithm for performing recommendations in real-time. We present experimental results, which show the effectiveness of our approach in comparison to state-of-the-art methods.

Question: What is the difference between a recommender system and a search engine?


Question: What is term rewriting?


Inductance and Resistance Calculations in Three-Dimensional Packaging Using Cylindrical Conduction-Mode Basis Functions. For the successful electrical design of system-in-package, this paper proposes an efficient method for extracting wideband resistance and inductance from a large number of 3-D interconnections. The proposed method uses the modal equivalent network from the electric field integral equation with cylindrical conduction-mode basis function, which reduces the matrix size for large 3-D interconnection problems. Additional enhancement schemes proposed further reduce the cost for computing the partial inductances. Therefore, the method discussed in this paper can be used to construct accurate models of a large number of 3-D interconnection structures such as more than 100 bonding wires used for stacking chips.

Question: How to calculate inductance and resistance of a 3D interconnection?


GUS, A Frame-Driven Dialog System. GUS is the first o f a series o f experimental computer systems that we intend to construct as part o f a program of research on language understanding. In large measure, these systems will fill the role o f periodic progress reports, summarizing what we have learned, assessing the mutual coherence o f the various lines o f investigation we have been following, and saggestin# where more emphasis is needed in future work. GUS (Genial Understander System) is intended to engage a sympathetic and highly cooperative human in an English dialog, directed towards a specific goal within a very restricted domain o f discourse. As a starting point, G US was restricted to the role o f a travel agent in a conversation with a client who wants to make a simple return trip to a single city in California. There is good reason for restricting the domain o f discourse for a computer system which is to engage in an English dialog. Specializing the subject matter that the system can talk about permiis it to achieve some measure o f realism without encompassing all the possibilities o f human knowledge or o f the English language. It also provides the user with specific motivation for participating in the conversation, thus narrowing the range o f expectations that GUS must have about the user's purposes. A system restricted in this way will be more able to guide the conversation within the boundaries o f its competence. 1. Motivation and Design Issues Within its limitations, ous is able to conduct a more-or-less realistic dialog. But the outward behavior of this first system is not what makes it interesting or significant. There are, after all, much more convenient ways to plan a trip and, unlike some other artificial intelligence programs, (;us does not offer services or furnish information that are otherwise difficult or impossible to obtain. The system is i nteresting because of the phenomena of natural dialog that it attempts to model tThis work wa

Question: What is the first large-scale Canadian peacekeeping mission?


Analysis of stairs-climbing ability for a tracked reconfigurable modular robot. Stairs-climbing ability is the crucial performance of mobile robot for urban environment mission such as urban search and rescue or urban reconnaissance. The track type mobile mechanism has been widely applied for its advantages such as high stability, easy to control, low terrain pressure, and continuous drive. Stairs-climbing is a complicated process for a tracked mobile robot under kinematics and dynamics constraints. In this paper, the stairs-climbing process has been divided into riser climbing, riser crossing, and nose line climbing. During each climbing process, robot's mobility has been analyzed for its kinematics and dynamics factor. The track velocity and acceleration's influences on riser climbing have been analyzed. And the semiempirical design method of the track grouser and the module length has been provided in riser crossing and nose line climbing correspondingly. Finally, stairs-climbing experiments have been made on the two-module robot in line type, and three-module robot in line type and in triangle type respectively.

Question: How to climb stairs?


Building a case-based diet recommendation system without a knowledge engineer. We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges th

Question: How to build a case-based diet recommendation system without a knowledge engineer?


Clustering Algorithms Applied in Educational Data Mining. Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

Question: What is the difference between clustering and classification?


It's More than Just Sharing Game Play Videos! Understanding User Motives in Mobile Game Social Media. As mobile gaming has become increasingly popular in recent years, new forms of mobile game social media such as GameDuck that share mobile gameplay videos have emerged. In this work, we set out to understand the user motives of GameDuck by leveraging the Uses and Gratification Theory. We first explore the major motive themes from users' responses (n=138) and generate motivation survey items. We then identify the key motivators by conducting exploratory factor analysis of the survey results (n=354). Finally, we discuss how this new social media relates to existing systems such as Twitch.

Question: What are the major motives of GameDuck?


ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA. This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

Question: What is the radius of a cylinder?


Common Consensus : a web-based game for collecting commonsense goals. In our research on Commonsense reasoning, we have found that an especially important kind of knowledge is knowledge about human goals. Especially when applying Commonsense reasoning to interface agents, we need to recognize goals from user actions (plan recognition), and generate sequences of actions that implement goals (planning). We also often need to answer more general questions about the situations in which goals occur, such as when and where a particular goal might be likely, or how long it is likely to take to achieve. In past work on Commonsense knowledge acquisition, users have been directly asked for such information. Recently, however, another approach has emerged—to entice users into playing games where supplying the knowledge is the means to scoring well in the game, thus motivating the players. This approach has been pioneered by Luis von Ahn and his colleagues, who refer to it as Human Computation. Common Consensus is a fun, self-sustaining web-based game, that both collects and validates Commonsense knowledge about everyday goals. It is based on the structure of the TV game show Family Feud1. A small user study showed that users find the game fun, knowledge quality is very good, and the rate of knowledge collection is rapid. ACM Classification: H.3.3 [INFORMATION STORAGE AND RETRIEVAL]: Information Search and Retrieval; I.2.6 [ARTIFICIAL INTELLIGENCE]: Learning

Question: What is the common consensus?


Structural Embedding of Syntactic Trees for Machine Comprehension. Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.

Question: What is the state-of-the-art neural attention model?


