A compact magnetic directional proximity sensor for spherical robots. Spherical robots have recently attracted significant interest due to their ability to offer high speed motion with excellent locomotion efficiency. As a result of the presence of a sealed outer shell, its obstacle avoidance strategy has been simply "hit and run". While this is convenient due to the specific geometry of the spherical robots, it however could pose serious issues when the robots are small and light. For portable spherical robots with on-board cameras, a high speed collision with a hard surface may damage the robot or the camera. This paper proposes a novel and compact proximity sensor that utilizes passive magnetic field to detect the ferromagnetic obstacles through perturbation of the magnetic field. Compared with the existing works that utilize the Earth's weak magnetic field as a means of detection, the approach undertaken here seeks to harness the same principle but uses an intelligently designed magnetic assembly. It efficiently amplifies the perturbation and therefore improves the detection performance. The presented method is able to simultaneously determine both the distance and direction of the nearby ferromagnetic obstacles. Both simulation and experimental results are presented to validate the sensing principle and operational performance.

Question: What is a spherical robot?


Magic decorator: automatic material suggestion for indoor digital scenes. Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.

Question: How does magic decorator work?


Deep Learning for Detection of Object-Based Forgery in Advanced Video. Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

Question: How to detect object-based forgery in advanced video?


Speaker-sensitive dual memory networks for multi-turn slot tagging. In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system — the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.

Question: How does the speaker-sensitive dual memory network work?


Current-Fed Dual-Bridge DC–DC Converter. A new isolated current-fed pulsewidth modulation dc-dc converter-current-fed dual-bridge dc-dc converter-with small inductance and no deadtime operation is presented and analyzed. The new topology has more than 3times smaller inductance than that of current-fed full-bridge converter, thus having faster transient response speed. Other characteristics include simple self-driven synchronous rectification, simple housekeeping power supply, and smaller output filter capacitance. Detailed analysis shows the proposed converter can have either lower voltage stress on all primary side power switches or soft switching properties when different driving schemes are applied. A 48-V/125-W prototype dc-dc converter with dual output has been tested for the verification of the principles. Both simulations and experiments verify the feasibility and advantages of the new topology

Question: What is the difference between a current-fed full-bridge converter and a current-fed dual-bridge converter?


Collection, exploration and analysis of crowdfunding social networks. Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.

Question: What is the impact of social engagement on startup fund raising success?


A Direct Least-Squares (DLS) method for PnP. In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).

Question: What is the direct least squares method for PnP?


Somatization vs . Psychologization of Emotional Distress : A Paradigmatic Example for Cultural Psychopathology. This paper describes the developing area of cultural psychopathology, an interdisciplinary field of study focusing on the ways in which cultural factors contribute to the experience and expression of psychological distress. We begin by outlining two approaches, often competing, in order to provide a background to some of the issues that complicate the field. The main section of the paper is devoted to a discussion of depression in Chinese culture as an example of the types of questions that can be studied. Here, we start with a review of the epidemiological literature, suggesting low rates of depression in China, and move to the most commonly cited explanation, namely that Chinese individuals with depression present this distress in a physical way. Different explanations of this phenomenon, known as somatization, are explored and reconceptualized according to an increasingly important model for cross-cultural psychologists: the cultural constitution of the self. We close by discussing some of the contributions, both theoretical and methodological, that can be made by cross-cultural psychologists to researchers in cultural psychopathology. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. This article is available in Online Readings in Psychology and Culture: https://scholarworks.gvsu.edu/orpc/vol10/iss2/3

Question: What is the difference between somatization and psychologization of emotional distress?


Animations 25 Years Later: New Roles and Opportunities. Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.

Question: What are the roles of animation in user interfaces?


BLAT--the BLAST-like alignment tool.. Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.

Question: What is BLAT?


The relational model is dead, SQL is dead, and I don't feel so good myself. We report the opinions expressed by well-known database researchers on the future of the relational model and SQL during a panel at the International Workshop on Non-Conventional Data Access (NoCoDa 2012), held in Florence, Italy in October 2012 in conjunction with the 31st International Conference on Conceptual Modeling. The panelists include: Paolo Atzeni (Università Roma Tre, Italy), Umeshwar Dayal (HP Labs, USA), Christian S. Jensen (Aarhus University, Denmark), and Sudha Ram (University of Arizona, USA). Quotations from movies are used as a playful though effective way to convey the dramatic changes that database technology and research are currently undergoing.

Question: What is the relational model?


OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

Question: What is a parallel corpus?


An Approximation Algorithm for the Noah's Ark Problem with Random Feature Loss. The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah's Ark Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to maximize the expected PD of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation and a probability of extinction. Only simplified instances of the problem, where one or more parameters are fixed as constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In this paper, we present a pseudopolynomial time approximation scheme for NAPL.

Question: What is the expected phylogenetic diversity of a set of species?


Relation Extraction with Matrix Factorization and Universal Schemas. Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof-the-art distant supervision.

Question: What is the relation between matrix factorization and universal schemas?


Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model. Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.

