End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient. Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.

Question: What is the goal-oriented dialog policy learning via policy gradient?


Capability Model for Open Data: An Empirical Analysis. Creating superior competitiveness is central to open data organization's survivability in the fast changing and competitive open data market. In their quest to develop and increase competitiveness and survivability, many of these organizations are moving towards developing open data capabilities. Research-based knowledge on open data capabilities and how they relate to each other remains sparse, however, with most of the open data literature focusing on social and economic value of open data, not capabilities required. By exploring the related literature on business and organizational capabilities and linking the findings to the empirical evidence collected through the survey of 49 open data organizations around the world, this study develops an open data capability model. The model emerged from our deductive research process improves both theoretical and practical understanding of open data capabilities and their relationships required to help increase competitiveness and survivability of these types of organizations.

Question: What is the relationship between open data and business capabilities?


Design of a linear variable-stiffness mechanism using preloaded bistable beams. A machine with variable internal stiffness can meet the performance requirement in different working environments. Linear variable-stiffness mechanisms (LVSM) are rarely found compared with those producing rotary motion. This paper presents an approach to design a linear variable-stiffness mechanism. The idea is to parallel connect two lateral bistable beams with a vertical spring. Through the preload adjustment of the bistable beams, the output force to displacement curve can exhibit different stiffnesses. The merit of the proposed LVSM is that very large stiffness variation can be achieved in a compact space. The stiffness may even be tuned to zero by assigning a proper stiffness to the vertical spring. An optimization formulation is presented to design a LVSM with the largest stiffness variation. The effects of various parameters on the stiffness variation and linearity are discussed. The results are numerically verified with a prototype illustrated.

Question: What is a variable-stiffness mechanism?


Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation "where needed." The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.

Question: What is Monte Carlo Localization?


SeriesNet:A Generative Time Series Forecasting Model. Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.

Question: What is a generative time series forecasting model?


We can learn your #hashtags: Connecting tweets to explicit topics. In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of <;tweet, hashtag> pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.

Question: How can we incorporate hashtag related features to serve for hashtag recommendation?


Continuous Requirements Engineering and Human-Centered Agile Software Development. The idea of Continuous Requirements Engineering in relation to a Human-Centered Agile Development Process is discussed. First, it is argued that Continuous Requirements Engineering has to cover design-time and runtime aspects. In this way maintenance is covered as well. Second, arguments are provided for integrating aspects of usability and user experience into requirements specifications. This has to be done continuously. Therefore, the term Continuous Human-Centered Development is introduced and discussed. Based on a process model for SCRUM some aspects of integrating HCD into the development process are discussed.

Question: What is continuous requirements engineering?


Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification. The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.

Question: What is the difference between multi-label and multi-class classification?


Multi-Task Learning For Parsing The Alexa Meaning Representation Language. The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. The first contribution is a linearization of the AMRL parses that aligns it to a related task of spoken language understanding (SLU) and a deep neural network architecture that uses multi-task learning to predict AMRL fine-grained types, properties and intents. The second contribution is a deep neural network architecture that leverages embeddings from the large-scale data resources that are available for SLU. When combined, these contributions enable the training of accurate models of AMRL parsing, even in the presence of data sparsity. The proposed models, which use the linearized AMRL parse, multi-task learning, residual connections and embeddings from SLU, decrease the error rates in the prediction of the full AMRL parse by 3.56%

Question: What is the difference between a linearization and a parsing?


An evaluation framework for viable business models for m-commerce in the information technology sector. This paper presents a study of the characteristics of viable business models in the field of Mobile Commerce (m-commerce). Mobility has given new dimensions to the way commerce works. All over the world various stakeholder organisations are consistently probing into the areas where m-commerce can be exploited and can generate revenue or value for them, even though some of those implementations are making the business environment more complex and uncertain. This paper proposes a viable business model evaluation framework, based on the VISOR model, which helps in determining the sustainability capabilities of a business model. Four individual cases were conducted with diverse organisations in the Information Technology sector. The four cases discussed dealt with mobile business models and the primary data was collected via semi structured interviews, supplemented by an extensive range of secondary data. A cross-case comparative data analysis was used to review the patterns of different viable business components across the four cases and, finally, the findings and conclusions of the study are presented.

Question: What is a viable business model?


Understanding the influence and service type of trusted third party on consumers' online trust: evidence from Australian B2C marketplace. In this study, the trusted third party (TTP) in Australia's B2C marketplace is studied and the factors influencing consumers' trust behaviour are examined from the perspective of consumers' online trust. Based on the literature review and combined with the development status and background of Australia's e-commerce, underpinned by the Theory of Planned Behaviour (TPB) and a conceptual trust model, this paper expatiates the specific factors and influence mechanism of TTP on consumers' trust behaviour. Also this paper explains two different functions of TTP to solve the online trust problem faced by consumers. Meanwhile, this paper summarizes five different types of services provided by TTPs during the establishment of the trust relationship. Finally, the present study selects 100 B2C enterprises by the simple random sampling method and makes a detailed analysis of their TTPs, to verify the services and functions of the proposed TTP in the trust model. This study is of some significance for comprehending the influence mechanism, functions and services of TTPs on consumers' trust behaviour in the realistic Australian B2C environment.

Question: What is the difference between a trusted third party and a third party?


