Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0. Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.

Question: How do digital twins work?


Lexical Analysis for the Webshell Attacks. In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.

Question: What is the difference between a webshell and a web shell?


IIT ( BHU ) : System Description for LSDSem ' 17 Shared Task. This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is based on voting and the other on logistic regression based classifier. Our final system is able to outperform the previous state of the art for the Story Cloze test. Another very interesting observation is the performance of sentiment based approach which works almost as well on its own as our final en-

Question: What is the story cloze test?


A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users. In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

Question: What is the difference between a wearable face recognition system and a traditional face recognition system?


Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games. Reinforcement Learning (RL) algorithms allow artificial agents to improve their selection of actions so as to increase rewarding experiences in their environments. The learning can become intractably slow as the state space of the environment grows. This has motivated methods that learn internal representations of the agents state by a function approximator. Impressive results have been produced by using deep artificial neural networks to learn the state representations. However, deep reinforcement learning algorithms require solving a non-convex and nonlinear unconstrained optimization problem. Methods for solving the optimization problems in deep RL are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle points. Furthermore, these methods require exhaustive trial and error to fine-tune many learning parameters. Using second derivative information can result in improved convergence properties, but computing the Hessian matrix for large-scale problems is not practical. QuasiNewton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limitedmemory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that construct positive definite Hessian approximations. In this paper, we introduce an efficient optimization method, based on the limited memory BFGS quasi-Newton method using line search strategy – as an alternative to SGD methods. Our method bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate a low-rank Hessian approximations. We provide empirical results on variety of the classic ATARI 2600 games. Our results show a robust convergenc

Question: What is the difference between a first order and a second order method?


TnT -- A Statistical Part-of-Speech Tagger. Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

Question: What is a statistical part-of-speech tagger?


A survey of glove-based input. Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance.<<ETX>>

Question: What is the ETX?


Tracking-based interaction for object creation in mobile augmented reality. In this work, we evaluate the feasibility of tracking-based interaction using a mobile phone's or tablet's camera in order to create and edit 3D objects in augmented reality applications. We present a feasibility study investigating if and how gestures made with your finger can be used to create such objects. A revised interface design is evaluated in a user study with 24 subjects that reveals a high usability and entertainment value, but also identifies issues such as ergonomic discomfort and imprecise input for complex tasks. Hence, our results suggest huge potential for this type of interaction in the entertainment, edutainment, and leisure domain, but limited usefulness for serious applications.

Question: How can we create objects in augmented reality?


Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.. Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.

Question: What is the primate ventral stream?


Offset Reduction of CMOS Based DynamicComparator by using Charge Storage Techniques- A Comparative Study. Comparator is one of the most widely used building block for analog and mixed signal systems. For the implementation of high-performance CMOS A/D converters, low offset comparators are essential. In this paper, dynamic comparator offset is calculated to the extent of high accuracy. The offset so calculated has been reduced by the charge storage techniques to achieve an efficient design. In addition to the offset, propagation delay and power dissipation, being the important parameter of the comparator, has been analyzed. It is observed that offset voltage in the comparator has been reduced to 350μV for output offset storage technique and 400μV for input offset storage techniques from 91mV. In this paper, BPTM model has been used in analyze the dynamic comparator. KeywordsDynamic comparator, offset voltage, storage capacitor, input offset storage, output offset storage.

Question: How to reduce the offset of a comparator?


Treatment planning of the edentulous maxilla. The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al., introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis. Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.

Question: What is the purpose of this article?


Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter. News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public.  In this paper, we describe Reuters Tracer, a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism

Question: What is a large scale system of detecting news events from twitter?


Classification of Multiple-Sentence Questions. Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.

Question: How to classify multiple-sentence questions?


6-DOF hovering controller design of the Quad Tiltrotor aircraft: Simulations and experiments. This paper presents a particular class of a convertible micro aerial vehicle (MAV) with fixed wings, the so-called Quad Tiltrotor aircraft. This aircraft is able to change its flight configuration from hover to level flight and vice versa by means of a transition maneuver. In this first part of the research, the hover dynamics of the Quad Tiltrotor is investigated. Dynamical model and nonlinear control based on Lyapunov design are studied. The presented approach focuses on the problem of finding a control law capable of stabilizing the aircraft's position. Some simulations results are given, which demonstrate the effectiveness of the controller. Further, some experimental results are presented tested on the Quad Tiltrotor experimental platform.

Question: How to stabilize a quadrotor?


Automated Text Summarization In SUMMARIST. SUMMARIST is an attempt to create a robust automated text summarization system, based on the 'equation': summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system's architecture and provide details of some of its modules.

Question: What is a text summarization system?


A modular control scheme for PMSM speed control with pulsating torque minimization. In this paper, a modular control approach is applied to a permanent-magnet synchronous motor (PMSM) speed control. Based on the functioning of the individual module, the modular approach enables the powerfully intelligent and robust control modules to easily replace any existing module which does not perform well, meanwhile retaining other existing modules which are still effective. Property analysis is first conducted for the existing function modules in a conventional PMSM control system: proportional-integral (PI) speed control module, reference current-generating module, and PI current control module. Next, it is shown that the conventional PMSM controller is not able to reject the torque pulsation which is the main hurdle when PMSM is used as a high-performance servo. By virtue of the internal model, to nullify the torque pulsation it is imperative to incorporate an internal model in the feed-through path. This is achieved by replacing the reference current-generating module with an iterative learning control (ILC) module. The ILC module records the cyclic torque and reference current signals over one entire cycle, and then uses those signals to update the reference current for the next cycle. As a consequence, the torque pulsation can be reduced significantly. In order to estimate the torque ripples which may exceed certain bandwidth of a torque transducer, a novel torque estimation module using a gain-shaped sliding-mode observer is further developed to facilitate the implementation of torque learning control. The proposed control system is evaluated through real-time implementation and experimental results validate the effectiveness.