Question: How can we model human understanding of complex intentional action?


Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.. Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Question: What is the difference between classification and regression?


A 27Gb/s Forwarded-Clock I/O Receiver Using an Injection-Locked LC-DCO in 45nm CMOS. This paper describes a method for both filtering and deskewing a link clock using a differential injection-locked LC-DCO and demonstrates a forwarded-clock data receiver using this technique operating at 27 Gb/s.

Question: What is a differential injection-locked LC-DCO?


3D strip meander delay line structure for multilayer LTCC-based SiP applications. Recently, the timing control of high-frequency signals is strongly demanded due to the high integration density in three-dimensional (3D) LTCC-based SiP applications. Therefore, to control the skew or timing delay, new 3D delay lines will be proposed. For frailty of the signal via, we adopt the concept of coaxial line and proposed an advanced signal via structure with quasi coaxial ground (QCOX-GND) vias. We will show the simulated results using EM and circuit simulator.

Question: What is the concept of coaxial line?


The long history of gaming in military training. There is a long history of the dual-use of games in both the military and the entertainment applications. This has taken the form of sand tables, miniatures, board games, and computer games. The current tension between entertainment and military applications of games is just the return of similar concerns that surrounded the gaming tools and technologies of previous generations. Dynamic representations of the physical world are interesting and useful tools in a number of fields, to include the military, city planning, architecture, education, and entertainment. Computer games are tools that allow all of these audiences to accomplish similar goals.

Question: What is the history of gaming in military training?


Facial Performance Capture with Deep Neural Networks. We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor's face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.

Question: How to train a deep neural network to produce high-quality results for unseen inputs?


New E-Commerce User Interest Patterns. The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.

Question: What is a Hadoop ecosystem?


VIDEO GAMES: PERSPECTIVE, POINT-OF-VIEW, AND IMMERSION. of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts VIDEO GAMES: PERSPECTIVE, POINT-OF-VIEW, AND IMMERSION By Laurie N. Taylor May 2002 Chair: Dr. Terry Harpold Department: English Presently, video game designers, players, and often even video game theorists approach the representation of point-of-view in video games as intuitive and uncomplicated. Drawing on the current critical work on video games, theories of immersion and engagement, and theories of human-computer-interaction, I question the significance of optical perspective in video games in terms of player point-of-view. Because video games are an experiential medium, critical study of video games must include the study of the diegetic, visual-auditory, interface, and experiential (often termed interactive or participatory) aspects of video game play. Through an analysis of the various points-of-view within different games and the differing points-of-view within a single game, I attempt to delineate the differences both in the creation of the experiential game space and in the possibilities for immersion within the game space. For this, I delimit two types of immersion: diegetic immersion, which corresponds to immersion in the game, and intra-diegetic or situated immersion, which corresponds to immersion

Question: What is the difference between diegetic and intra-diegetic immersion?


The trade-off between taxi time and fuel consumption in airport ground movement. Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport's surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multiobjective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the S. Ravizza ( ) · J.A.D. Atkin School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK e-mail: smr@cs.nott.ac.uk J.A.D. Atkin e-mail: jaa@cs.nott.ac.uk J. Chen · P. Stewart School of Engineering, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK J. Chen e-mail: juchen@lincoln.ac.uk P. Stewart e-mail: pstewart@lincoln.ac.uk E.K. Burke Department of Computing and Mathematics, University of Stirling, Cottrell Building, Stirling FK9 4LA, UK e-mail: e.k.burke@stir.ac.uk 26 S. Ravizza et al. trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.

Question: What is the trade-off between taxi time and fuel consumption in airport ground movement?


Crowd disasters as systemic failures: analysis of the Love Parade disaster. Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd 'go mad'? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as 'intentional pushing', 'mass panic', 'stampede', and 'crowd crushes'. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a 'domino effect', resulting from a phenomenon called 'crowd turbulence' or 'crowd quake'. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from 'mass panic' or 'crowd crushes'. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.

Question: What is the Love Parade?


Situation assessment algorithm for a collision prevention assistant. In this paper, we present the concept of a collision prevention assistant, a system that we believe can significantly contribute to road safety. We propose a new situation assessment algorithm which is tailored to the action of braking and that further accounts for the nonlinearities that arise when vehicles cut out or come to a standstill. The effect of sensor uncertainty on the performance of the proposed algorithm is modelled using a Markov chain and analyzed by means of a Monte Carlo simulation on a typical traffic situation.

Question: What is a collision prevention assistant?


A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games. To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.

Question: What is a MOBA game?


Intelligent book positioning for library using RFID and book spine matching. In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.

Question: How to locate books in a library?


The definition and classification of glaucoma in prevalence surveys.. This review describes a scheme for diagnosis of glaucoma in population based prevalence surveys. Cases are diagnosed on the grounds of both structural and functional evidence of glaucomatous optic neuropathy. The scheme also makes provision for diagnosing glaucoma in eyes with severe visual loss where formal field testing is impractical, and for blind eyes in which the optic disc cannot be seen because of media opacities.