Exosomes in tumor microenvironment: novel transporters and biomarkers. Tumor microenvironment (TME) plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Exosome is an important part of TME. Exosomes are small vesicles formed in vesicular bodies with a diameter of 30–100 nm and a classic "cup" or "dish" morphology. They can contain microRNAs, mRNAs, DNA fragments and proteins, which are shuttled from a donor cell to recipient cells. Exosomes secreted from tumor cells are called tumor-derived (TD) exosomes. There is emerging evidence that TD exosomes can construct a fertile environment to support tumor proliferation, angiogenesis, invasion and premetastatic niche preparation. TD exosomes also may facilitate tumor growth and metastasis by inhibiting immune surveillance and by increasing chemoresistance via removal of chemotherapeutic drugs. Therefore, TD-exosomes might be potential targets for therapeutic interventions via their modification or removal. For example, exosomes can serve as specific delivery vehicles to tumors of drugs, small molecules, or agents of prevention and gene therapy. Furthermore, the biomarkers detected in exosomes of biological fluids imply a potential for exosomes in the early detection and diagnosis, prediction of therapeutic efficacy, and determining prognosis of cancer. Although exosomes may serve as cancer biomarkers and aid in the treatment of cancer, we have a long way to go before we can further enhance the anti-tumor therapy of exosomes and develop exosome-based cancer diagnostic and therapeutic strategies.

Question: What is the difference between tumor-derived exosomes and normal exosomes?


Speech emotion recognition using hidden Markov models. In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is proposed. The proposed method makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier. The emotions are classified into six categories. The category labels used are, the archetypal emotions of Anger, Disgust, Fear, Joy, Sadness and Surprise. Adatabase consisting of 60 emotional utterances, each from twelve speakers is constructed and used to train and test the proposed system. Performance of the LFPC feature parameters is compared with that of the linear prediction Cepstral coefficients (LPCC) and mel-frequency Cepstral coefficients (MFCC) feature parameters commonly used in speech recognition systems. Results show that the proposed system yields an average accuracy of 78% and the best accuracy of 96% in the classification of six emotions. This is beyond the 17% chances by a random hit for a sample set of 6 categories. Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters. 2003 Elsevier B.V. All rights reserved.

Question: How does the proposed system perform?


Improvements in children's speech recognition performance. There are several reasons why conventional speech recognition systems modeled on adult data fail to perform satisfactorily on children's speech input. For instance, children's vocal characteristics differ signi cantly from those of adults. In addition, their choices of vocabulary and sentence construction modalities usually do not conform to adult patterns. We describe comparative studies demonstrating the performance gain realized by adopting to children's acoustic and language model data to construct a children's speech recognition system.

Question: How do children's speech characteristics differ from adults?


Images Features Extraction of Tobacco Leaves. Images features extraction is very important for the grading process of flue-cured tobacco leaves. In this paper, a machine vision techniques base system is proposed for the automatic inspection of flue-cured tobacco leaves. Machine vision techniques are used in this system to solve problems of features extraction and analysis of tobacco leaves, which include features of color, size, shape andsurface texture. The experimental results show that this system is a viable way for the features extraction of tobacco leaves, and can be used for the automatic classification of tobacco leaves.

Question: What is the size of the canadian military?


A cascade filter for pulse wave baseline drift elimination. Analyzing the pulse wave is an important method to diagnosis the status of human health. Generally the directly acquired signal contains varying degrees of baseline drift which is usually caused by breathing and physical movement, it mostly influences the accuracy of signal analysis and must be eliminated. This article presented a cascade filter framework to remove the baseline drift of pulse wave. The first stage zero phase filter (ZPF) roughly reduced the trend of the baseline drift. The second stage of the removed method considered the signal features. Through the pulse waveform reference points' accurate location and cubic spline estimation, we completely eliminated the rest drift. Compared with the other methods, e.g. wavelet-based filtering, morphological filtering, empirical mode decomposition (EMD) filtering, the experimental result showed that the proposed method had not only better effect, but also small calculation cost, and the scheme was more suitable for actual application.

Question: What is the difference between the proposed method and the other methods?


Challenges of Computational Processing of Code-Switching. This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed. It refers to code-switching which has become more popular in our daily life and therefore obtains an increasing amount of attention from the research community. We report our experience that covers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency parsing but also more downstream ones such as machine translation and automatic speech recognition. We highlight and discuss the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work.

Question: What is the key problem of code-switching?


Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering. Why-question answering (why-QA) is a task to retrieve answers (or answer passages) to why-questions (e.g., "why are tsunamis generated?") from a text archive. Several previously proposed methods for why-QA improved their performance by automatically recognizing causalities that are expressed with such explicit cues as "because" in answer passages and using the recognized causalities as a clue for finding proper answers. However, in answer passages, causalities might be implicitly expressed, (i.e., without any explicit cues): "An earthquake suddenly displaced sea water and a tsunami was generated." The previous works did not deal with such implicitly expressed causalities and failed to find proper answers that included the causalities. We improve why-QA based on the following two ideas. First, implicitly expressed causalities in one text might be expressed in other texts with explicit cues. If we can automatically recognize such explicitly expressed causalities from a text archive and use them to complement the implicitly expressed causalities in an answer passage, we can improve why-QA. Second, the causes of similar events tend to be described with a similar set of words (e.g., "seismic energy" and "tectonic plates" for "the Great East Japan Earthquake" and "the 1906 San Francisco Earthquake"). As such, even if we cannot find in a text archive any explicitly expressed cause of an event (e.g., "the Great East Japan Earthquake") expressed in a question (e.g., "Why did the Great East Japan earthquake happen?"), we might be able to identify its implicitly expressed causes with a set of words (e.g., "tectonic plates") that appear in the explicitly expressed cause of a similar event (e.g., "the 1906 San Francisco Earthquake").  We implemented these two ideas in our multi-column convolutional neural networks with a novel attention mechanism, which we call causality attention. Th

Question: What is the difference between a convolutional neural network and a multi-column convolutional neural network?