Question: What is the difference between a PMSM and a permanent magnet motor?


An fMRI investigation of emotional engagement in moral judgment.. The long-standing rationalist tradition in moral psychology emphasizes the role of reason in moral judgment. A more recent trend places increased emphasis on emotion. Although both reason and emotion are likely to play important roles in moral judgment, relatively little is known about their neural correlates, the nature of their interaction, and the factors that modulate their respective behavioral influences in the context of moral judgment. In two functional magnetic resonance imaging (fMRI) studies using moral dilemmas as probes, we apply the methods of cognitive neuroscience to the study of moral judgment. We argue that moral dilemmas vary systematically in the extent to which they engage emotional processing and that these variations in emotional engagement influence moral judgment. These results may shed light on some puzzling patterns in moral judgment observed by contemporary philosophers.

Question: What is the difference between moral and immoral?


Liquid Leakage Thin Film-Tape Sensor. A liquid leakage sensor on adhesive tape with alarming function has been developed. The film sensor tape consists of three layers: a base film layer, a substrate film layer, and a protective film layer. Three conductive lines and one resistive line are printed on the substrate film layer with a nano-sized silver conductive ink by the electronic gravure printing method. The location of liquid leakage has been found to be monitored accurately by applying the pulse voltage to the conductive and resistance lines in a periodic polarity switching mode. The leakage tests for various electrolyte liquids have been performed and the accuracy of leakage error position has been achieved to be about 0.25% on 200 meter long tape length.

Question: What is the accuracy of the leakage sensor?


Leaky-Wave Antennas. This paper gives a basic review and a summary of recent developments for leaky-wave antennas (LWAs). An LWA uses a guiding structure that supports wave propagation along the length of the structure, with the wave radiating or "leaking" continuously along the structure. Such antennas may be uniform, quasi-uniform, or periodic. After reviewing the basic physics and operating principles, a summary of some recent advances for these types of structures is given. Recent advances include structures that can scan to endfire, structures that can scan through broadside, structures that are conformal to surfaces, and structures that incorporate power recycling or include active elements. Some of these novel structures are inspired by recent advances in the metamaterials area.

Question: What is a leaky-wave antenna?


Applications of Probabilistic Programming (Master's thesis, 2015). This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals. The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependent Dirichlet process mixture of objects model for object recognition and tracking. We begin this work by providing a brief introduction to probabilistic programming. In the second Chapter we present an approach to automatic discovery of samplers in the form of probabilistic programs. Specifically, we learn the procedure code of samplers for one-dimensional distributions. We formulate a Bayesian approach to this problem by specifying a grammar-based prior over probabilistic program code. We use an approximate Bayesian computation method to learn the programs, whose executions generate samples that statistically match observed data or analytical characteristics of distributions of interest. In our experiments we leverage different probabilistic programming systems, including Anglican and Probabilistic C, to perform Markov chain Monte Carlo sampling over the space of programs. Experimental results have demonstrated that, using the proposed methodology, we can learn approximate and even some exact samplers. Finally, we show that our results are competitive with regard to genetic programming methods.

Question: What is the difference between probabilistic programming and genetic programming?


Explainable User Clustering in Short Text Streams. User clustering has been studied from different angles: behavior-based, to identify similar browsing or search patterns, and content-based, to identify shared interests. Once user clusters have been found, they can be used for recommendation and personalization. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of short text streams. User clustering in this setting is more challenging than in the case of long documents as it is difficult to capture the users' dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (or UCT for short). UCT adaptively tracks changes of each user's time-varying topic distribution based both on the short texts the user posts during a given time period and on the previously estimated distribution. To infer changes, we propose a Gibbs sampling algorithm where a set of word-pairs from each user is constructed for sampling. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed clustering model compared to state-of-the-art baselines.

Question: How to cluster users in short text streams?


Paralinguistics in speech and language - State-of-the-art and the challenge. Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants' predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language. © 2012 Elsevier Ltd. All rights reserved.

Question: What is the first broad Paralinguistic Challenge?


Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier. This paper proposes a patient-specific epileptic seizure onset detection algorithm. In this algorithm, spectral features in five frequency bands (Δ, α, ß, θ and γ) is extracted from small frames of seizure and non-seizure EEG signals by applying Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT). These features can create the maximum distinction between two classes. Then a neural network (NN) classifier based on improved particle swarm optimization (IPSO) is used to determine an optimal nonlinear decision boundary. This classifier allows adjusting the parameter of the NN classifier, efficiently. Finally, the performance of algorithm is evaluated based on three measures, sensitivity, specificity and latency. The results indicate that the proposed algorithm obtain a higher sensitivity and smaller latency than other common algorithms. The proposed algorithm can be used as a seizure onset detector to initiate the just-in time therapy methods.

Question: What is the difference between a neural network and a neural network classifier?


Activity recognition from acceleration data based on discrete consine transform and SVM. This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.

Question: What is the best activity recognition system?


Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles. This paper overviews various electrical machine technologies, including brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with particular emphasis on the concept of hybrid electrical machine topologies, and compares their relative merits, in terms of torque and power density, operating speed range, over load capability, and efficiency, for application in electric, hybrid and fuel cell vehicles. electric vehicles.

Question: What is the difference between electric and hybrid vehicles?


Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games. Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.

Question: What is the difference between a social network and a social game?