Gay and bisexual identity development among female-to-male transsexuals in North America: emergence of a transgender sexuality.. We studied a North American sample of female-to-male (FtM) transsexuals sexually attracted to men, aiming to understand their identity and sexuality in the context of a culture of transgender empowerment. Sex-reassigned FtM transsexuals, 18 years or older and attracted to men, were recruited via an FtM community conference and listserv. Participants (N = 25) responded to open-ended questions about identity development, sexual behavior, and social support. Data were analyzed by content analysis. Scores for sexual identity, self esteem, sexual functioning, and psychological adjustment were compared to those of a comparison group (N = 76 nontransgender gay and bisexual men). Of the 25 FtMs, 15 (60%) identified as gay, 8 (32%) as bisexual, and 2 (8%) as queer. All were comfortable with their gender identity and sexual orientation. The FtM group was more bisexual than the nontransgender gay and bisexual controls. No significant group differences were found in self esteem, sexual satisfaction, or psychological adjustment. For some FtMs, sexual attractions and experiences with men affirmed their gender identity; for others, self-acceptance of a transgender identity facilitated actualization of their attractions toward men. Most were "out" as transgender among friends and family, but not on the job or within the gay community. Disclosure and acceptance of their homosexuality was limited. The sexual identity of gay and bisexual FtMs appears to mirror the developmental process for nontransgender homosexual men and women in several ways; however, participants also had experiences unique to being both transgender and gay/bisexual. This signals the emergence of a transgender sexuality.

Question: What is the sexual identity of female-to-male transsexuals?


Characterizing the software process: a maturity framework. A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement.<<ETX>>

Question: What is the software-development process maturity framework?


Efficient detail-enhanced exposure correction based on auto-fusion for LDR image. We consider the problem of how to simultaneously and well correct the over- and under-exposure regions in a single low dynamic range (LDR) image. Recent methods typically focus on global visual quality but cannot well-correct much potential details in extremely wrong exposure areas, and some are also time consuming. In this paper, we propose a fast and detail-enhanced correction method based on automatic fusion which combines a pair of complementarily corrected images, i.e. backlight & highlight correction images (BCI &HCI). A BCI with higher visual quality in details is quickly produced based on a proposed faster multi-scale retinex algorithm; meanwhile, a HCI is generated through contrast enhancement method. Then, an automatic fusion algorithm is proposed to create a color-protected exposure mask for fusing BCI and HCI when avoiding potential artifacts on the boundary. The experiment results show that the proposed method can fast correct over/under-exposed regions with higher detail quality than existing methods.

Question: How to correct over/under-exposed regions in a single low dynamic range (LDR) image?


The New Frontier of Smart Grids. The power grid is a massive interconnected network used to deliver electricity from suppliers to consumers and has been a vital energy supply. To minimize the impact of climate change while at the same time maintaining social prosperity, smart energy must be embraced to ensure a balanced economical growth and environmental sustainability. There fore, in the last few years, the new concept of a smart grid (SG) became a critical enabler in the contempo rary world and has attracted increas ing attention of policy makers and engineers. This article introduces the main concepts and technological challenges of SGs and presents the authors' views on some required challenges and opportunities pre sented to the IEEE Industrial Electronics Society (IES) in this new and exciting frontier.

Question: What is a smart grid?


Learning Depth from Single Monocular Images. We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc.) and their corresponding ground-truth depthmaps. Then, we apply supervised learning to predict the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a discriminatively-trained Markov Random Field (MRF) that incorporates multiscale localand global-image features, and models both depths at individual points as well as the relation between depths at different points. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps.

Question: How do you estimate depth from a single monocular image?


Why Are Consumers Willing to Pay for Privacy? An Application of the Privacy-freemium Model to Media Companies. Monetizing their users' personal information instead of charging a fee has become an established revenue model for platform operators—a new form of media companies specialized in aggregating, managing, and distributing user-generated online content. However, the commodification of privacy leads to privacy concerns that might be a risk for such businesses. Thus, a new approach is to focus on consumers' willingness to pay for privacy, assuming that monetizing privacy protection might be an alternative revenue model. Following the freemium idea, we developed an innovative research design, offering 553 online survey participants the opportunity to subscribe to a fictional premium version of Facebook with additional privacy control features in return for a monthly fee. Based on the theory of planned behavior, we developed and tested a research model to explain actual willingness to pay for privacy behavior. Our findings show that perceived usefulness and trust significantly affect willingness to pay. In contrast, perceived internet privacy risk was not found to have a significant influence. We thus conclude that consumers are willing to pay for privacy in the form of a privacy-freemium model, provided they perceive the premium version as offering added value and as trustworthy.

Question: What is the privacy-freemium model?


Deep Domain Adaptation in Action Space. In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.

Question: What is the difference between supervised learning and unsupervised learning?


A Blended Deep Learning Approach for Predicting User Intended Actions. User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

Question: What is attrition?


Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing. Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on lowpower neuromorphic hardware by converting a sequence of game frames into discrete actions.

Question: What is the first paper on low precision policy distillation?


Probabilistic Visual Learning for Object Representation. We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands.

Question: What is the maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding?


Augmented Reality through Wearable Computing. Wearable computing moves computation from the desktop to the user. We are forming a community of networked, wearable-computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer could track the user's finger to act as the system's mouse; perform face recognition; and detect passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. With the use of input from these interface devices and sensors, a long-term goal of this project is to model the user's actions, anticipate his or her needs, and perform a seamless interaction between the virtual and physical environments.

Question: What is augmented reality?


Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment. Sensors in a data fusion environment over hostile territory are geographically dispersed and change location with time. In order to collect and process data from these sensors an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.

Question: How to cluster sensors in wireless ad hoc networks operating in a threat environment?


SOCIAL SPACE AND SYMBOLIC POWER *. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/asa.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

Question: How to use JSTOR archive?