Anomaly Detection in Video Surveillance via Gaussian Process. In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze di®erent anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce

Question: What is anomaly detection in video surveillance?


Hierarchical organization versus self-organization. In this paper the difference between hierarchical organization and selforganization is investigated. Organization is defined as a structure with a function. But how does the structure affect the function? I will start to examine this by doing two simulations. The idea is to have a given network of agents, which influence their neighbors. How the result differs in three different types of networks, is then explored. In the first simulation, agents try to align with their neighbors. The second simulation is inspired by the ecosystem. Agents take certain products from their neighbors, and transform them into products their neighbors can use.

Question: What is the difference between hierarchical organization and self-organization?


An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing. Collaborative filtering is one of the most important technologies in electronic commerce. With the development of recommender systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users' ratings is the major reason causing the poor quality. To address this issue, an item-based collaborative filtering recommendation algorithm using slope one scheme smoothing is presented. This approach predicts item ratings that users have not rated by the employ of slope one scheme, and then uses Pearson correlation similarity measurement to find the target items' neighbors, lastly produces the recommendations. The experiments are made on a common data set using different recommender algorithms. The results show that the proposed approach can improve the accuracy of the collaborative filtering recommender system.

Question: What is the difference between collaborative filtering and content-based filtering?


The evolution of synthetic aperture radar systems and their progression to the EOS SAR. The spaceborne imaging radar program of the National Aeronautics and Space Administration (NASA) has evolved primarily through Seasat and the Shuttle Imaging Radar (SIR) to a multifrequency, multipolarization system capable of monitoring the Earth with a variety of imaging geometries, resolutions, and swaths. In particular, the ability to digitally process and calibrate the data has been a key factor in developing the algorithms which will operationally provide geophysical and biophysical information about the surface of the Earth using synthetic aperture radar (SAR). This paper describes the evolution of the spaceborne imaging radar starting with the Seasat SAR, through the SIR-A, SIR-B, and SIR-CI X-SAR missions, to the EOS SAR which is scheduled for launch as part of the Earth Observing System (EOS) at the end of the 1990s. A summary of the planned international missions, which may produce a permanent active microwave capability in space starting as early as 1991, is also presented, along with a description of the airborne systems which will be essential to the algorithm development and long-term calibration of the spaceborne data. Finally, a brief summary of the planetary missions utilizing SAR and a comparison of their imaging capabilities with those available on Earth is presented.

Question: What is the evolution of synthetic aperture radar systems and their progression to the EOS SAR?


A Practical Approach to Recognizing Physical Activities. We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.

Question: What is the accuracy rate of the system?


Real-time capable methods to determine the magnet temperature of permanent magnet synchronous motors — A review. The permanent magnet synchronous motor (PMSM) is widely used in highly utilised automotive traction drives and other industrial applications. With regards to the device life-time, safe operation and control performance, the magnet temperature is of great interest. Since a direct magnet temperature measurement is not feasible in most cases, this contribution gives a review on state-of-the-art model-based magnet temperature determination methods in PMSM. In this context, the existing publications can be classified into thermal models, flux observers and voltage signal injection approaches. Firstly, brief introductions of these methods are given, followed by a direct comparison regarding drawbacks and benefits. Finally, this contribution concludes with an outlook of potential further investigations in this research field.

Question: What is the magnet temperature of a permanent magnet synchronous motor?


ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI. BACKGROUND There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans.   OBJECTIVE To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images.   METHODS We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML.   RESULTS Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively.   CONCLUSION ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.

Question: What is the difference between a bright and dark lesion?


Evaluation of games for teaching computer science. Well-designed educational games (both online and offline) can be a valuable way to engage students with a topic. However, games can also be a time-consuming distraction, so it is useful to be able to identify which are suitable. This paper provides guidelines for evaluating games, and applies them to identify promising games. We begin by defining what is meant by a game, ruling out simple animations or quizzes, and focusing on a more playful approach to education. Based on this definition, we have identified games that are available for teaching topics in computer science, and classified them. We have developed criteria for evaluating games based on existing research on educational games, combined with educational theory and considering them in the context of CS. The criteria developed cover the topics taught, how easy it is to install and run, how engaging the game is, and how much time students might invest to use the game. We find that although there are a number of worth-while games in the field of CS and most are free, the coverage of topics is very patchy, with most focused on programming concepts and binary numbers.

Question: What is the difference between a game and a quiz?


Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification. In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

Question: How to learn a visual representation from raw spatiotemporal signals in videos?


A Face in any Form: New Challenges and Opportunities for Face Recognition Technology. Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.

Question: What is the largest face recognition database?


An Empirical Investigation into Programming Language Syntax. Recent studies in the literature have shown that syntax remains a significant barrier to novice computer science students in the field. While this syntax barrier is known to exist, whether and how it varies across programming languages has not been carefully investigated. For this article, we conducted four empirical studies on programming language syntax as part of a larger analysis into the, so called, programming language wars. We first present two surveys conducted with students on the intuitiveness of syntax, which we used to garner formative clues on what words and symbols might be easy for novices to understand. We followed up with two studies on the accuracy rates of novices using a total of six programming languages: Ruby, Java, Perl, Python, Randomo, and Quorum. Randomo was designed by randomly choosing some keywords from the ASCII table (a metaphorical placebo). To our surprise, we found that languages using a more traditional C-style syntax (both Perl and Java) did not afford accuracy rates significantly higher than a language with randomly generated keywords, but that languages which deviate (Quorum, Python, and Ruby) did. These results, including the specifics of syntax that are particularly problematic for novices, may help teachers of introductory programming courses in choosing appropriate first languages and in helping students to overcome the challenges they face with syntax.