Dynamic load balancing on single- and multi-GPU systems. The computational power provided by many-core graphics processing units (GPUs) has been exploited in many applications. The programming techniques currently employed on these GPUs are not sufficient to address problems exhibiting irregular, and unbalanced workload. The problem is exacerbated when trying to effectively exploit multiple GPUs concurrently, which are commonly available in many modern systems. In this paper, we propose a task-based dynamic load-balancing solution for single-and multi-GPU systems. The solution allows load balancing at a finer granularity than what is supported in current GPU programming APIs, such as NVIDIA's CUDA. We evaluate our approach using both micro-benchmarks and a molecular dynamics application that exhibits significant load imbalance. Experimental results with a single-GPU configuration show that our fine-grained task solution can utilize the hardware more efficiently than the CUDA scheduler for unbalanced workload. On multi-GPU systems, our solution achieves near-linear speedup, load balance, and significant performance improvement over techniques based on standard CUDA APIs.

Question: How to balance the load on multiple GPUs?


Growth charts for individuals with Smith-Lemli-Opitz syndrome.. Smith-Lemli-Opitz syndrome (SLOS) is a rare multiple congenital anomaly neurodevelopmental syndrome of impaired cholesterol synthesis. Growth restriction and developmental delay are very common clinical manifestations of SLOS. The degree, etiology, and consequences of growth restriction in SLOS remain an area of limited knowledge to the scientific community. There have been no studies describing the growth parameters and providing reference growth charts for individuals with SLOS. Our longitudinal data from 78 patients between the ages of 0.1 and 16 years with SLOS show a growth restriction of about two standard deviations below the Centers for Disease Control (CDC) norms for age. This study represents comprehensive anthropometric data from the largest cohort available, and proposes growth charts for widespread use in the management and study of individuals with SLOS.

Question: What is the growth chart for individuals with Smith-Lemli-Opitz syndrome?


A Study of Big Data in Cloud Environment with their Related Challenges. Big Data , it is a word which is used for the huge amount of data sets which have the big, extra various and difficu lt structure facing some difficult ies in storing the data then analyzing and visualizing the data for the final outcome [1] . Big data analytics is the procedure of probing huge amounts of data. Here we use cloud computing technology so firstly acquaint the general environment of big data and some associated technology like Hadoop. This powerful technology is used to execute the huge s cale and complicated data computing. It eradicates the requirements of hardware computing which is costly dedicated space and software's [3]. It is a resolution for analyzing Big Data by deploying Big Data softwares like Hadoop on cloud background which gives users an innovative pattern of service called as Analytics as a Service [7]. Hadoop uses the map-reduce paradigm .We also talk about various advantages, challenges, and issues associated with Big Data.

Question: What is the difference between big data and big data analytics?


Inside the Black Box: How to Explain Individual Predictions of a Machine Learning Model : How to automatically generate insights on predictive model outputs, and gain a better understanding on how the model predicts each individual data point.. Machine learning models are becoming more and more powerful and accurate, but their good predictions usually come with a high complexity. Depending on the situation, such a lack of interpretability can be an important and blocking issue. This is especially the case when trust is needed on the user side in order to take a decision based on the model prediction. For instance, when an insurance company uses a machine learning algorithm in order to detect fraudsters: the company would trust the model to be based on meaningful variables before actually taking action and investigating on a particular individual. In this thesis, several explanation methods are described and compared on multiple datasets (text data, numerical), on classification and regression problems.

Question: How to automatically generate insights on predictive model outputs, and gain a better understanding on how the model predicts each individual data point.


Dynamic Motions by a Quadruped Musculoskeletal Robot with Angle-Dependent Moment Arms. Current robotics have failed to replicate the great dexterity of the animal's motions. We believe that it is because most of the works have focused on the controller part rather than on its body structure. Indeed, the musculoskeletal system is essential to produce the animal's motions and several researchers have attempted to exploit it [1][2][3]. However, most of them have used simple mono-articular muscles and torque control which cannot replicate the nonlinearity of the living tissues, muscles and nerves [4]. In contrast, bipedal robots with monoand bi-articular muscles for performing adaptive motions [5][6][7] have been produced. To further investigate the role of the musculoskeletal system for animal motions, we desgned a quadruped robot with monoand bi-articular muscles. We used artificial pneumatic muscles as actuators due to their high mass/power ratio and their high compliance. Although these actuators replicate several characteristics of biological muscles in terms of damping and elasticity, the length-tension relation differs from them [8][9]. In the result, the angle-torque relation of the robot with pneumatic muscles differed substantially from the one of animals. Thus, we tried to bring the angle-torque relation as close as the one of living tissues by devising the angle-moment arm relation.

Question: What is the difference between a quadruped robot with monoand bi-articular muscles and a quadruped robot with monoand bi-articular muscles?


Event-based stock market prediction. There are various studies on the behavior of the market. In particular, derivatives such as futures and options have taken a lot of attentions, lately. Predicting these derivatives is not only important for the risk management purposes, but also for price speculative activities. Besides that accurate prediction of the market's direction can help investors to gain enormous profits with small amount of capital [Tsaih et al., 1998]. Stock market prediction can be viewed as a challenging time-series prediction [Kim, 2003]. There are many factors that are influential on the financial markets, including political events, natural disasters, economic conditions, and so on. Despite the complexity of the movements in market prices, the market behavior is not completely random. Instead, it is governed by a highly nonlinear dynamical system [Blank, 1991]. Forecasting the future prices is carried out based on the technical analysis, which studies the market's action using past prices and the other market information. Market analysis is in contradiction with the Efficient Market Hypothesis (EMH). EMH was developed in 1970 by economist Eugene Fama [Fama, 1965a, Fama, 1965b] whose theory stated that it is not possible for an investor to outperform the market because all available information is already built into all stock prices. If the EMH was true, it would not be possible to use machine learning techniques for market prediction. Nevertheless, there are many successful technical analysis in the financial world and number of studies appearing in academic literature that are using machine learning techniques for market prediction [Choudhry and Garg, 2008]. One way to forecast the market movement is by analyzing the special events of the market such as earnings announcements. Earnings announcement for each stock is an official public statement of a company's profitability for a specific time period, typically a quarter or a year. Each company ha

Question: What is the difference between technical analysis and machine learning?