Optimization of Belt-Type Electrostatic Separation of Triboaerodynamically Charged Granular Plastic Mixtures. Electrostatic separation is frequently employed for the selective sorting of conducting and insulating materials for waste electric and electronic equipment. In a series of recent papers, the authors have presented several novel triboelectrostatic separation installations for the recycling of the main categories of plastics contained in information technology wastes. The aim of the present work is to optimize the design and the operation of such an installation, composed of a belt-type electrostatic separator associated to a propeller-type tribocharging device. The experimental design method is employed for modeling and optimizing the separation of a mixture of high-impact polystyrene and acrylonitrile butadiene styrene granules originating from shredded out-of-use computer cases. A distinct experiment is carried out on three synthetic mixtures of virgin plastic granules: 75% polyamide (PA)+ 25% polycarbonate (PC), 50% PA+ 50% PC, and 25% PA + 75% PC. The best results are obtained for the mixtures containing equal quantities of the two constituents.

Question: What is the best way to recycle plastic?


High-Resolution Frequency-Wavenumber Spectrum Analysis. The output of an array bfoensors is considered to be a homogeneous random field. In this case there is a spectral representation for this field, similar to that for stationary random processes, which consists of a superposition of traveling waves. The frequencywavenumber power spectral density provides the mean-square value for the amplitudes of these waves and is of considerable importance in the analysis of propagating waves by means of an array of sensors. The conventional method of frequency-wavenumber power spectral density estimation uses a fixed wavenumber window and its resolution is determined esserrtially by& beam pattern of the array of sensors. A high-resolution method-of estimation is introduced which employs a wavenumber windoar whose shape changes and is a function of the wavenumber at which an estimate is obtained. It is shown that the wavenumber resolution of this method is considerably better than that of the conventional method. Application of these results is given to seismic data obtained from the large apenure seismic array located in eastern Montana. In addition, the application of the high-resolution method to other areas, such as radar, sonar, and radio astronomy, is indicated.

Question: What is the high-resolution method of estimation?


W ORDS OR C HARACTERS ? F INE-GRAINED. Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.1

Question: What is the difference between a character and a word?


End-to-end text recognition with convolutional neural networks. Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.

Question: What is the state of the art in end-to-end text recognition?


GALE: Geometric Active Learning for Search-Based Software Engineering. Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.

Question: What is a near-linear time MOEA?


A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels. This paper presents an approach to automatic video game level design consisting of a computational model of player enjoyment and a generative system based on evolutionary computing. The model estimates the entertainment value of game levels according to the presence of "rhythm groups," which are defined as alternating periods of high and low challenge. The generative system represents a novel combination of genetic algorithms (GAs) and constraint satisfaction (CS) methods and uses the model as a fitness function for the generation of fun levels for two different games. This top-down approach improves upon typical bottom-up techniques in providing semantically meaningful parameters such as difficulty and player skill, in giving human designers considerable control over the output of the generative system, and in offering the ability to create levels for different types of games.

Question: What is a generative system?


Evolution of Architectural Floor Plans. Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.

Question: What is the evolutionary representation and requirements specification used?


Automatic detection of users' skill levels using high-frequency user interface events. Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users' skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.

Question: What is the difference between a task-independent and a task-dependent classifier?


Customer relationship management capabilities Measurement , antecedents and consequences. Purpose – This study seeks to extend the resource-based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities. Design/methodology/approach – Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling. Findings – A three-factor (customer interaction management capability, customer relationship upgrading capability and customer win-back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer-centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on organizational performance. Practical implications – This study provides a useful measurement mode of CRM capabilities that managers can use to evaluate the status in quo of CRM capabilities of their firms. Managers may also improve their CRM programs more effectively and efficiently by deploying such strategic resources of firms as customer orientation, customer-centric organizational system and CRM technology to build and strengthen their CRM capabilities. Originality/value – The paper addresses significant gaps in the current literature by taking a capability view of CRM, developing a valid measurement model of CRM capabilities, and examining how possession of important CRM resources influences the effective deployment of CRM capabilities.

Question: What is customer relationship management?


Player Profiling with Fallout 3. In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player's behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants' personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create person-

Question: How can we use a video game to profile a player?


Rule-Based Neural Networks for Classification and Probability Estimation. In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm. Our primary motivation for this is to ensure that the knowledge embodied in the network is explicitly encoded in the form of understandable rules. This enables the network's decision to be understood, and provides an audit trail of how that decision was arrived at. We utilize an information theoretic approach to learning a model of the domain knowledge from examples. This model takes the form of a set of probabilistic conjunctive rules between discrete input evidence variables and output class variables. These rules are then mapped onto the weights and nodes of a feedforward neural network resulting in a directly specified architecture. The network acts as parallel Bayesian classifier, but more importantly, can also output posterior probability estimates of the class variables. Empirical tests on a number of data sets show that the rule-based classifier performs comparably with standard neural network classifiers, while possessing unique advantages in terms of knowledge representation and probability estimation.

Question: What is a rule-based neural network?


Statistical Parsing for harmonic Analysis of Jazz Chord Sequences. Analysing music resembles natural language parsing in requiring the derivation of structure from an unstructured and highly ambiguous sequence of elements, whether they are notes or words. Such analysis is fundamental to many music processing tasks, such as key identification and score transcription. The focus of the present paper is on harmonic analysis. We use the three-dimensional tonal harmonic space developed by [4, 13, 14] to define a theory of tonal harmonic progression, which plays a role analogous to semantics in language. Our parser applies techniques from natural language processing (NLP) to the problem of analysing harmonic progression. It uses a formal grammar of jazz chord sequences of a kind that is widely used for NLP, together with the statistically based modelling techniques standardly used in wide-coverage parsing, to map music onto underlying harmonic progressions in the tonal space. Using supervised learning over a small corpus of jazz chord sequences annotated with harmonic analyses, we show that grammar-based musical parsing using simple statistical parsing models is more accurate than a baseline Markovian model trained on the same corpus.