Question: What is glaucoma?


Serotonin alterations in anorexia and bulimia nervosa: New insights from imaging studies. Anorexia nervosa (AN) and bulimia nervosa (BN) are related disorders with relatively homogenous presentations such as age of onset and gender distribution. In addition, they share symptoms, such as extremes of food consumption, body image distortion, anxiety and obsessions, and ego-syntonic neglect, raises the possibility that these symptoms reflect disturbed brain function that contributes to the pathophysiology of this illness. Recent brain imaging studies have identified altered activity in frontal, cingulate, temporal, and parietal cortical regions in AN and BN. Importantly, such disturbances are present when subjects are ill and persist after recovery, suggesting that these may be traits that are independent of the state of the illness. Emerging data point to a dysregulation of serotonin pathways in cortical and limbic structures that may be related to anxiety, behavioral inhibition, and body image distortions. In specific, recent studies using PET with serotonin specific radioligands implicate alterations of 5-HT1A and 5-HT2A receptors and the 5-HT transporter. Alterations of these circuits may affect mood and impulse control as well as the motivating and hedonic aspects of feeding behavior. Such imaging studies may offer insights into new pharmacology and psychotherapy approaches.

Question: What is the difference between anorexia and bulimia?


Learning effective Gait features using LSTM. Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new feature learning method for gait recognition. Not only can the learned feature preserve temporal information in a gait sequence, but it can also be applied to cross-view gait recognition. Heatmaps extracted by a convolutional neutral network (CNN) based pose estimate method are used to describe the gait information in one frame. To model a gait sequence, the LSTM recurrent neural network is naturally adopted. Our LSTM model can be trained with unlabeled data, where the identity of the subject in a gait sequence is unknown. When labeled data are available, our LSTM works as a frame to frame view transformation model (VTM). Experiments on a gait benchmark demonstrate the efficacy of our method.

Question: How to learn effective gait features using LSTM?


There exists a neural network that does not make avoidable mistakes. The authors show that a multiple-input, single-output, single-hidden-layer feedforward network with (known) hardwired connections from input to hidden layer, monotone squashing at the hidden layer and no squashing at the output embeds as a special case a so-called Fourier network, which yields a Fourier series approximation properties of Fourier series representations. In particular, approximation to any desired accuracy of any square integrable function can be achieved by such a network, using sufficiently many hidden units. In this sense, such networks do not make avoidable mistakes.<<ETX>>

Question: What is a neural network?


Key organizational factors in data warehouse architecture selection. a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates wh

Question: What is the difference between a data warehouse and a data mart?


A conversational intelligent tutoring system to automatically predict learning styles. This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%.

Question: What is a conversational intelligent tutoring system?


UPTIME: Ubiquitous pedestrian tracking using mobile phones. The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

Question: How accurate is the step count estimation?


Direct voltage control of DC-DC boost converters using model predictive control based on enumeration. This paper presents a model predictive control (MPC) approach for the dc-dc boost converter. Based on a hybrid model of the converter suitable for both continuous and discontinuous conduction mode an objective function is formulated, which is to be minimized. The proposed MPC scheme, utilized as a voltage-mode controller, achieves regulation of the output voltage to its reference, without requiring a subsequent current control loop. Simulation and experimental results are provided to demonstrate the merits of the proposed control methodology, which include fast transient response and robustness.

Question: How to control a dc-dc boost converter?


Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities. Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the "prosumer". The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. However in literature, there is very little attention on prosumer management schemes. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of autonomous, intelligent goal-oriented virtual communities. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. According to the literature, this research is the first of this type introducing a community approach for prosumer management. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyzes the different parameters influencing the prosumer's energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.

Question: What is the prosumer community?


Grid interfaced solar PV based water pumping using brushless DC motor drive. This paper proposes a solar photovoltaic (PV) fed water pumping system driven by a brushless DC (BLDC) motor. A promising case of interruption in the water pumping due to the intermittency of PV power generation is resolved by using a power grid as an external power backup. The power is drawn from the grid in case the PV array is unable to meet the required power demand, otherwise the PV array is preferably used. A unidirectional power flow control for the same is developed and realized through a power factor corrected (PFC) boost converter. The proposed system and control enable a consumer to get a full volume of water delivery throughout the day and night regardless of the climatic condition. The utilization of motor-pump at best is thus made possible in addition to an enhanced reliability of the pumping system. The power quality standards required at the utility grid are met by the developed control. The MATLAB/Simulink based simulations and the performance analysis are carried out to demonstrate the applicability of the system.

Question: How does a grid-interfaced solar PV-based water pumping system work?


Two cases of hymenal scars occurred by child rape. Children who have been raped some years back may have hymenal scars. However, medical professionals are not accustomed in assessing these scars because of the lack of experience in performing physical examinations of the external genitalia of children who suffered from rape some years back. Moreover, the importance of physical examination of the victim's external genitalia is sometimes overlooked. Two cases of rape victims with hymenal scars who visited Daegu Child Sexual Abuse Response Center several years after their first sexual abuse along with a literature review are presented here.