Design and implementation of NN5 for Hong Kong stock price forecasting. A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN). In this paper, the theoretical background of NNs and the backpropagation algorithm is reviewed. Subsequently, an attempt to build a stock buying/selling alert system using a backpropagation NN, NN5, is presented. The system is tested with data from one Hong Kong stock, The Hong Kong and Shanghai Banking Corporation (HSBC) Holdings. The system is shown to achieve an overall hit rate of over 70%. A number of trading strategies are discussed. A best strategy for trading non-volatile stock like HSBC is recommended. r 2006 Elsevier Ltd. All rights reserved.

Question: What is the best stock trading strategy?


Deep Network Embedding with Aggregated Proximity Preserving. Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.

Question: What is a semi-supervised stacked auto-encoder?


Multi-label learning: a review of the state of the art and ongoing research. Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. The paper presents an up-todate overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed and ongoing research are presented.

Question: What is multi-label learning?


Coding polygon meshes as compressable ASCII. Because of the convenience of a text-based format 3D content is often published in form of a gzipped file that contains an ASCII description of the scene graph. While compressed image, audio, and video data is kept in seperate binary files, polygonal data is usually included uncompressed into the ASCII description, as there is no widely-accepted standard for compressed polygon meshes.In this paper we show how to incorporate compression of polygonal data into a purely text-based scene graph description. Our scheme codes polygon meshes as ASCII strings that compress well with standard compression schemes such as gzip. The coder is lossless when only the position and texture coordinate {\em indices} are coded. If loss is acceptable, positions and texture coordinates can be quantized and delta coded, which reduces the file size further. The gzipped scene graph description files decrease by a factor of two (six) in size when the polygon meshes they contain are coded with the lossless (lossy) ASCII coder.Furthermore we describe in detail a proof-of-concept implementation that uses the Shout3D~\cite{shout3d} pure java API---a plugin-less Web3D player that downloads all required java classes on demand. Our prototype is an extremely light-weight implementation of the decoder that can be distributed at minimal additional cost. The size of the compiled decoder class is less than 6KB by itself and less than 3KB if included into a compressed archive of java class files. It makes no use of specific features of the Shout3D API. Hence, our method will work for any scene graph API that allows (a) to extend the node set and (b) to store the scene graph as ASCII.

Question: How to compress polygon meshes as ASCII?


Sensor Systems for Prognostics and Health Management. Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.

Question: What is the state of the art in sensor systems for prognostics and health management?


UMH: A Hardware-Based Unified Memory Hierarchy for Systems with Multiple Discrete GPUs. In this article, we describe how to ease memory management between a Central Processing Unit (CPU) and one or multiple discrete Graphic Processing Units (GPUs) by architecting a novel hardware-based Unified Memory Hierarchy (UMH). Adopting UMH, a GPU accesses the CPU memory only if it does not find its required data in the directories associated with its high-bandwidth memory, or the NMOESI coherency protocol limits the access to that data. Using UMH with NMOESI improves performance of a CPU-multiGPU system by at least 1.92 × in comparison to alternative software-based approaches. It also allows the CPU to access GPUs modified data by at least 13 × faster.

Question: How to ease memory management between a CPU and multiple GPUs?


On the segmentation of 3D LIDAR point clouds. This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.

Question: What is the segmentation of 3D LIDAR point clouds?


Multimodal Word Distributions. Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.

Question: What is the multimodal word distribution?


Privacy Aspects of Recommender Systems. The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for the recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users. Arik Friedman NICTA, Australia, e-mail: arik.friedman@nicta.com.au Bart Knijnenburg UC Irvine, USA, e-mail: bart.k@uci.edu Kris Vanhecke iMinds Ghent University, Belgium, e-mail: kris.vanhecke@intec.ugent.be Luc Martens iMinds Ghent University, Belgium, e-mail: luc.martens@intec.ugent.be Shlomo Berkovsky CSIRO, Australia, e-mail: shlomo.berkovsky@csiro.au 1 Accepted to be included in the 2nd edition of the Recommender Systems handbook. Please do not share publicly, and consult the authors before citing this chapter.

Question: What is the size of the canadian military?


A ternary content-addressable memory (TCAM) based on 4T static storage and including a current-race sensing scheme. A 256 144-bit TCAM is designed in 0.18m CMOS. The proposed TCAM cell uses 4T static storage for increased density. The proposed match-line (ML) sense scheme reduces power consumption by minimizing switching activity of search-lines and limiting voltage swing of MLs. The scheme achieves a match-time of 3 ns and operates at a minimum supply voltage of 1.2 V.

Question: What is a ternary content-addressable memory?


Challenges in developing opinion mining tools for social media. While much work has recently focused on the analysis of social media in order to get a feel for what people think about current topics of interest, there are, however, still many challenges to be faced. Text mining systems originally designed for more regular kinds of texts such as news articles may need to be adapted to deal with facebook posts, tweets etc. In this paper, we discuss a variety of issues related to opinion mining from social media, and the challenges they impose on a Natural Language Processing (NLP) system, along with two example applications we have developed in very different domains. In contrast with the majority of opinion mining work which uses machine learning techniques, we have developed a modular rule-based approach which performs shallow linguistic analysis and builds on a number of linguistic subcomponents to generate the final opinion polarity and score.

Question: What are the challenges in developing opinion mining tools for social media?