Question: What is the difference between a Markovian model and a grammar-based model?


On the compensation of dead time and zero-current crossing for a PWM-inverter-controlled AC servo drive. Conventional dead-time compensation methods, for pulsewidth-modulated (PWM) inverters, improve the current output waveforms; however, the zero-current crossing effect is still apparent. This letter proposes a new method, based on angle domain repetitive control, to reduce the distortions in the PWM inverters output waveforms caused by the dead time and the zero-crossing problem.

Question: How to compensate dead time and zero-current crossing for a PWM-inverter-controlled AC servo drive?


Windows NT pagefile.sys Virtual Memory Analysis. As hard disk encryption, RAM disks, persistent data avoidance technology and memory resident malware become morewidespread, memory analysis becomes more important. In order to provide more virtual memory than is actually physicalpresent on a system, an operating system may transfer frames of memory to a pagefile on persistent storage. Current memoryanalysis software does not incorporate such pagefiles and thus misses important information. We therefore present a detailedanalysis of Windows NT paging. We use dynamic gray-box analysis, in which we place known data into virtual memory andexamine where it is mapped to, in either the physical memory or the pagefile, and cross-reference these findings with theWindows NT Research Kernel source code. We demonstrate how to decode the non-present page table entries, and accuratelyreconstruct the complete virtual memory space, including non-present memory pages on Windows NT systems using 32-bit,PAE or IA32e paging. Our analysis approach can be used to analyze other operating systems as well.

Question: How to analyze memory on windows?


High-Speed Flight of Quadrotor Despite Loss of Single Rotor. In order to achieve high-speed flight of a damaged quadrotor with complete loss of a single rotor, a multiloop hybrid nonlinear controller is designed. By fully making use of sensor measurements, the model dependence of this control method is reduced, which is conducive to handling disturbance from the unknown aerodynamic effects. This controller is tested on a quadrotor vehicle with one rotor completely removed in the high-speed condition. Free flights are performed in the Open Jet Facility, a large-scale wind tunnel. Over 9 m/s flight speed is reached for the damaged quadrotor in these tests. In addition, several high-speed spin-induced aerodynamic effects are discovered and analyzed.

Question: How to control a quadrotor with one rotor removed?


Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.

Question: What is a multidimensional scaling algorithm?


Grid-based DBSCAN for clustering extended objects in radar data. The online observation using high-resolution radar of a scene containing extended objects imposes new requirements on a robust and fast clustering algorithm. This paper presents an algorithm based on the most cited and common clustering algorithm: DBSCAN [1]. The algorithm is modified to deal with the non-equidistant sampling density and clutter of radar data while maintaining all its prior advantages. Furthermore, it uses varying sampling resolution to perform an optimized separation of objects at the same time it is robust against clutter. The algorithm is independent of difficult to estimate input parameters such as the number or shape of available objects. The algorithm outperforms DBSCAN in terms of speed by using the knowledge of the sampling density of the sensor (increase of app. 40-70%). The algorithm obtains an even better result than DBSCAN by including the Doppler and amplitude information (unitless distance criteria).

Question: How to cluster radar data?


MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.

Question: What is MXNet?


Predictive Control of Power Converters: Designs With Guaranteed Performance. In this work, a cost function design based on Lyapunov stability concepts for finite control set model predictive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal.

Question: What is a buck dc-dc converter?


ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.

Question: What is the state of the art in neural network architecture design?


A novel method for eye tracking and blink detection in video frames. This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.

Question: What is a non intrusive method?


Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking. Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.

Question: How to design a privacy-preserving profile matching protocol for proximity-based mobile social networking?


Control Strategy on Doubly Fed Induction Generator Using PSIM. This paper deals with doubly fed induction generator control strategy. This paper includes the results performed in PSIM-9 software such as Performance comparison of output voltage under different loading conditions and output power. Keywords— Doubly Fed Induction Generator (DFIG), variable speed wind turbine

Question: What is the output voltage of a doubly fed induction generator?


Examining the Bilingual Advantage on Conflict Resolution Tasks: A Meta-Analysis. A great deal of research has compared monolingual and bilinguals on conflict resolution tasks, with inconsistent findings: Some studies reveal a bilingual advantage on global RTs, some reveal a bilingual advantage on interference cost, and some show no advantage. We report a meta-analysis of 73 comparisons (N = 5538), with estimates of global RTs and interference cost for each study. Results revealed a moderately significant effect size that was not moderated by type of cost (global RT or interference cost) or task. Age interacted with type of cost, showing a pattern difficult to reconcile with theories of bilingualism and executive control. Additionally there was a significant main effect of lab, which might be due to sociolinguistic differences in samples, data treatment and methodology, or Hawthorne effects.

Question: What is a meta-analysis?


System structure for software fault tolerance. The paper presents, and discusses the rationale behind, a method for structuring complex computing systems by the use of what we term "recovery blocks", "conversations" and "fault-tolerant interfaces". The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.

Question: What is a fault-tolerant interface?