Question: What programming languages are easy for novices to understand?


O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs. Bitcoin is a rising digital currency and exemplifies the growing need for systematically gathering and analyzing public transaction data sets such as the blockchain. However, the blockchain in its raw form is just a large ledger listing transfers of currency units between alphanumeric character strings, without revealing contextually relevant real-world information. In this demo, we present GraphSense, which is a solution that applies a graph-centric perspective on digital currency transactions. It allows users to explore transactions and follow the money flow, facilitates analytics by semantically enriching the transaction graph, supports path and graph pattern search, and guides analysts to anomalous data points. To deal with the growing volume and velocity of transaction data, we implemented our solution on a horizontally scalable data processing and analytics infrastructure. Given the ongoing digital transformation in financial services and technologies, we believe that our approach contributes to development of analytics solutions for digital currency ecosystems, which is relevant in fields such as financial analytics, law enforcement, or scientific research.

Question: What is the largest bitcoin transaction?


Sufficient Sample Sizes for Multilevel Modeling. An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors. In addition, the influence of other factors, such as the lowest-level sample size and different variance distributions between the levels (different intraclass correlations), is examined. The results show that only a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second-level standard errors. In all of the other simulated conditions the estimates of the regression coefficients, the variance components, and the standard errors are unbiased and accurate.

Question: What is the minimum sample size for multilevel modeling?


FTP-SC: Fuzzy Topology Preserving Stroke Correspondence. Stroke correspondence construction is a precondition for vectorized 2D animation inbetweening and remains a challenging problem. This paper introduces the FTP-SC, a fuzzy topology preserving stroke correspondence technique, which is accurate and provides the user more effective control on the correspondence result than previous matching approaches. The method employs a two-stage scheme to progressively establish the stroke correspondence construction between the keyframes. In the first stage, the stroke correspondences with high confidence are constructed by enforcing the preservation of the so-called "fuzzy topology" which encodes intrinsic connectivity among the neighboring strokes. Starting with the high-confidence correspondences, the second stage performs a greedy matching algorithm to generate a full correspondence between the strokes. Experimental results show that the FTP-SC outperforms the existing approaches and can establish the stroke correspondence with a reasonable amount of user interaction even for keyframes with large geometric and spatial variations between strokes. CCS Concepts •Computing methodologies → Animation; Parametric curve and surface models;

Question: What is the fuzzy topology preserving stroke correspondence?


Time Series Prediction: Forecasting the Future and Understanding the Past. Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading time series prediction forecasting the future and understanding the past, you can take more advantages with limited budget.

Question: What is time series prediction?


Compact ultra-wide stopband lowpass filter using transformed stepped impedance hairpin resonator. A compact microstrip lowpass filter (LPF) with ultra-wide stopband using transformed stepped impedance hairpin resonator is proposed. The transformed resonator consists of a stepped impedance hairpin resonator and an embedded hexagon stub loaded coupled-line structure. Without enlarging the size, the embedded structure is introduced to get a broad stopband. A prototype LPF has been simulated, fabricated and measured, and the measurements are in good agreement with simulations. The implemented lowpass filter exhibits an ultra-wide stopband up to 12.01fc with a rejection level of 14 dB. In addition, the proposed filter features a size of 0.071λg× 0.103λg, where λg is the waveguide length at the cutoff frequency 1.45 GHz.

Question: What is a compact microstrip lowpass filter?


Photoplethysmography and its detailed pulse waveform analysis for arterial stiffness. Arterial stiffness index is one of the biomechanical indices of vascular healthiness. These indexes are based on detailed pulse waveform analysis which is presented here. After photoplethysmographyic (PPG) pulse wave measurement, we decompose the pulse waveform for the estimation and determination of arterial elasticity. Firstly, it is electro-optically measured PPG signal and by electromechanical film (EMFi) measured signal that are analyzed and investigated by dividing each wave into five logarithmic normal function components. For both the PPG and EMFi waveform we can find very easily a good fit between the original and overlapped and summed wave components. Each wave component is assumed to resemble certain phenomenon in the arteries and certain indexes can be calculated for example based on the mutual timing of the components. Several studies have demonstrated that these kinds of indexes calculated based on actual biomechanical processed can predict future cardiovascular events. Many dynamic factors, e.g., arterial stiffness, depend on fixed structural features of the arterial wall. For more accurate description, arterial stiffness is estimated based on pulse wave decomposition analysis in the radial measured by EMFi and PPG method and tibial arterial walls measured by PPG method parallelly. Elucidation of the precise relationship between endothelial function and arterial stiffness can be done through biomechanics. However, arterial wall elasticity awaits still further biomechanical studies with clinical relations and the influence of arterial flexibility, resistance and ageing inside of the radial pulse waveform.

Question: What is the difference between photoplethysmography and pulse wave analysis?


Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?. We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existenc

Question: How to maximize the spread of products in a network?


Gaze tracking by Binocular Vision and LBP features. In this paper, a new method for eye gaze tracking is proposed under natural head movement. In this method, Local-Binary-Pattern Texture Feature (LBP) is adopted to calculate the eye features according to the characteristic of the eye, and a precise Binocular Vision approach is used to detect the space coordinate of the eye. The combined features of space coordinates and LBP features of the eyes are fed into Support Vector Regression (SVR) to match the gaze mapping function, in the hope of tracking gaze direction under natural head movement. The experimental results prove that the proposed method can determine the gaze direction accurately.

Question: What is the gaze tracking method?