Question: What is the importance of physical examination of the victim's external genitalia?


An Expectation Propagation Perspective on Approximate Message Passing. An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.

Question: What is the difference between expectation propagation and approximate message passing?


Contactless Sleep Apnea Detection on Smartphones. We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away from the subject and can simultaneously identify and track the fine-grained breathing movements from multiple subjects. We do this by transforming the phone into an active sonar system that emits frequency-modulated sound signals and listens to their reflections; our design monitors the minute changes to these reflections to extract the chest movements. Results from a home bedroom environment shows that our design operates efficiently at distances of up to a meter and works even with the subject under a blanket.  Building on the above system, we develop algorithms that identify various sleep apnea events including obstructive apnea, central apnea, and hypopnea from the sonar reflections. We deploy our system at the UW Medicine Sleep Center at Harborview and perform a clinical study with 37 patients for a total of 296 hours. Our study demonstrates that the number of respiratory events identified by our system is highly correlated with the ground truth and has a correlation coefficient of 0.9957, 0.9860, and 0.9533 for central apnea, obstructive apnea and hypopnea respectively. Furthermore, the average error in computing of rate of apnea and hypopnea events is as low as 1.9 events/hr.

Question: How to detect sleep apnea on smartphones?


Proposing a new design approach for M-learning applications. As Information and Communication Technologies (ICT) are developing frequently, new learning approaches have been introduced to facilitate teaching and learning processes. Mobile devices are growing in popularity and there has been an increasing interest in their potential as innovative tools for learning. Mobile learning or shortly (M-learning) is a new learning approach intending to use mobile devices such as laptops, smart phones and personal digital assistant (PDA) in learning process in any place and at any time. M-learning application needs to be designed in a way that considers the special features and constraints of mobile devices such as screen size, available storage, processor speed and battery life. This paper explores the existing design approaches for M-learning and highlights their main limitations. As a step forward in this direction, this paper also proposes a new design approach for M-learning applications focusing mainly on three main aspects: learner, learning content and technology. The approach consists of three phases: starting dimensions (SD) phase, M-learning Development (MLD) phase and Learning Content Design (LCD) phase. Finally, this paper provides a case study scenario to demonstrate the feasibility of the proposed approach.

Question: What is M-learning?


Experiments with crowdsourced re-annotation of a POS tagging data set. Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.

Question: What is a crowdsourced POS tagging data set?


A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET. This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.

Question: What is a self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET?


Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons. The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Question: What is the difference between Wi-Fi fingerprinting and other indoor positioning techniques?


Automated Music Emotion Recognition: A Systematic Evaluation. Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval (MIR) with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the ArousalValence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect induction rather than affect attribution. We consider a variety of algorithms at each stage of the training process, from preprocessing to feature selection and model selection, and we report the results of extensive testing. We found that: (1) none of the variations we considered leads to a substantial improvement in performance, which we present as evidence of a limit on what is achievable under this architecture, and (2) the size of the small data sets that are commonly used in the MER literature limits the possibility of improving the set of features used in MER due to the phenomenon of Subset Selection Bias. We conclude with some proposals for advancing the state of the art.

Question: What is the limit of the architecture of automated music emotion recognition?


Prevention of falls and consequent injuries in elderly people. Injuries resulting from falls in elderly people are a major public-health concern, representing one of the main causes of longstanding pain, functional impairment, disability, and death in this population. The problem is going to worsen, since the rates of such injuries seem to be rising in many areas, as is the number of elderly people in both the developed and developing world. Many methods and programmes to prevent such injuries already exist, including regular exercise, vitamin D and calcium supplementation, withdrawal of psychotropic medication, cataract surgery, professional environment hazard assessment and modification, hip protectors, and multifactorial preventive programmes for simultaneous assessment and reduction of many of the predisposing and situational risk factors. To receive broader-scale effectiveness, these programmes will need systematic implementation. Care must be taken, however, to rigorously select the right actions for those people most likely to benefit, such as vitamin D and calcium supplementation and hip protectors for elderly people living in institutions.

Question: What is the best way to prevent falls in elderly people?


Microwave vision: From RF safety to medical imaging. This article gives an overview of the activities of the company Microwave Vision, formerly Satimo, oriented to health-related applications. The existing products in terms of Specific Absorption Rate (SAR) measurement and RF safety are described in detail. The progress of the development of a new imaging modality for breast pathology detection using microwaves is shortly reported.

Question: What is the SAR of microwaves?


A novel strong-motion seismic network for community participation in earthquake monitoring. The Quake-Catcher Network (QCN) is breaking new ground in seismology by combining new micro-electro-mechanical systems (MEMS) technology with volunteer seismic station distributed computing. Rather than distributing just computations, the QCN allows volunteers to participate in scientific data collection and computation. Using these innovative tools, QCN will increase the number of strong-motion observations for improved earthquake detection and analysis in California, and throughout the world. QCN's increased density of seismic measurements will revolutionize seismology. The QCN, in concert with current seismic networks, may soon provide advanced alerts when earthquakes occur, estimate the response of a building to earthquakes even before they happen, and generate a greater understanding of earthquakes for scientists and the general public alike. Details of how one can join the QCN are outlined. In addition, we have activities on our website that can be used in K-16 classrooms to teach students basic seismology and physics concepts.