Virtual peers as partners in storytelling and literacy learning. Literacy learning learning how to read and write, begins long before children enter school. One of the key skills to reading and writing is the ability to represent thoughts symbolically and share them in language with an audience who may not necessarily share the same temporal and spatial context for the story. Children learn and practice these important language skills everyday, telling stories with the peers and adults around them. In particular, storytelling in the context of peer collaboration provides a key environment for children to learn language skills important for literacy. In light of this, we designed Sam, an embodied conversational agent who tells stories collaboratively with children. Sam was designed to look like a peer for preschool children, but to tell stories in a developmentally advanced way: modeling narrative skills important for literacy. Results demonstrated that children who played with the virtual peer told stories that more closely resembled the virtual peer's linguistically advanced stories: using more quoted speech and temporal and spatial expressions. In addition, children listened to Sam's stories carefully, assisting her and suggesting improvements. The potential benefits of having technology play a social role in young children's literacy learning is discussed.

Question: What is the potential benefit of having technology play a social role in young children's literacy learning?


High Resolution AMOLED Pixel Using Negative Feedback Structure for Improving Image Quality. Abstract An active matrix organic light emitting diode (AMOLED) pixel structure is proposed to improve image quality of high resolution small sized displays. The proposed pixel structure increases luminance uniformity by decreasing the distortion of the voltage in the storage capacitors using negative feedback structure. The simulation results show that the distortion of the voltage in the storage capacitors with negative feedback structure decreases to 55.3% compared to that without negative feedback structure and the emission current error of the proposed pixel structure ranges from –0.71 LSB to 0.56 LSB when the threshold voltage of the driving TFT varies from –0.2 V to 0.2 V.

Question: How to improve image quality of high resolution small sized displays?


An efficient centralized scheduling algorithm in IEEE 802.15.4e TSCH networks. IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard has gained a lot of attention within the Industrial Internet of Things research community due to its effectiveness in improving reliability and providing ultra-low power consumption for industrial applications, and in which its communication is orchestrated by a schedule. Despite its relevance, the standard leaves out of its scope in defining how the schedule is built, updated and maintained. This open issue is one of the trending topics in the IETF 6TiSCH WG, that still need to be addressed. This work focuses on scheduling in TSCH networks in a centralized manner where the gateway makes time and frequency slot allocation. This paper formulates the scheduling problem as a throughput maximization problem and delay minimization problem. We propose a graph theoretical approach to solve the throughput maximization problem in a centralized way. The combinatorial properties of the scheduling problem are addressed by providing an equivalent maximum weighted bipartite matching (MWBM) problem to reduce the computational complexity and also adopting the Hungarian algorithm in polynomial time. Simulation results are provided to evaluate the performance of the proposed scheme.

Question: What is the IEEE 802.15.4e TSCH standard?


The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity. Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as "black teenagers" are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.

Question: What is the problem of bias in AI?


A Robust Minimax Approach to Classification. When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and covariance matrix of each class are assumed to be known. No further assumptions are made with respect to the class-conditional distributions. Misclassification probabilities are then controlled in a worst-case setting: that is, under all possible choices of class-conditional densities with given mean and covariance matrix, we minimize the worst-case (maximum) probability of misclassification of future data points. For a linear decision boundary, this desideratum is translated in a very direct way into a (convex) second order cone optimization problem, with complexity similar to a support vector machine problem. The minimax problem can be interpreted geometrically as minimizing the maximum of the Mahalanobis distances to the two classes. We address the issue of robustness with respect to estimation errors (in the means and covariances of the classes) via a simple modification of the input data. We also show how to exploit Mercer kernels in this setting to obtain nonlinear decision boundaries, yielding a classifier which proves to be competitive with current methods, including support vector machines. An important feature of this method is that a worst-case bound on the probability of misclassification of future data is always obtained explicitly.

Question: What is a robust minimax approach to classification?


Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations. Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PERSTOUR for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geotagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PERSTOUR against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

Question: How to recommend a tour based on user interests and POI visit durations?


Control of systems integrating logic, dynamics, and constraints. This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include constrained linear systems, finite state machines, some classes of discrete event systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.

Question: What is a mixed logical dynamical system?


Transition-based Knowledge Graph Embedding with Relational Mapping Properties. Many knowledge repositories nowadays contain billions of triplets, i.e. (head-entity, relationship, tail-entity), as relation instances. These triplets form a directed graph with entities as nodes and relationships as edges. However, this kind of symbolic and discrete storage structure makes it difficult for us to exploit the knowledge to enhance other intelligenceacquired applications (e.g. the QuestionAnswering System), as many AI-related algorithms prefer conducting computation on continuous data. Therefore, a series of emerging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with fewer parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flexible enough to tackle well with the various mapping properties of triplets, even though its authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the distinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet depending on its own weight. We carry out extensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application scenarios on several benchmark datasets. Results show that the model we proposed significantly outperforms the former ones with lower parameter complexity as TransE.

Question: What is the state-of-the-art method for knowledge graph embedding?


Towards a better understanding of Vector Quantized Autoencoders. Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts. Recent work on vector quantized autoencoders (VQ-VAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10. In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm. Training the discrete bottleneck with EM helps us achieve better image reconstruction results on SVHN together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.

Question: What is the difference between a discrete latent variable model and a continuous latent variable model?


DGASensor: Fast Detection for DGA-Based Malwares. DNS protocol has been used by many malwares for command-and-control (C&C). To improve the resiliency of C&C communication, Domain Generation Algorithm (DGA) has been utilized by recent malwares such as Locky, Conficker and Zeus. Many detection systems have been introduced for DGA-based botnets detection. However, such botnets detection approaches suffer from several limitations, for instance, requiring a group of DGA domains, period behaviors, the presence of multiple bots, and so forth. It is very hard for them to detect an individually running DGA-based malware which leave only a few traces. In this paper, we develop DGASensor to detect DGA-based malwares immediately by identifying a single DGA domain using lexical evidence. First, DGASensor automatically analyzes the lexical patterns of the most popular domains listed in Alexa top 100,000, and then extracts two templates, namely distribution template and structure template. Second, the above two templates, pronounceable attributes, and some frequently used properties like entropy and length, are used to extract features from a single domain. Third, we train our classifier using a non-DGA dataset consisting of domains obtained from Alexa rank and a DGA dataset generated by known DGAs. At last, we provide a short word filter to decrease the false positive rate. We implement a prototype system and evaluate it using the above training dataset with 10-fold cross validation. Moreover, a set of real world DNS traffic collected from a recursive DNS server is used to measure real world performance of our system. The results show that DGASensor detects DGA domains with accuracy 93% in our training dataset and is able to identify a variety of malwares in the real world dataset with an extremely high processing capability.