TwiNER: named entity recognition in targeted twitter stream. Many private and/or public organizations have been reported to create and monitor targeted Twitter streams to collect and understand users' opinions about the organizations. Targeted Twitter stream is usually constructed by filtering tweets with user-defined selection criteria e.g. tweets published by users from a selected region, or tweets that match one or more predefined keywords. Targeted Twitter stream is then monitored to collect and understand users' opinions about the organizations. There is an emerging need for early crisis detection and response with such target stream. Such applications require a good named entity recognition (NER) system for Twitter, which is able to automatically discover emerging named entities that is potentially linked to the crisis. In this paper, we present a novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER. In the first step, it leverages on the global context obtained from Wikipedia and Web N-Gram corpus to partition tweets into valid segments (phrases) using a dynamic programming algorithm. Each such tweet segment is a candidate named entity. It is observed that the named entities in the targeted stream usually exhibit a gregarious property, due to the way the targeted stream is constructed. In the second step, TwiNER constructs a random walk model to exploit the gregarious property in the local context derived from the Twitter stream. The highly-ranked segments have a higher chance of being true named entities. We evaluated TwiNER on two sets of real-life tweets simulating two targeted streams. Evaluated using labeled ground truth, TwiNER achieves comparable performance as with conventional approaches in both streams. Various settings of TwiNER have also been examined to verify our global context + local context combo idea.

Question: What is a targeted twitter stream?


Scaling Scrum in a Large Distributed Project. This paper presents a currently ongoing single case study on adopting and scaling Scrum in a large software development project distributed across four sites. The data was gathered by 19 semi-structured interviews of project personnel, including managers, architects, developers and testers. At the time of the interviews the project had grown in size during the past 2,5 years from two collocated Scrum teams to 20 teams located in four countries and employing over 170 persons. In this paper we first describe our research approach and goals. Then we briefly summarize the preliminary results of this ongoing study: we explain how Scrum practices were scaled, as well as discuss the successes and challenges experienced when adopting the agile practices and scaling them, while at the same time growing the project size at a fast pace. Even though this project has been very successful from the business point of view, it has experienced a lot of problems in applying Scrum, especially related to scaling the agile practices. Thus, it seems that adapting Scrum practices to work well in a large distributed setting is challenging.

Question: How to scale Scrum in a large distributed project?


Supporting Situationally Aware Cybersecurity Systems 30 th September 2015. In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for Linked Open Data cloud, we envision UCO to serve as the core for the specialized cyber security Linked Open Data cloud which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use-cases supported by the UCO ontology. To the best of our knowledge, this is the first cyber security ontology that has been mapped to general world ontologies to support broader and diverse security use-cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.

Question: What is the ontology for cyber security?


Data Mining in Sports: A Systematic Review. Data mining technique has attracted attention in the information industry and society as a whole, because of the big amount of data and the imminent need to transform that data into useful information and knowledge. Recently conducted studies with successfully demarcated results using this technique, to estimate several parameters in a variety of domains. However, the effective use of data in some areas is still developing, as is the case of sports, which has shown moderate growth. In this context, the objective of this article is to present a systematic review of the literature about research involving sports data mining. As systematic searches were made out in five databases, resulting in 21 articles that answered a question that grounded this article.

Question: What is a systematic review?


Planar Differential Elliptical UWB Antenna Optimization. A recently proposed optimization procedure, based on the time domain characteristics of an antenna, is exploited to design a planar differential elliptical antenna for ultrawideband (UWB) applications. The optimization procedure aims at finding an antenna not only with low VSWR but also one exhibiting low-dispersion characteristics over the relevant frequency band. Furthermore, since in pulse communications systems the input signal is often of a given form, suited to a particular purpose, the optimization procedure also aims at finding the best antenna for the given input signal form. Specifically, the optimized antenna is designed for high temporal correlation between its electric field intensity signal and the given input signal. The optimization technique followed in this work makes use of genetic algorithm (GA) search concepts. The electromagnetic analysis of the antenna is done by means of a finite-difference time-domain method using the commercially available CST Microwave Studio software

Question: What is the difference between a differential and an elliptical antenna?


A Measurement-Driven Anti-Jamming System for 802.11 Networks. Dense, unmanaged IEEE 802.11 deployments tempt saboteurs into launching jamming attacks by injecting malicious interference. Nowadays, jammers can be portable devices that transmit intermittently at low power in order to conserve energy. In this paper, we first conduct extensive experiments on an indoor 802.11 network to assess the ability of two physical-layer functions, rate adaptation and power control, in mitigating jamming. In the presence of a jammer, we find that: 1) the use of popular rate adaptation algorithms can significantly degrade network performance; and 2) appropriate tuning of the carrier sensing threshold allows a transmitter to send packets even when being jammed and enables a receiver to capture the desired signal. Based on our findings, we build ARES, an Anti-jamming REinforcement System, which tunes the parameters of rate adaptation and power control to improve the performance in the presence of jammers. ARES ensures that operations under benign conditions are unaffected. To demonstrate the effectiveness and generality of ARES, we evaluate it in three wireless test-beds: 1) an 802.11n WLAN with MIMO nodes; 2) an 802.11a/g mesh network with mobile jammers; and 3) an 802.11a WLAN with TCP traffic. We observe that ARES improves the network throughput across all testbeds by up to 150%.

Question: What is the difference between 802.11n and 802.11a?


Detection of parked vehicles from a radar based occupancy grid. For autonomous parking applications to become possible, knowledge about the parking environment is required. Therefore, a real-time algorithm for detecting parked vehicles from radar data is presented. These data are first accumulated in an occupancy grid from which objects are detected by applying techniques borrowed from the computer vision field. Two random forest classifiers are trained to recognize two categories of objects: parallel-parked vehicles and cross-parked vehicles. Performances of the classifiers are evaluated as well as the capacity of the complete system to detect parked vehicles in real world scenarios.