An Artificial Light Source Influences Mating and Oviposition of Black Soldier Flies, Hermetia illucens. Current methods for mass-rearing black soldier flies, Hermetia illucens (L.) (Diptera: Stratiomyidae), in the laboratory are dependent on sunlight. Quartz-iodine lamps and rare earth lamps were examined as artificial light sources for stimulating H. illucens to mate and lay eggs. Sunlight was used as the control. Adults in the quartz-iodine lamp treatment had a mating rate of 61% of those in the sunlight control. No mating occurred when the rare earth lamp was used as a substitute. Egg hatch for the quartz-iodine lamp and sunlight treatments occurred in approximately 4 days, and the hatch rate was similar between these two treatments. Larval and pupal development under these treatments required approximately 18 and 15 days at 28°C, respectively. Development of methods for mass rearing of H. illucens using artificial light will enable production of this fly throughout the year without investing in greenhouse space or requiring sunlight.

Question: How does artificial light influence mating and oviposition of black soldier flies?


WiFi RSS fingerprinting indoor localization for mobile devices. While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. An offline RSS fingerprint of the environment is compared with an online RSS measurement to estimate the location of a user. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device. Experimental results in the considered environment validate the approach for both architectures.

Question: How to build a radio map for indoor localization?


Attitudes -1 The Construction of Attitudes. Attitudes have long been considered a central concept of social psychology. In fact, early writers have defined social psychology as the scientific study of attitudes (e.g., Thomas & Znaniecki, 1918) and in 1954 Gordon Allport noted, "This concept is probably the most distinctive and indispensable concept in contemporary American social psychology" (p. 43). As one may expect of any concept that has received decades of attention, the concept of attitudes has changed over the years (see Allport, 1954, for an early review). The initial definitions were broad and encompassed cognitive, affective, motivational, and behavioral components. For example, Allport (1935) defined an attitude as "a mental and neural state of readiness, organized through experience, exerting a directive and dynamic influence upon the individual's response to all objects and situations with which it is related" (p. 810). A decade later, Krech and Crutchfield (1948) wrote, "An attitude can be defined as an enduring organization of motivational, emotional, perceptual, and cognitive processes with respect to some aspect of the individual's world" (p. 152). These definitions emphasized the enduring nature of attitudes and their close relationship to individuals' defined attitudes simply in terms of the probability that a person will show a specified behavior in a specified situation. In subsequent decades, the attitude concept lost much of its breadth and was largely reduced to its evaluative component. In the succinct words of Daryl Bem, "Attitudes are likes and dislikes" attitudes as "a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor" (p. 1). Along the way, many functions that were initially ascribed to attitudes have been reassigned to other cognitive structures and the accumulating body of empirical findings drew many of the classic assumptions into question. A growing body of literature su

Question: What is the difference between an attitude and a belief?


Open innovation : State of the art and future perspectives. Open innovation has become one of the hottest topics in innovation management. This article intends to explore the limits in our understanding of the open innovation concept. In doing so, I address the questions of what (the content of open innovation), when (the context dependency) and how (the process). Open innovation is a rich concept, that can be implemented in many different ways. The context dependency of open innovation is one of the least understood topics; more research is needed on the internal and external environment characteristics affecting performance. The open innovation process relates to both the transition towards open innovation, and the various open innovation practices. As with any new concept, initial studies focus on successful and early adopters, are based on case studies, and descriptive. However, not all lessons learned from the early adopters may be applicable to following firms. Case study research increases our understanding of how things work and enables us to identify important phenomena. They should be followed by quantitative studies involving large samples to determine the relative importance of factors, to build path models to understand chains of effects, and to formally test for context dependencies. However, the evidence shows that open innovation has been a valuable concept for so many firms and in so many contexts, that it is on its way to find its final place in innovation management. & 2010 Elsevier Ltd. All rights reserved.

Question: What is the difference between open innovation and innovation management?


Face Alignment With Deep Regression. In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while the following local layers iteratively update the shape with local image observations. Combining standard derivations and numerical approximations, we make all layers able to backpropagate error differentials, so that we can apply the standard backpropagation to jointly learn the parameters from all layers. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency that often occurs in the cascaded regression methods and deteriorates the overall performance: yielding early stage regressors with high alignment accuracy gains but later stage regressors with low alignment accuracy gains. Experimental results on standard benchmarks demonstrate that our approach brings significant improvements over previous cascaded regression algorithms.

Question: How does face alignment work?


Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions. Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.

Question: What is the diagnosis of road traffic congestions?


An extended online purchase intention model for middle-aged online users. This article focuses on examining the determinants and mediators of the purchase intention of nononline purchasers between ages 31 and 60 who mostly have strong purchasing power. It propose anew online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. Based on a sample of more than 300 middle-aged non-online purchasers, beyond some situationally-specific predictor variables, online purchasing attitude and habitual online usage are key mediators. Personal awareness of security only affects habitual online usage, t indicating a concern of middle-aged users. Habitual online usage is a

Question: What is the extended online purchase intention model?


Yet Another Visit to Paxos. This paper presents a modular introduction to crash-tolerant and Byzantine-tolerant protocols for reaching consensus that use the method introduced by the Paxos algorithm of Lamport and by the viewstamped replication algorithm of Oki and Liskov. The consensus protocol runs a sequence of epoch abstractions as governed by an epoch-change abstraction. Implementations of epoch and epoch-change that tolerate crash faults yield the consensus algorithm in Paxos and in viewstamped replication. Implementations of epoch and epoch-change that tolerate Byzantine faults yield the consensus algorithm in the PBFT protocol of Castro and Liskov.

Question: What is the consensus algorithm in Paxos?