Question: What is the difference between a botnet and a bot?


Play Therapy: Practitioners' Perspectives on Implementation and Effectiveness. The purpose of the present research was to explore practitioners' perspectives on play therapy as an intervention when working with a child who has experienced trauma, has present PTSD symptoms and has a co-morbid mental health diagnosis. Play therapy has been accepted as an effective intervention to utilize with children who have been exposed to trauma (Schaefer, 1994). However, there is currently limited research evaluating play therapy as an intervention with children who have been traumatized and have developed PTSD or other mental health symptoms/disorders. The current study aimed to supplement the gap in existing research. Two agencies that serve early childhood mental health clients agreed to participate in the present study by completing an online survey. Data was gathered from 22 practitioner respondents. The results indicate that practitioners believe that play therapy is an effective intervention when treating children with trauma histories, PTSD symptoms, and mental health disorders. The results of the present research support findings from previous literature regarding play therapy when used as an intervention for treating trauma and/or mental health disorders. Furthermore, the present research confirms the notion that creating a safe space for their clients using play therapy is an important part of the intervention process. Given the gap in research surrounding play therapy as an intervention when PTSD and a co-morbid mental health disorders occur concurrently, further research would be beneficial to the field of social work and would positively inform the practitioners who work in early intervention settings. PRACTITIONERS'    PERSPECTIVES 2

Question: What is the purpose of the present research?


Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application. This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of -60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.

Question: What is a universal filter?


Business Intelligence. Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics. The framework highlights the importance of unstructured data and discusses the need to develop BI tools for its acquisition, integration, cleanup, search, analysis, and delivery. In addition, this paper explores a matrix for BI data types (structured vs. unstructured) and data sources (internal and external) to guide research.

Question: What is business intelligence?


A power line communication network infrastructure for the smart home. Low voltage electrical wiring in homes has largely been dismissed as too noisy and unpredictable to support high speed communication signals. However, recent advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel media access control (MAC) and physical layer (PHY) protocols, capable of supporting power line communication networks at speeds comparable to wired local area networks (LANs). In this paper we motivate the use of power line LAN's as a basic infrastructure for building integrated "smart homes," wherein information appliances (IA)—ranging from simple control or monitoring devices to multimedia entertainment systems—are seamlessly interconnected by the very wires which provide them electricity. By simulation and actual measurements using "reference design" prototype commercial powerline products, we show that the HomePlugMAC and PHY layers can guarantee QoS for real-time communications, supporting delay sensitive data streams for "Smart Home" applications.

Question: What is a smart home?


GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild. In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet [1]. It populates a majority of 563 object classes and 87 motion patterns in real-world, resulting in a scale of over 10 thousand video segments and 1.5 million bounding boxes. To our knowledge, GOT-10k is by far the richest motion trajectory dataset, and its coverage of object classes is more than a magnitude wider than similar scale counterparts [20], [23]. By publishing GOT-10k, we hope to encourage the development of generic purposed trackers that work for a wide range of moving objects and under diverse real-world scenarios. To promote generalization and avoid the evaluation results biased to seen classes, we follow the one-shot principle [35] in dataset splitting where training and testing classes are zero-overlapped. We also carry out a series of analytical experiments to select a compact while highly representative testing subset – it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation. Finally, we train and evaluate a number of representative trackers on GOT-10k and analyze their performance. The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers. The database and toolkits are publicly available at https://got-10k.github.io.

Question: What is the name of the dataset?


Interactive volumetric fog display. Traditional fog screens are 2D. We propose a new design of fog screen that can generate fog at different depth positions and at only where necessary. Our approach applies projection mapping onto a non-planar and reconfigurable fog screen, thus enabling interactive visual contents to be displayed at multiple depth levels. Viewers can perceive the three-dimensionality naturally, and interact with the unencumbered images by touching them directly in mid-air. The display can be used in mixed reality settings where physical objects co-exist and interact with the 3D imagery in physical space.

Question: What is a non-planar and reconfigurable fog screen?


Combining Poisson singular integral and total variation prior models in image restoration. In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods. & 2013 Elsevier B.V. All rights reserved.

Question: Combining Poisson singular integral and total variation prior models in image restoration.


Overview of Virtual Reality Technologies. The promise of being able to be inside another world might be resolved by Virtual Reality. It wouldn't be naïve to assume that we will be able to enter and completely feel another world at some point in the future; be able to interact with knowledge and entertainment in a totally immersive state. Advancements are becoming more frequent, and with the recent popularity of technology in this generation, a lot of investment is being made. Prototypes of head displays that completely cover the user's view and movement recognition which doesn't need an intermediate device for input of data are already Virtual Reality devices available to developers and even to the public. From time to time, the way we interact with computers change, and virtual reality promises to make this interaction as real as possible. Although scenes like flying a jet or tank, are already tangible, another scenes, such as being able to feel the dry air of the Sahara in geography classes or feel the hard, cold scales of a dragon in a computer game seem to be in a long w a y f r o m n o w. H o w e v e r , t e c h n o l o g i c advancements and increase in the popularity of these technologies point to the possibility of such amazing scenes coming true.