Question: How to detect parked vehicles from radar data?


Harvesting Mid-level Visual Concepts from Large-Scale Internet Images. Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive well organized Google and Bing image data, visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.

Question: What is the state of the art in visual concept learning from Internet images?


Continuous-Scale Kinetic Fluid Simulation. Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving the incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ a novel non-orthogonal central-moment-relaxation model, where we propose an adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction algorithm which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.

Question: What is a continuous-scale kinetic fluid simulation?


Spare parts inventory control : a literature review. Spare parts inventory are needed for maintenance and repair of final products, vehicles, industrial machines and equipments, frequently requiring high investments and significantly affecting customer satisfaction. Inventory management is complex due to the large number of different items and low demands. This article presents a literature review on single location spare parts inventory control, embracing both demand forecasting techniques and inventory control decisions on the different life cycle stages. Overall, the literature review identified the following research opportunities on inventory management: criteria to decide to stock or not an item, how much to order in the first and the last batch, demand forecasting and inventory control models integration and case studies on real applications.

Question: What is the inventory management?


With a flick of the wrist: stretch sensors as lightweight input for mobile devices. With WristFlicker, we detect wrist movement through sets of stretch sensors embedded in clothing. Our system supports wrist rotation (pronation/supination), and both wrist tilts (flexion/extension and ulnar/radial deviation). Each wrist movement is measured by two opposing stretch sensors, mimicking the counteracting movement of muscles. We discuss interaction techniques that allow a user to control a music player through this lightweight input.

Question: How do you detect wrist movement?


Joint optimization of LCMV beamforming and acoustic echo cancellation. Full-duplex hands-free acoustic human/machine interfaces often require the combination of acoustic echo cancellation and speech enhancement in order to suppress acoustic echoes, local interference, and noise. In order to optimally exploit positive synergies between acoustic echo cancellation and speech enhancement, we present in this contribution a combined least-squares (LS) optimization criterion for the integration of acoustic echo cancellation and adaptive linearly-constrained minimum variance (LCMV) beamforming. Based on this optimization criterion, we derive a computationally efficient system based on the generalized sidelobe canceller (GSC), which effectively deals with scenarioes with time-varying acoustic echo paths and simultaneous presence of double-talk of acoustic echoes, local interference, and desired speakers.

Question: How to combine acoustic echo cancellation and speech enhancement?


Social recommendation using Euclidean embedding. Traditional recommender systems assume that all the users are independent, and they usually face the cold start and data sparse problems. To alleviate these problems, social recommender systems use social relations as an additional input to improve recommendation accuracy. Social recommendation follows the intuition that people with social relationships share some kinds of preference towards items. Current social recommendation methods commonly apply the Matrix Factorization (MF) model to incorporate social information into the recommendation process. As an alternative model to MF, we propose a novel social recommendation approach based on Euclidean Embedding (SREE) in this paper. The idea is to embed users and items in a unified Euclidean space, where users are close to both their desired items and social friends. Experimental results conducted on two real-world data sets illustrate that our proposed approach outperforms the state-of-the-art methods in terms of recommendation accuracy.

Question: What is the difference between the matrix factorization and the euclidean embedding?


The inferior alveolar nerve's loop at the mental foramen and its implications for surgery.. BACKGROUND In this study, the authors aimed to identify and measure the anterior extension of the alveolar loop (aAL) and the caudal extension of the alveolar loop (cAL) of the inferior alveolar nerve by using cone-beam computed tomography (CBCT). They also aimed to provide recommendations for surgery in the anterior mandible.   METHODS In this retrospective case study of the frequency and extension of aAL and cAL, the authors evaluated 1,384 mandibular sites in 694 CBCT scans of dentate and partly edentulous patients, performed mainly for further diagnosis before removal of the mandibular third molars between January 2009 and February 2013, by using multiplanar reconstructions.   RESULTS The frequency of aAL was 69.73 percent and of cAL was 100 percent. The mean value for aAL was 1.16 millimeters, with a range of 0.3 to 5.6 mm; the mean value for cAL was 4.11 mm, with a range of 0.25 to 8.87 mm. For aAL, 95.81 percent of the sites showed values of 0 to 3 mm; for cAL, 93.78 percent of the sites showed values of 0.25 to 6 mm. Dentate patients showed statistically significantly higher values for cAL than did partly edentulous patients (P = .043). CBCT resolution had a statistically significant impact on cAL measurements (P = .001), with higher values at higher resolution.   CONCLUSIONS This study showed a high frequency of and large variations in aAL and cAL. In contrast to panoramic radiography, CBCT has been shown to be a reliable tool for identifying and measuring the AL. Therefore, preoperative diagnosis with CBCT is recommended for planning three-dimensional tasks such as implant placement in the vicinity of the mental foramen.   PRACTICAL IMPLICATIONS Owing to the variability of aAL and cAL measurements, it is difficult to recommend reliable safety margins for surgical procedures such as implant placement, bone harvesting or genioplasty Depending on the indication, the cl

Question: What is the difference between aAL and cAL?


Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering. We develop a novel time-selection strategy for iterative image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the filtered image is minimized. The new method is applicable to any images where the noise to be removed is uncorrelated with the signal, under the assumptions that the filter used is suitable for the given type of data, and that neither the additive noise nor the filtering procedure alter the average gray value; no other knowledge (e.g. the noise variance, training data etc.) is needed. We analyse the theoretical properties of the method, then test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.

Question: What is the optimal stopping time for nonlinear diffusion filtering?