Question: What is a novel strong-motion seismic network for community participation in earthquake monitoring?


Neuroprotective effect of Bacopa monnieri on beta-amyloid-induced cell death in primary cortical culture.. AIM OF THE STUDY Bacopa monnieri (Brahmi) is extensively used in traditional Indian medicine as a nerve tonic and thought to improve memory. To examine the neuroprotective effects of Brahmi extract, we tested its protection against the beta-amyloid protein (25-35) and glutamate-induced neurotoxicity in primary cortical cultured neurons.   MATERIALS AND METHODS Neuroprotective effects were determined by measuring neuronal cell viability following beta-amyloid and glutamate treatment with and without Brahmi extract. Mechanisms of neuroprotection were evaluated by monitoring cellular oxidative stress and acetylcholinesterase activity.   RESULTS Our result demonstrated that Brahmi extract protected neurons from beta-amyloid-induced cell death, but not glutamate-induced excitotoxicity. This neuroprotection was possibly due to its ability to suppress cellular acetylcholinesterase activity but not the inhibition of glutamate-mediated toxicity. In addition, culture medium containing Brahmi extract appeared to promote cell survival compared to neuronal cells growing in regular culture medium. Further study showed that Brahmi-treated neurons expressed lower level of reactive oxygen species suggesting that Brahmi restrained intracellular oxidative stress which in turn prolonged the lifespan of the culture neurons. Brahmi extract also exhibited both reducing and lipid peroxidation inhibitory activities.   CONCLUSIONS From this study, the mode of action of neuroprotective effects of Brahmi appeared to be the results of its antioxidant to suppress neuronal oxidative stress and the acetylcholinesterase inhibitory activities. Therefore, treating patients with Brahmi extract may be an alternative direction for ameliorating neurodegenerative disorders associated with the overwhelming oxidative stress as well as Alzheimer's disease.

Question: What is the neuroprotective effect of Brahmi?


A fast, invariant representation for human action in the visual system.. Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action r

Question: How does the brain recognize actions?


The SeaLion has Landed: An IDE for Answer-Set Programming---Preliminary Report. We report about the current state and designated features of the tool SeaLion, aimed to serve as an integrated development environment (IDE) for answer-set programming (ASP). A main goal of SeaLion is to provide a user-friendly environment for supporting a developer to write, evaluate, debug, and test answer-set programs. To this end, new support techniques have to be developed that suit the requirements of the answer-set semantics and meet the constraints of practical applicability. In this respect, SeaLion benefits from the research results of a project on methods and methodologies for answer-set program development in whose context SeaLion is realised. Currently, the tool provides source-code editors for the languages of Gringo and DLV that offer syntax highlighting, syntax checking, and a visual program outline. Further implemented features are support for external solvers and visualisation as well as visual editing of answer sets. SeaLion comes as a plugin of the popular Eclipse platform and provides itself interfaces for future extensions of the IDE.

Question: What is the SeaLion?


Digital Marketing Maturity Models : Overview and Comparison. The variety of available digital tools, strategies and activities might confuse and disorient even an experienced marketer. This applies in particular to B2B companies, which are usually less flexible in uptaking of digital technology than B2C companies. B2B companies are lacking a framework that corresponds to the specifics of the B2B business, and which helps to evaluate a company's capabilities and to choose an appropriate path. A B2B digital marketing maturity model helps to fill this gap. However, modern marketing offers no widely approved digital marketing maturity model, and thus, some marketing institutions provide their own tools. The purpose of this paper is building an optimized B2B digital marketing maturity model based on a SWOT (strengths, weaknesses, opportunities, and threats) analysis of existing models. The current study provides an analytical review of the existing digital marketing maturity models with open access. The results of the research are twofold. First, the provided SWOT analysis outlines the main advantages and disadvantages of existing models. Secondly, the strengths of existing digital marketing maturity models, helps to identify the main characteristics and the structure of an optimized B2B digital marketing maturity model. The research findings indicate that only one out of three analyzed models could be used as a separate tool. This study is among the first examining the use of maturity models in digital marketing. It helps businesses to choose between the existing digital marketing models, the most effective one. Moreover, it creates a base for future research on digital marketing maturity models. This study contributes to the emerging B2B digital marketing literature by providing a SWOT analysis of the existing digital marketing maturity models and suggesting a structure and main characteristics of an optimized B2B digital marketing maturity model. Keywords—B2B digital

Question: Digital marketing maturity models


Reveal, a general reverse engineering algorithm for inference of genetic network architectures.. Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Question: What is the size of the canadian military?