Question: What is virtual reality?


Question: What is the difference between C4.5 and C5?


Characterizing large-scale use of a direct manipulation application in the wild. Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users' command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community's higher-level tasks from low-level logging data.

Question: What is the largest scale application of a direct manipulation application?


Activity Recognition with Smartphone Sensors Second Exam Literature Review. The ubiquity of smartphones together with their ever-growing computing, networking and sensing powers have been changing the landscape of people's daily life. Among others, activity recoginition, which takes the raw sensor reading as inputs and predicts a user's motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones' sensors. We start with the basic concepts such as sensors, activity types, etc. We review the core data mining techniques behind the main stream activity recognition algorithms, analyze their major challenges and introduce a variety of real applications enabled by activity recognition. Professor: Dr. Hanghang Tong Signature: Date: 06-12-2014 Professor: Dr. Ping Ji Signature: Date: 06-12-2014 Professor: Dr. Ted Brown Signature: Date: 06-12-2014 Advisor: Dr. Hanghang Tong

Question: What is the difference between activity recognition and activity recognition with smartphones' sensors?


Sparse inverse covariance estimation with the graphical lasso.. We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

Question: What is the name of the algorithm?


On the Generalization Ability of Online Strongly Convex Programming Algorithms. This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound, that holds with high probability, on the excess risk of the output of an online algorithm in terms of the average regret. This allows one to use recent algorithms with logarithmic cumulative regret guarantees to achieve fast convergence rates for the excess risk with high probability. As a corollary, we characterize the convergence rate of P EGASOS(with high probability), a recently proposed method for solving the SVM optimization problem.

Question: What is the average regret of online convex programming algorithms?


Tomographic reconstruction of piecewise smooth images. In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it is necessary to consider the estimation of reconstructed images when the problem is under-constrained, i.e., when a unique solution does not exist. To resolve ambiguities among solutions, it is necessary to place additional constraints on the reconstructed image. In this paper, we present a surface evolution technique to model the reconstructed image as piecewise smooth. We model the reconstructed image as two regions that are each smoothly varying in intensity and are separated by a smooth surface. We define a cost functional to penalize deviation from piecewise smoothness while ensuring that the projections of the estimated image match the measured projections. From this functional, we derive an evolution for the modeled image intensity and an evolution for the surface, thereby defining a variational tomographic estimation technique. We show example reconstructions to highlight the performance of the proposed method on real medical images.

Question: How to reconstruct a piecewise smooth image?


Track--Stair Interaction Analysis and Online Tipover Prediction for a Self-Reconfigurable Tracked Mobile Robot Climbing Stairs. This paper analyzes track-stair interactions and develops an online tipover prediction algorithm for a self-reconfigurable tracked mobile robot climbing stairs, which is vulnerable to tipping-over. Tipover prediction and prevention for a tracked mobile robot in stair climbing are intractable problems because of the complex track--stair interactions. Unlike the wheeled mobile robots, which are normally assumed to obey the nonholonomic constraints, slippage is unavoidable for a tracked mobile robot, especially in stair climbing. Furthermore, the track-stair interactive forces are complicated, which may take the forms of grouser-tread hooking force, track--stair edge frictional force, grouser-riser clutching force, and even their compositions. In this paper, the track--stair interactions are analyzed systematically, and tipover stability criteria are derived for a tracked mobile robot climbing stairs. An online tipover prediction algorithm is also developed, which forms an essential part for autonomous and semiautonomous stair-climbing control. The effectiveness of the proposed algorithms are verified by experiments.

Question: What is the difference between wheeled and tracked mobile robots?


Computational approaches to motor control. This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.

Question: What is the computational approach to motor control?


Opening the Black Box of Deep Neural Networks via Information. Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work [Tishby and Zaslavsky (2015)] proposed to analyze DNNs in the Information Plane; i.e., the plane of the Mutual Information values that each layer preserves on the input and output variables. They suggested that the goal of the network is to optimize the Information Bottleneck (IB) tradeoff between compression and prediction, successively, for each layer. In this work we follow up on this idea and demonstrate the effectiveness of the InformationPlane visualization of DNNs. Our main results are: (i) most of the training epochs in standard DL are spent on compression of the input to efficient representation and not on fitting the training labels. (ii) The representation compression phase begins when the training errors becomes small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift to smaller training error into a stochastic relaxation, or random diffusion, constrained by the training error value. (iii) The converged layers lie on or very close to the Information Bottleneck (IB) theoretical bound, and the maps from the input to any hidden layer and from this hidden layer to the output satisfy the IB self-consistent equations. This generalization through noise mechanism is unique to Deep Neural Networks and absent in one layer networks. (iv) The training time is dramatically reduced when adding more hidden layers. Thus the main advantage of the hidden layers is computational. This can be explained by the reduced relaxation time, as this it scales super-linearly (exponentially for simple diffusion) with the information compression from the previous layer. (v) As we expect critical slowing down of the stochastic relaxation near phase transitions on the IB curve, we expect the hidden layers to converge to such critic

Question: What is the information bottleneck?