Three-dimensional soft-tissue and hard-tissue changes in the treatment of bimaxillary protrusion.. INTRODUCTION Facial convexity related to bimaxillary protrusion is prevalent in many populations. Underlying skeletal protrusion combined with increased dentoalveolar protrusion contributes to facial muscle imbalance and lip incompetence, which is undesirable for many patients. In this study, we evaluated the relationship between soft-tissue and hard-tissue changes in an orthodontically treated Asian population.   METHODS Twenty-four consecutive adult Asian patients (mean age, 24 years), diagnosed with severe bimaxillary dentoalveolar protrusion, were evaluated using pretreatment and posttreatment cone-beam computed tomography. The patients were treated with 4 first premolar extractions followed by anterior retraction with either skeletal or intraoral anchorage. Serial cone-beam computed tomography radiographs were registered on the entire cranial base and fossa. Soft-tissue and hard-tissue changes were determined through landmark displacement and color mapping.   RESULTS Upper lip retraction was concentrated between the nasolabial folds and commissures. Lower lip retraction was accompanied by significant redistribution of soft tissues at pogonion. Soft-tissue changes correlated well with regional facial muscle activity. Significant retractions (2-4 mm) of the soft tissues occurred beyond the midsagittal region. Use of skeletal anchorage resulted in 1.5 mm greater lower lip retraction than intraoral anchorage, with greater retraction of the maxillary and mandibular incisor root apices.   CONCLUSIONS Profound soft-tissue changes accompanied retraction of the anterior dentition with both treatment modalities.

Question: What is the relationship between soft-tissue and hard-tissue changes in the treatment of bimaxillary protrusion?


MRTouch: Adding Touch Input to Head-Mounted Mixed Reality. We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.

Question: How do you add touch input to a head-mounted mixed reality system?


Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. The ABSTRACT is to be in fully-justified italicized text, at the top of the left-hand column, below the author and affiliation information. Use the word "Abstract" as the title, in 12-point Times, boldface type, centered relative to the column, initially capitalized. The abstract is to be in 10point, single-spaced type. Leave two blank lines after the Abstract, then begin the main text. Look at previous CVPR abstracts to get a feel for style and length.

Question: How to write a CVPR abstract?


A Robust Mid-Level Representation for Harmonic Content in Music Signals. When considering the problem of audio-to-audio matching, determining musical similarity using low-level features such as Fourier transforms and MFCCs is an extremely difficult task, as there is little semantic information available. Full semantic transcription of audio is an unreliable and imperfect task in the best case, an unsolved problem in the worst. To this end we propose a robust mid-level representation that incorporates both harmonic and rhythmic information, without attempting full transcription. We describe a process for creating this representation automatically, directly from multi-timbral and polyphonic music signals, with an emphasis on popular music. We also offer various evaluations of our techniques. Moreso than most approaches working from raw audio, we incorporate musical knowledge into our assumptions, our models, and our processes. Our hope is that by utilizing this notion of a musically-motivated mid-level representation we may help bridge the gap between symbolic and audio research.

Question: How to create a mid-level representation for music signals?


Wide-band microstrip-to-coplanar stripline/slotline transitions. Wide-band microstrip line to coplanar stripline (CPS) transitions are proposed. The transition consists of a multisection matching transformer and a quarter-wavelength radial stub for the impedance matching and field matching between the microstrip line and CPS, respectively. The proposed planar transition has the advantages of compact size, wide bandwidth, and straightforward design procedure. Several parameters are studied through simulations and experiments to derive some design guidelines. With the return loss of better than 14 dB, the 1- and 3-dB back-to-back insertion loss bandwidth can cover from 1.4 to 6.6 GHz (1 : 4.7) and from 1.1 to 10.5 GHz (1 : 9.6), respectively. In addition, the microstrip-to-CPS transition is extended to design a microstrip-to-slotline transition by tapering the CPS into a slotline. From 2.7 to 10.4 GHz (1 : 3.85), the back-to-back return loss is better than 15 dB and the insertion loss is less than 3 dB.

Question: What is the difference between a microstrip line and a coplanar stripline?


Learning by Example: Training Users with High-quality Query Suggestions. The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems.

Question: What is the canadian military?


Accurate and dense wide-baseline stereo matching using SW-POC. This paper proposes an accurate and dense wide-baseline stereo matching method using Scaled Window Phase-Only Correlation (SW-POC). The wide-baseline setting of the stereo camera can improve the accuracy of the 3D reconstruction compared with the short-baseline setting. However, it is difficult to find accurate and dense correspondence from wide-baseline stereo images due to its large perspective distortion. Addressing this problem, we employ the SW-POC, which is a correspondence matching method using 1D POC with the concept of Scale Window Matching (SWM). The use of SW-POC makes it possible to find the accurate and dense correspondence from a wide-baseline stereo image pair with low computational cost. We also apply the proposed method to 3D reconstruction using a moving and uncalibrated consumer digital camera.

Question: What is the difference between the short-baseline and wide-baseline settings?


Tensor2Tensor for Neural Machine Translation. Tensor2Tensor is a library for deep learning models that is very well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model. 1 Neural Machine Translation Background Machine translation using deep neural networks achieved great success with sequence-tosequence models Sutskever et al. (2014); Bahdanau et al. (2014); Cho et al. (2014) that used recurrent neural networks (RNNs) with LSTM cells Hochreiter and Schmidhuber (1997). The basic sequence-to-sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed-sized state vector. This is followed by an RNN decoder, which generates the target sentence, one token at a time, from the state vector. While a pure sequence-to-sequence recurrent neural network can already obtain good translation results Sutskever et al. (2014); Cho et al. (2014), it suffers from the fact that the whole input sentence needs to be encoded into a single fixed-size vector. This clearly manifests itself in the degradation of translation quality on longer sentences and was partially overcome in Bahdanau et al. (2014) by using a neural model of attention. Convolutional architectures have been used to obtain good results in word-level neural machine translation starting from Kalchbrenner and Blunsom (2013) and later in Meng et al. (2015). These early models used a standard RNN on top of the convolution to generate the output, which creates a bottleneck and hurts performance. Fully convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et al. (2016). The model in Kaiser and Bengio (2016) (Extended Neural GPU) used a recurrent stack of gated convolutional layers, while the model in Kalchbrenner et al. (2016) (ByteNet) did away with recursion and used left-padded convolutions in the decoder. This id

Question: What is the difference between a neural machine translation model and a neural machine translation model?