The Collateral Damage of Internet Censorship by DNS Injection. Some ISPs and governments (most notably the Great Firewall of China) use DNS injection to block access to "unwanted" websites. The censorship tools inspect DNS queries near the ISP's boundary routers for sensitive domain keywords and injecting forged DNS responses, blocking the users from accessing censored sites, such as twitter.com and facebook. com. Unfortunately this causes large scale collateral damage, affecting communication beyond the censored networks when outside DNS traffic traverses censored links. In this paper, we analyze the causes of the collateral damages comprehensively and measure the Internet to identify the injecting activities and their effect. We find 39 ASes in China injecting forged replies even for transit DNS traffic, and 26% of 43,000 measured open resolvers outside China, distributed in 109 countries, may suffer some collateral damage. Different from previous work, we find that most collateral damage arises from resolvers querying TLD name servers who's transit passes through China rather than effects due to root servers (F, I, J) located in China.

Question: What is the collateral damage of internet censorship?


Estimating types in binaries using predictive modeling. Reverse engineering is an important tool in mitigating vulnerabilities in binaries. As a lot of software is developed in object-oriented languages, reverse engineering of object-oriented code is of critical importance. One of the major hurdles in reverse engineering binaries compiled from object-oriented code is the use of dynamic dispatch. In the absence of debug information, any dynamic dispatch may seem to jump to many possible targets, posing a significant challenge to a reverse engineer trying to track the program flow. We present a novel technique that allows us to statically determine the likely targets of virtual function calls. Our technique uses object tracelets – statically constructed sequences of operations performed on an object – to capture potential runtime behaviors of the object. Our analysis automatically pre-labels some of the object tracelets by relying on instances where the type of an object is known. The resulting type-labeled tracelets are then used to train a statistical language model (SLM) for each type.We then use the resulting ensemble of SLMs over unlabeled tracelets to generate a ranking of their most likely types, from which we deduce the likely targets of dynamic dispatches.We have implemented our technique and evaluated it over real-world C++ binaries. Our evaluation shows that when there are multiple alternative targets, our approach can drastically reduce the number of targets that have to be considered by a reverse engineer.

Question: How to reverse engineer binaries?


Design, development and performance test of an automatic two-Axis solar tracker system. The energy extracted from solar photovoltaic (PV) or solar thermal depends on solar insolation. For the extraction of maximum energy from the sun, the plane of the solar collector should always be normal to the incident radiation. The diurnal and seasonal movement of the earth affects the radiation intensity received on the solar collector. Sun trackers move the solar collector to follow the sun trajectories and keep the orientation of the solar collector at an optimal tilt angle. Energy efficiency of solar PV or solar thermal can be substantially improved using solar tracking system. In this work, an automatic solar tracking system has been designed and developed using LDR sensors and DC motors on a mechanical structure with gear arrangement. Two-axis solar tracking (azimuth angle as well as altitude angle) has been implemented through microcontroller based sophisticated control logic. Performance of the proposed system over the important parameters like solar radiation received on the collector, maximum hourly electrical power, efficiency gain, short circuit current, open circuit voltage and fill factor has been evaluated and compared with those for fixed tilt angle solar collector.

Question: How does a solar tracker work?


Visual Path Prediction in Complex Scenes with Crowded Moving Objects. This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.

Question: How to find a plausible future path in complex scenes with a large number of moving objects?


Safe Exploration of State and Action Spaces in Reinforcement Learning. In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.

Question: How to safely explore state-action spaces?


Deep Learning for Nonlinear Diffractive Imaging. Image reconstruction under multiple light scattering is crucial for a number of important applications in cell microscopy and tissue imaging. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and a regularizer is used to enforce the prior on the object. In this letter, We propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network (CNN) for inverting multiple scattering. Simulations show that the proposed formulation is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

Question: What is a deep convolutional neural network?


Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety. This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.

Question: What is the role of road users on possible injury risks?


Multi-objective optimisation algorithms for GIS-based multi-criteria decision analysis : an application for evacuation planning. Geographic Information Systems have gradually acquired greater relevance as tools to support decision-making processes, and during the last decades they have been used in conjunction with Multi-Criteria Decision Analysis techniques (GIS-MCDA) to solve real-world spatial problems. GIS-MCDA can be generally divided in two main approaches: Multi-Attribute and MultiObjective techniques. Until now most of the applications of GIS-MCDA have been focused only on using the multi-attribute approach, and less than 10% of the research has been related to a specific type of multi-objective technique: the use of heuristic/meta-heuristic algorithms. The present study explores how different heuristic/meta-heuristic methods perform on solving a spatial multi-objective optimisation problem. To achieve this, four algorithms representing different types of heuristics methods were implemented, and applied to solve the same spatial multi-objective optimisation problem related with an evacuation planning situation. The implemented algorithms were Standard Particle Swarm Optimisation (SPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi-Objective Simulated Annealing (AMOSA) and Multi-Objective Grey Wolf Optimiser (MOGWO). The results show that the four algorithms were effective on solving the given problem, although in general AMOSA and MOGWO had a higher performance for the evaluated aspects (number of solutions, effectiveness of the optimisation, diversity, execution time and repeatability). However, the differences in the results obtained from each algorithm were not clear enough to state that one type of heuristic is superior than others. Since AMOSA and MOGWO are the most recent algorithms among the implemented ones, they include several improvements achieved by the latest research, and their superior performance could be connected to

Question: What is the difference between multi-attribute and multi-objective techniques?


Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation. In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.

Question: How to solve a constrained low-rankness and sparsity minimization problem?


Formal Verification of CNN-based Perception Systems. We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on reachability analysis for feed-forward neural networks and relies on MILP encodings of both the CNNs and transformations under question. We present an implementation and discuss the experimental results obtained for a CNN trained from the MNIST data set.

Question: How to verify neural-based perception systems?


Adversarially Learned Inference. We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.

Question: What is adversarially learned inference?


Evaluating Recommender Systems -- An evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context. Preface It was January 2007 when Dolf Trieschnigg, my supervisor for the course Information Retrieval , first told me about the availability of graduation projects at TNO Information and Communication Technology. This was a bull's eye since I just started to orientate myself on a subject to investigate. I was also looking for a company where I could perform my research since I wanted to get familiar with working in a professional environment. TNO Information and Communication Technology was one of the companies that seemed interesting to me, so I contacted Stephan Raaijmakers for more information. The subject of the proposed research project, evaluating recommender systems, was completely new to me, but seemed fascinating. And it is. In September 2008 I started my research by crafting the goals and research questions for the project. Now, almost nine months later, the research has resulted in the report that you just started reading. It marks the end of my life as a student (at least for now) and the start of my professional career at TNO. At TNO I can continue to work on personalisation and recommender systems. Although I wrote this thesis the research would never have been completed without the support of other people. In the first place, I would like to thank my supervisors, Erik, Djoerd, Stephan and Dolf for supervising me throughout the research project. They were always there for me when I needed their help and/or expertise and provided loads of valuable comments and suggestions. I also like to thank Jan Telman for his help and advice with respect to statistics (and hiking through the Alps). Furthermore I would like to thank all people that participated in the user study. Without their devotion to the demanding study, the research would not have been completed. I would also like to thank my lovely girlfriend L

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


EEG-based BCI and video games: a progress report. This paper presents a systematic review of electroencephalography (EEG)-based brain–computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

Question: What is a systematic review?


The potential distribution measurement on the stress grading system of high-voltage rotating machines by using pockels effect. A surface potential measuring system was developed by utilizing Pockels sensor for measuring the potential distribution on the stress grading (SG) system of form wound motor coils. A Bi4Ge3O12 (BGO) Pockels crystal is put close to a measuring object and the capacitively-induced voltage on the end surface of the BGO crystal is measured by using Pockels effect of the crystal itself. The spatial resolution of the measuring system is 5 mm, and the frequency response reaches up to GHz-range. With this system, the surface potential distributions on a model bar coil were measured. According to the measuring results, it is shown that the newly developed potential measuring system here is very useful for evaluating the reliability of the SG system of motors.

Question: What is the potential distribution on the stress grading system of high-voltage rotating machines?


On-Line Fingerprint Verification. Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy.

Question: What is the on-line fingerprint verification system?


Illuminating search spaces by mapping elites. Nearly all science and engineering fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highestperforming solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how highperforming solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their costto-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAPElites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves the state-of-the-art for finding a single, best solution, it will catalyze advances throughout

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


Social constructivist perspectives on teaching and learning.. Social constructivist perspectives focus on the interdependence of social and individual processes in the co-construction of knowledge. After the impetus for understanding the influence of social and cultural factors on cognition is reviewed, mechanisms hypothesized to account for learning from this perspective are identified, drawing from Piagetian and Vygotskian accounts. The empirical research reviewed illustrates (a) the application of institutional analyses to investigate schooling as a cultural process, (b) the application of interpersonal analyses to examine how interactions promote cognition and learning, and (c) discursive analyses examining and manipulating the patterns and opportunities in instructional conversation. The review concludes with a discussion of the application of this perspective to selected contemporary issues, including: acquiring expertise across domains, assessment, educational equity, and educational reform.

Question: What is social constructivism?


Mass Cytometry: Single Cells, Many Features. Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications.

Question: What is mass cytometry?


A Survey of Caching Mechanisms in Information-Centric Networking. Information-Centric Networking (ICN) is a novel networking paradigm that attracts increasing research interests in recent years. In-network caching has been viewed as an attractive feature of ICN because it can reduce network traffic, alleviate server bottleneck, and reduce the user access latencies. Because of this, the network community has proposed many in-network caching mechanisms that aim at optimizing various performance metrics such as cache hit ratio and cache hit distance. In this survey, we present a comprehensive overview of the recently proposed in-network caching mechanisms for ICN. For each caching mechanism, we describe it in detail, present examples to illustrate how it works, and analyze its possible benefits and drawbacks. We also compare some typical in-network caching mechanisms through extensive simulations and discuss the remaining research challenges.