Hidost: a static machine-learning-based detector of malicious files. Malicious software, i.e., malware, has been a persistent threat in the information security landscape since the early days of personal computing. The recent targeted attacks extensively use non-executable malware as a stealthy attack vector. There exists a substantial body of previous work on the detection of non-executable malware, including static, dynamic, and combined methods. While static methods perform orders of magnitude faster, their applicability has been hitherto limited to specific file formats. This paper introduces Hidost, the first static machine-learning-based malware detection system designed to operate onmultiple file formats. Extending a previously published, highly effective method, it combines the logical structure of files with their content for even better detection accuracy. Our system has been implemented and evaluated on two formats, PDF and SWF (Flash). Thanks to its modular design and general feature set, it is extensible to other formats whose logical structure is organized as a hierarchy. Evaluated in realistic experiments on timestamped datasets comprising 440,000 PDF and 40,000 SWF files collected during several months, Hidost outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.

Question: What is the highest number of malicious files detected by an antivirus engine?


A new multiclass SVM algorithm and its application to crowd density analysis using LBP features. Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.

Question: How to classify a crowd density?


Phase statistics of interferograms with applications to synthetic aperture radar.. Interferometric methods are well established in optics and radio astronomy. In recent years, interferometric concepts have been applied successfully to synthetic aperture radar (SAR) and have opened up new possibilities in the area of earth remote sensing. However interferometric SAR applications require thorough phase control through the imaging process. The phase accuracy of SAR images is affected by decorrelation effects between the individual surveys. We analyze quantitatively the influence of decorrelation on the phase statistics of SAR interferograms. In particular, phase aberrations as they occur in typical SAR processors are studied in detail. The dependence of the resulting phase bias and variance on processor parameters is presented in several diagrams.

Question: What is the phase statistics of interferograms with applications to synthetic aperture radar?


Performing a check-in: emerging practices, norms and 'conflicts' in location-sharing using foursquare. Location-sharing services have a long history in research, but have only recently become available for consumers. Most popular commercial location-sharing services differ from previous research efforts in important ways: they use manual 'check-ins' to pair user location with semantically named venues rather than tracking; venues are visible to all users; location is shared with a potentially very large audience; and they employ incentives. By analysis of 20 in-depth interviews with foursquare users and 47 survey responses, we gained insight into emerging social practices surrounding location-sharing. We see a shift from privacy issues and data deluge, to more performative considerations in sharing one's location. We discuss performance aspects enabled by check-ins to public venues, and show emergent, but sometimes conflicting norms (not) to check-in.

Question: What are the emerging practices surrounding location-sharing?


BiDirectional optical communication with AquaOptical II. This paper describes AquaOptical II, a bidirectional, high data-rate, long-range, underwater optical communication system. The system uses the software radio principle. Each AquaOptical II modem can be programmed to transmit user defined waveforms and record the received waveforms for detailed analysis. This allows for the use of many different modulation schemes. We describe the hardware and software architecture we developed for these goals. We demonstrate bidirectional communication between two AquaOptical II modems in a pool experiment. During the experiment AquaOptical II achieved a signal to noise ration of 5.1 over a transmission distance of 50 m at pulse widths of 1 µsec, 500 ns, and 250 ns. When using discrete pulse interval modulation (DPIM) this corresponds to a bit-rate of 0.57 Mbit/s, 1.14 Mbit/s, and 2.28 Mbit/s.

Question: What is the bit rate of AquaOptical II?


Characterization of a Novel Nine-Transistor SRAM Cell. Data stability of SRAM cells has become an important issue with the scaling of CMOS technology. Memory banks are also important sources of leakage since the majority of transistors are utilized for on-chip caches in today's high performance microprocessors. A new nine-transistor (9T) SRAM cell is proposed in this paper for simultaneously reducing leakage power and enhancing data stability. The proposed 9T SRAM cell completely isolates the data from the bit lines during a read operation. The read static-noise-margin of the proposed circuit is thereby enhanced by 2 X as compared to a conventional six-transistor (6T) SRAM cell. The idle 9T SRAM cells are placed into a super cutoff sleep mode, thereby reducing the leakage power consumption by 22.9% as compared to the standard 6T SRAM cells in a 65-nm CMOS technology. The leakage power reduction and read stability enhancement provided with the new circuit technique are also verified under process parameter variations.

Question: How does the proposed 9T SRAM cell compare to the conventional 6T SRAM cell?


Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels. To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the model generates spatial highlights of each category presented in images using an attention model, and subsequently performs binary segmentation for each highlighted region using decoder. Combining attention model, the decoder trained with segmentation annotations in different categories boosts accuracy of weakly-supervised semantic segmentation. The proposed algorithm demonstrates substantially improved performance compared to the state-of-theart weakly-supervised techniques in PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.

Question: What is the weakly-supervised semantic segmentation?


Modelling Interaction of Sentence Pair with Coupled-LSTMs. Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-ofthe-art methods.

Question: How to model the interactions of two sentences with deep neural networks?


The Galois / Counter Mode of Operation ( GCM ).

Question: What is the Galois / Counter Mode of Operation?


Discriminative Label Consistent Domain Adaptation. Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target. We present in this paper a novel unsupervised DA method for cross-domain visual recognition which simultaneously optimizes the three terms of a theoretically established error bound. Specifically, the proposed DA method iteratively searches a latent shared feature subspace where not only the divergence of data distributions between the source domain and the target domain is decreased as most state-of-the-art DA methods do, but also the inter-class distances are increased to facilitate discriminative learning. Moreover, the proposed DA method sparsely regresses class labels from the features achieved in the shared subspace while minimizing the prediction errors on the source data and ensuring label consistency between source and target. Data outliers are also accounted for to further avoid negative knowledge transfer. Comprehensive experiments and in-depth analysis verify the effectiveness of the proposed DA method which consistently outperforms the state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain image classification tasks.

Question: What is the difference between discriminative label consistent domain adaptation and discriminative label consistent domain adaptation?


Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning. Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

Question: How to use a GPS receiver to estimate the position of a vehicle?