HCI and Autonomous Vehicles: Contextual Experience Informs Design. The interaction between drivers and their cars will change significantly with the introduction of autonomous vehicles. The driver's role will shift towards a supervisory control of their autonomous vehicle. The eventual relief from the driving task enables a complete new area of research and practice in human-computer interaction and interaction design. In this one-day workshop, participants will explore the opportunities the design space of autonomous driving will bring to HCI researchers and designers. On the day before workshop participants are invited to visit (together with workshop organizers) Google Partnerplex and Stanford University. At Google participants will have the opportunity to explore Google's autonomous car simulator and might have the chance to experience one of the Google Cars (if available). At Stanford participants are invited to ride in a Wizard-of-Oz autonomous vehicle. Based on this first-hand experience we will discuss design approaches and prototype interaction systems during the next day's workshop. The outcome of this workshop will be a set of concepts, interaction sketches, and low-fidelity paper prototypes that address constraints and potentials of driving in an autonomous car.

Question: What is the difference between a human-computer interaction and an interaction design?


Ontology and the Lexicon. A lexicon is a linguistic object and hence is not the same thing as an ontology, which is non-linguistic. Nonetheless, word senses are in many ways similar to ontological concepts and the relationships found between word senses resemble the relationships found between concepts. Although the arbitrary and semi-arbitrary distinctions made by natural languages limit the degree to which these similarities can be exploited, a lexicon can nonetheless serve in the development of an ontology, especially in a technical domain.

Question: What is a lexicon?


Finite Precision Error Analysis of Neural Network Hardware Implementations. 29 computation. On the other hand, for network learning, at least 14-16 bits of precision must be used for the weights to avoid having the training process divert too much from the trajectory of the high precision computation. References [1] D. Hammerstrom. A VLSI architecture for high-performance, low cost, on-chip learning. Figure 10: The average squared dierences between the desired and actual outputs of the XOR problem after the network converges. The paper is devoted to the derivation of the nite precision error analysis techniques for neural network implementations, especially analysis of the back-propagation learning of MLP's. This analysis technique is proposed to be more versatile and to prepare the ground for a wider variety of neural network algorithms: recurrent neural networks, competitive learning networks, and etc. All these networks share similar computational mechanisms as those used in back-propagation learning. For the forward retrieving operations, it is shown that 8-bit weights are sucient to maintain the same performance as using high precision 27 ture of the XOR problem, a soft convergence is good enough for the termination of training. Therefore, at the predicted point of 12-13 bits of weights, the squared dierence curve dives. Another interesting observation is worthwhile to mention: the total nite precision error in a single iteration of weight updating is mainly generated in the nal jamming operators in the computation of the output delta, hidden delta, and weight update. Therefore, even though it is required to have at least 13 to 16 bits assigned to the computation of the weight update and stored as the total weight value, the number of weight bits in the computation of forward retrieving and hidden delta steps of learning can be as low as 8 bits without excessive degradation of learning convergence and accuracy.

Question: What is the average squared dierence between the desired and actual outputs of the XOR problem after the network converges?


Synthesizing Training Images for Boosting Human 3D Pose Estimation. Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.

Question: How to synthesize training images for 3D pose estimation?


Negotiation as a Metaphor for Distributed Problem Solving. We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-n, and PUP6 are used to demonstrate that negotiation--the two-way transfer of information--is a natural extension to the transfer of control mechanisms used in earlier problem-

Question: What is the difference between negotiation and distributed problem solving?


Efficient Deep Learning on Multi-Source Private Data. Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.

Question: How to train a machine learning model without revealing the training data?


Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge. Modern microprocessors are evolving into system-on-a-chip designs with high integration levels, catering to ever-shrinking form factors. Portability without compromising performance is a driving market need. An architectural approach that's adaptive to and cognizant of workload behavior and platform physical constraints is indispensable to meeting these performance and efficiency goals. This article describes power-management innovations introduced on Intel's Sandy Bridge microprocessor.

Question: What is the power-management architecture of the intel microarchitecture code-named sandy bridge?


Full-resolution interactive CPU volume rendering with coherent BVH traversal. We present an efficient method for volume rendering by raycasting on the CPU. We employ coherent packet traversal of an implicit bounding volume hierarchy, heuristically pruned using preintegrated transfer functions, to exploit empty or homogeneous space. We also detail SIMD optimizations for volumetric integration, trilinear interpolation, and gradient lighting. The resulting system performs well on low-end and laptop hardware, and can outperform out-of-core GPU methods by orders of magnitude when rendering large volumes without level-of-detail (LOD) on a workstation. We show that, while slower than GPU methods for low-resolution volumes, an optimized CPU renderer does not require LOD to achieve interactive performance on large data sets.

Question: How to render a large volume on a laptop?


A Storage Solution for Massive IoT Data Based on NoSQL. Storage is an important research direction of the data management of the Internet of Things. Massive and heterogeneous data of the Internet of Things brings the storage huge challenges. Based on the analysis of the IoT data characteristics, this paper proposed a storage management solution called IOTMDB based on NoSQL as current storage solutions are not well support storing massive and heterogeneous IoT data. Besides, the storage strategies about expressing and organizing IoT data in a uniform manner were proposed, some evaluations were carried out. Furthermore, we not only just concerned about descripting the data itself, but also cared for sharing of the data, so a data sharing mechanism based on ontology was proposed. Finally, index was studied and a set of query syntaxes to meet the needs of different kinds of IoT queries based NoSQL was given.

Question: What is the storage solution for massive IoT data based on NoSQL?