Question: What is the cache hit ratio?


Recommending citations with translation model. Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.

Question: How to recommend citations with translation model?


Towards Frequent Subgraph Mining on Single Large Uncertain Graphs. Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain graph is #P-hard, we develop an approximation algorithm with accuracy guarantee for this purpose. To enhance the solution, we devise optimization techniques to achieve better mining performance. Experiment results on real-life data confirm the usability of the algorithm.

Question: What is the size of the canadian military?


Ontology-Based Data Access: Ontop of Databases. We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, T -mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.

Question: What is the ontology-based data access?


3 D model classification using convolutional neural network. Our goal is to classify 3D models directly using convolutional neural network. Most of existing approaches rely on a set of human-engineered features. We use 3D convolutional neural network to let the network learn the features over 3D space to minimize classification error. We trained and tested over ShapeNet dataset with data augmentation by applying random transformations. We made various visual analysis to find out what the network has learned. We extended our work to extract additional information such as pose of the 3D model.

Question: What is a 3D convolutional neural network?


VirtualTable: a projection augmented reality game. VirtualTable is a projection augmented reality installation where users are engaged in an interactive tower defense game. The installation runs continuously and is designed to attract people to a table, which the game is projected onto. Any number of players can join the game for an optional period of time. The goal is to prevent the virtual stylized soot balls, spawning on one side of the table, from reaching the cheese. To stop them, the players can place any kind of object on the table, that then will become part of the game. Depending on the object, it will become either a wall, an obstacle for the soot balls, or a tower, that eliminates them within a physical range. The number of enemies is dependent on the number of objects in the field, forcing the players to use strategy and collaboration and not the sheer number of objects to win the game.

Question: What is a projection augmented reality installation?


Stochastic Video Prediction with Conditional Density Estimation. Frame-to-frame stochasticity is a major challenge in video prediction. The use of standard feedforward and recurrent networks for video prediction leads to averaging of future states, which can in part be attributed to the networks' limited ability to model stochasticity. We propose the use of conditional variational autoencoders (CVAE) for video prediction. To model multi-modal densities in frame-to-frame transitions, we extend the CVAE framework by modeling the latent variable with a mixture of Gaussians in the generative network. We tested our proposed Gaussian mixture CVAE (GM-CVAE) on a simple video-prediction task involving a stochastically moving object. Our architecture demonstrates improved performance, achieving noticeably lower rates of blurring/averaging compared to a feedforward network and a Gaussian CVAE. We also describe how the CVAE framework can be applied to improve existing deterministic video prediction models.1

Question: How does the CVAE framework improve video prediction?


Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children.. OBJECTIVE Television and computer game consumption are a powerful influence in the lives of most children. Previous evidence has supported the notion that media exposure could impair a variety of behavioral characteristics. Excessive television viewing and computer game playing have been associated with many psychiatric symptoms, especially emotional and behavioral symptoms, somatic complaints, attention problems such as hyperactivity, and family interaction problems. Nevertheless, there is insufficient knowledge about the relationship between singular excessive media consumption on sleep patterns and linked implications on children. The aim of this study was to investigate the effects of singular excessive television and computer game consumption on sleep patterns and memory performance of children.   METHODS Eleven school-aged children were recruited for this polysomnographic study. Children were exposed to voluntary excessive television and computer game consumption. In the subsequent night, polysomnographic measurements were conducted to measure sleep-architecture and sleep-continuity parameters. In addition, a visual and verbal memory test was conducted before media stimulation and after the subsequent sleeping period to determine visuospatial and verbal memory performance.   RESULTS Only computer game playing resulted in significant reduced amounts of slow-wave sleep as well as significant declines in verbal memory performance. Prolonged sleep-onset latency and more stage 2 sleep were also detected after previous computer game consumption. No effects on rapid eye movement sleep were observed. Television viewing reduced sleep efficiency significantly but did not affect sleep patterns.   CONCLUSIONS The results suggest that television and computer game exposure affect children's sleep and deteriorate verbal cognitive performance,

Question: What is the effect of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children?


Heterogenous Uncertainty Sampling for Supervised Learning. Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.

Question: How to select instances for training?


Sensorless current estimation and sharing in multiphase input-parallel output-parallel DC-DC converters. This paper introduces a sensorless current-sharing strategy for multiphase input-parallel output-parallel (IPOP) DC-DC converters. A dual active bridge (DAB) DC-DC converter is chosen as the basic DC-DC converter. With this strategy, by perturbing the duty cycles in (n-1) out of n phases in turn and measuring the changes of duty cycles respectively, the parameters mismatches among phases are estimated. According to the mismatches, a set of variables, which are proportional to the per-phase output currents, are calculated. Then with a current-sharing regulator, parameter mismatches are compensated, thus achieving current sharing without current sensing. The strategy is verified through both simulation and experimental implementation with a 30V-to-70V, 272W, 20kHz, three-phase IPOP DC-DC converter.

Question: How to share current in a multiphase DC-DC converter?