Screening for trisomy 18 by maternal age, fetal nuchal translucency, free beta-human chorionic gonadotropin and pregnancy-associated plasma protein-A.. OBJECTIVES To derive a model and examine the performance of first-trimester screening for trisomy 18 by maternal age, fetal nuchal translucency (NT) thickness, and maternal serum free beta-human chorionic gonadotropin (beta-hCG) and pregnancy-associated plasma protein-A (PAPP-A).   METHODS Prospective combined screening for trisomy 21 was performed at 11 + 0 to 13 + 6 weeks in 56 893 singleton pregnancies, including 56 376 cases of euploid fetuses, 395 with trisomy 21 and 122 with trisomy 18. The measured free beta-hCG and PAPP-A were converted into a multiple of the median (MoM) and then into likelihood ratios (LR). Similarly, the measured NT was transformed into LRs using the mixture model of NT distributions. In each case the LRs for NT and the biochemical markers were multiplied by the age and gestation-related risk to derive the risk for trisomy 21 and trisomy 18. Detection rates (DRs) and false-positive rates (FPRs) were calculated by taking the proportions with risks above a given risk threshold.   RESULTS In screening with the algorithm for trisomy 21, at a FPR of 3%, the estimated DRs of trisomies 21 and 18 were 89% and 82%, respectively. The use of an algorithm for trisomy 18 identified 93% of affected fetuses at a FPR of 0.2%. When the algorithm for trisomy 21 was used and screen positivity was fixed at a FPR of 3%, and in addition the algorithm for trisomy 18 was used and screen positivity was fixed at a FPR of 0.2%, the overall FPR was 3.1% and the DRs of trisomies 21 and 18 were 90% and 97%, respectively.   CONCLUSIONS A beneficial side effect of first-trimester combined screening for trisomy 21 is the detection of a high proportion of fetuses with trisomy 18. If an algorithm for trisomy 18 in addition to the one for trisomy 21 is used, more than 95% of trisomy 18 fetuses can be detected with a minor in

Question: What is the difference between trisomy 18 and trisomy 21?


Recent advances in image processing techniques for automated harvesting purposes: A review. Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.

Question: What is the purpose of this survey?


Repeatable Reverse Engineering with PANDA. We present PANDA, an open-source tool that has been purpose-built to support whole system reverse engineering. It is built upon the QEMU whole system emulator, and so analyses have access to all code executing in the guest and all data. PANDA adds the ability to record and replay executions, enabling iterative, deep, whole system analyses. Further, the replay log files are compact and shareable, allowing for repeatable experiments. A nine billion instruction boot of FreeBSD, e.g., is represented by only a few hundred MB. PANDA leverages QEMU's support of thirteen different CPU architectures to make analyses of those diverse instruction sets possible within the LLVM IR. In this way, PANDA can have a single dynamic taint analysis, for example, that precisely supports many CPUs. PANDA analyses are written in a simple plugin architecture which includes a mechanism to share functionality between plugins, increasing analysis code re-use and simplifying complex analysis development. We demonstrate PANDA's effectiveness via a number of use cases, including enabling an old but legitimately purchased game to run despite a lost CD key, in-depth diagnosis of an Internet Explorer crash, and uncovering the censorship activities and mechanisms of an IM client.

Question: What is a whole system reverse engineering?


Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.

Question: What is the difference between a distributed neuronal assembly and a distributed neuronal network?


Accelerating ODE-Based Simulation of General and Heterogeneous Biophysical Models Using a GPU. Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.

Question: How to accelerate ODE-based simulation of general and heterogeneous biophysical models using a GPU?


A hybrid SoC interconnect with dynamic TDMA-based transaction-less buses and on-chip networks. The two dominant architectural choices for implementing efficient communication fabrics for SoC's have been transaction-based buses and packet-based networks-on-chip (NoC). Both implementations have some inherent disadvantages - the former resulting from poor scalability and the transactional character of their operation, and the latter from inconsistent access times and deterioration of performance at high injection rates. In this paper, we propose a transaction-less, time-division-based bus architecture, which dynamically allocates timeslots on-the-fly - the dTDMA bus. This architecture addresses the contention issues of current bus architectures, while avoiding the multi-hop overhead of NoC's. It is compared to traditional bus architectures and NoC's and shown to outperform both for configurations with fewer than 10 PE's. In order to exploit the advantages of the dTDMA bus for smaller configurations, and the scalability of NoC's, we propose a new hybrid SoC interconnect combining the two, showing significant improvement in both latency and power consumption.

Question: What is a hybrid SoC interconnect?


A study of near-field direct antenna modulation systems using convex optimization. This paper studies the constellation diagram design for a class of communication systems known as near-field direct antenna modulation (NFDAM) systems. The modulation is carried out in a NFDAM system by means of a control unit that switches among a number of pre-designed passive controllers such that each controller generates a desired voltage signal at the far field. To find an optimal number of signals that can be transmitted and demodulated reliably in a NFDAM system, the coverage area of the signal at the far field should be identified. It is shown that this coverage area is a planar convex region in general and simply a circle in the case when no constraints are imposed on the input impedance of the antenna and the voltage received at the far field. A convex optimization method is then proposed to find a polygon that is able to approximate the coverage area of the signal constellation diagram satisfactorily. A similar analysis is provided for the identification of the coverage area of the antenna input impedance, which is beneficial for designing an energy-efficient NFDAM system.

Question: What is a convex optimization method?


Recent Advancements in Event Processing. Event processing (EP) is a data processing technology that conducts online processing of event information. In this survey, we summarize the latest cutting-edge work done on EP from both industrial and academic research community viewpoints. We divide the entire field of EP into three subareas: EP system architectures, EP use cases, and EP open research topics. Then we deep dive into the details of each subsection. We investigate the system architecture characteristics of novel EP platforms, such as Apache Storm, Apache Spark, and Apache Flink. We found significant advancements made on novel application areas, such as the Internet of Things; streaming machine learning (ML); and processing of complex data types such as text, video data streams, and graphs. Furthermore, there has been significant body of contributions made on event ordering, system scalability, development of EP languages and exploration of use of heterogeneous devices for EP, which we investigate in the latter half of this article. Through our study, we found key areas that require significant attention from the EP community, such as Streaming ML, EP system benchmarking, and graph stream processing.

Question: What is the latest cutting-edge work done on event processing?


Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition. Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set.

Question: What is the difference between feature-based and holistic face recognition algorithms?


Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks. This paper presents a novel unsupervised approach to detect cyber attacks in Cyber-Physical Systems (CPS). We describe an unsupervised learning approach using a Recurrent Neural network which is a time series predictor as our model. We then use the Cumulative Sum method to identify anomalies in a replicate of a water treatment plant. The proposed method not only detects anomalies in the CPS but also identifies the sensor that was attacked. The experiments were performed on a complex dataset which is collected through a Secure Water Treatment Testbed (SWaT). Through the experiments, we show that the proposed technique is able to detect majority of the attacks designed by our research team with low false positive rates.

Question: What is a cyber physical system?


Anomaly detection approach using hybrid algorithm of data mining technique. The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This paper proposes a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization (SMO) classification. It introduces hybrid approach that able to reduce the rate of false positive alarm, false negative alarm rate, to improve the detection rate and detect zero-day attackers. The NSL-KDD dataset has been used in the proposed technique.. The classification has been performed by using Sequential Minimal Optimization. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique (K-mean + SMO) has achieved a positive detection rate of (94.48%) and reduce the false alarm rate to (1.2%) and achieved accuracy of (97.3695%).

Question: What is the hybrid machine learning technique?


The motivations and experiences of the on-demand mobile workforce. On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.

Question: What are the main drivers for participating in an on-demand mobile workforce?


The neural basis of intuitive best next-move generation in board game experts.. The superior capability of cognitive experts largely depends on quick automatic processes. To reveal their neural bases, we used functional magnetic resonance imaging to study brain activity of professional and amateur players in a board game named shogi. We found two activations specific to professionals: one in the precuneus of the parietal lobe during perception of board patterns, and the other in the caudate nucleus of the basal ganglia during quick generation of the best next move. Activities at these two sites covaried in relevant tasks. These results suggest that the precuneus-caudate circuit implements the automatic, yet complicated, processes of board-pattern perception and next-move generation in board game experts.

Question: What is the neural basis of intuitive best next-move generation in board game experts?


Intellectual capital disclosure in Malaysia. This study investigates intellectual capital (IC) disclosure in Malaysian public listed companies. Specifically, this research examines the relationship between IC disclosure and companies' profitability, productivity, and firm size. We measure the IC disclosure using the IC disclosure index and I C disclosure frequency. The sample includes 255 firm-year observations from 2006-2008. The results from our content analysis confirm those of prior research that the IC disclosure has been increasing over time. The study finds that human capital is the most reported intellectual capital disclosure. In addition, we also find that firm size positively contribute to the disclosure of IC. The result of this study added to the body of literature by providing evidence on the relationship between IC disclosure and firms' characteristics.

Question: What is the relationship between firm size and intellectual capital disclosure?


Comparison of eSports and Traditional Sports Consumption Motives. With recognition of the need for studying eSports in this interactive digital communication era, this study explored 14 motivational factors affecting the time spent on eSports gaming. Using a sample of 515 college students and athletic event attendees, we further compared eSports game patterns to their non-eSport or traditional sport involvements (game participation, game attendance, sports viewership, sports readership, sports listenership, Internet usage specific to sports, and purchase of team merchandise). Multiple regression results indicated that competition and skill had a statistically significant impact on the time spent on eSports games while peer pressure had marginal significance. Related to the overall findings, developing tailored messages that drives consumption behaviors of target audiences to specific eSports games will provide a better chance for marketers to fulfill their strategic goals of increased purchasing and larger market shares. Understanding that the interest in competition and skill are critical to eSports gamers may influence marketers to focus on creating games and opportunities for gamers to compete against each other and give tangible rewards to the winner. The use of peer pressure may be another motivational factor for playing. Consequently, those marketing dollars could be spent more on the interactive nature of game design. The subsequent analysis on cross-validation check suggests that the results of the regression analysis could be generalized.

Question: What are the motivational factors of eSports?


CINCO: a simplicity-driven approach to full generation of domain-specific graphical modeling tools. Even with the help of powerful metamodeling frameworks, the development of domain-specific graphical modeling tools is usually a complex, repetitive, and tedious task, which introduces substantial upfront costs often prohibiting such approaches in practice. In order to reduce these costs, the presented Cinco meta tooling suite is designed to provide a holistic approach that greatly simplifies the development of such domain-specific tools. Our solution is based on the idea to apply the concept of domain specialization also to the (meta-)domain of "domain-specific modeling tools". Important here is our focus on complex graph-based models, comprising various kinds of nodes and edges together with their individual representation, correlations, and interpretation. This focus allows for high-level specifications of the model structures and functionalities as the prerequisite for push-button tool generation.

Question: What is a domain-specific graphical modeling tool?


Named Entity Recognition and Compositional Morphology for Word Representations in Chinese. We approach the task of named entity recognition (NER) for Chinese by representing each entity with a composition of its traits: the token-entity, its characters, and its characters' main radicals. Character and radical-level information for each entity are included to provide additional relationships that might not be strictly captured within a token-entity's word embedding during training. We learn using neural networks that are some combination of the following traits: unidirectional or bidirectional; single or multi-layer; simple, gated recurrent unit (GRU), or long short term memory (LSTM) celled. We achieve a maximum not-O token-level F1 score of 76 and entity-level F1 of 70.

Question: What is the maximum not-O token-level F1 score?