Real time detection of lane markers in urban streets. We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

Question: What is a Bezier spline?


How schema and novelty augment memory formation. Information that is congruent with existing knowledge (a schema) is usually better remembered than less congruent information. Only recently, however, has the role of schemas in memory been studied from a systems neuroscience perspective. Moreover, incongruent (novel) information is also sometimes better remembered. Here, we review lesion and neuroimaging findings in animals and humans that relate to this apparent paradoxical relationship between schema and novelty. In addition, we sketch a framework relating key brain regions in medial temporal lobe (MTL) and medial prefrontal cortex (mPFC) during encoding, consolidation and retrieval of information as a function of its congruency with existing information represented in neocortex. An important aspect of this framework is the efficiency of learning enabled by congruency-dependent MTL-mPFC interactions.

Question: How schema and novelty augment memory formation.


Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume. Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.

Question: How does mindfulness training protect against proactive interference?


A vehicular rooftop, shark-fin, multiband antenna for the GPS/LTE/cellular/DSRC systems. A shark-fin rooftop multiband antenna for the automotive industry is proposed. The antenna system receives Right Hand Circularly Polarized (RHCP) satellite signals for the Global Positioning System's (GPS) L1-Band and Vertically Linearly Polarized (VLP) signals for the Long Term Evolution (LTE), the cellular frequencies, the Universal Mobile Telecommunications Systems (UMTS) and the Dedicated Short Range Communications (DSRC) system. A ceramic patch antenna element was used for the GPS signals and a printed monopole utilizing printed ground sleeve technique for the cellular and LTE signals. A study of four potential DSRC antenna elements to determine the appropriate antenna element location within the antenna cover to obtain optimum performance is also presented and discussed in detail. The antenna module was measured on a 1-meter diameter ground plane and on a vehicle roof model. The design and measurements results are presented and discussed below.

Question: What is the purpose of the antenna?


Providing Research Graph Data in JSON-LD Using Schema.org. In this position paper, we describe a pilot project that provides Research Graph records to external web services using JSON-LD. The Research Graph database contains a largescale graph that links research datasets (i.e., data used to support research) to funding records (i.e. grants), publications and researcher records such as ORCID profiles. This database was derived from the work of the Research Data Alliance Working Group on Data Description Registry Interoperability (DDRI), and curated using the Research Data Switchboard open source software. By being available in Linked Data format, the Research Graph database is more accessible to third-party web services over the Internet, which thus opens the opportunity to connect to the rest of the world in the semantic format. The primary purpose of this pilot project is to evaluate the feasibility of converting registry objects in Research Graph to JSON-LD by accessing widely used vocabularies published at Schema.org. In this paper, we provide examples of publications, datasets and grants from international research institutions such as CERN INSPIREHEP, National Computational Infrastructure (NCI) in Australia, and Australian Research Council (ARC). Furthermore, we show how these Research Graph records are made semantically available as Linked Data through using Schema.org. The mapping between Research Graph schema and Schema.org is available on GitHub repository. We also discuss the po©c 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. WWW 2017 Companion, April 3–7, 2017, Perth, Australia. ACM 978-1-4503-4914-7/17/04. http://dx.doi.org/10.1145/3038912.3038914 . tential need for an extension to Schema.org vocabulary for scholarly communication.

Question: What is the purpose of this paper?


Power Transformer Economic Evaluation in Decentralized Electricity Markets. Owing to deregulation, privatization, and competition, estimating financial benefits of electrical power system projects is becoming increasingly important. In other words, it is necessary to assess the project profitability under the light of new developments in the electricity market. In this paper, a detailed methodology for the least cost choice of a distribution transformer is proposed, showing how the higher price of a facility can be traded against its operational cost over its life span. The proposed method involves the incorporation of the discounted cost of transformer losses to their economic evaluation, providing the ability to take into account variable energy cost during the transformer operating lifetime. In addition, the influence of the variability in the energy loss cost is investigated, taking into account a potential policy intended to be adopted by distribution network operators. The method is combined with statistical and probabilistic assessment of electricity price volatility in order to derive its impact on the transformer purchasing policy.

Question: What is the least cost choice of a distribution transformer?


Recurrence Plots of Dynamical Systems .. A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples. In recent years a number of methods have been devised to compute dynamical parameters from time series [ 11. Such parameters are the information dimension, entropy, Liapunov exponents, dimension spectrum, etc. In all cases it is assumed that the time series is obtained from an autonomous dynamical system, i .e. the evolution equations do not contain the time explicitly. It is also assumed that the time series is much longer than the characteristic times of the dynamical system. In the present letter we present a new diagnostic tool which we call recurrence plot; this tool tests the above assumptions, and gives useful information also when they are not satisfied. As the examples will show, the information obtained from recurrence plots is often surprising, and not easily obtainable by other methods. Let x( i ) be the i-th point on the orbit describing a dynamical system in d-dimensional space, for i = 1, ..., N . The recurrence plot is an array of dots in a N x N square, where a dot is placed at (i, j) whenever x( j) is sufficiently close to x( i ) . In practice one proceeds as follows to obtain a recurrence plot from a time series {ui}. First, choosing an embedding dimension d, one constructs the d-dimensional orbit of X(i) by the method of time delays (i.e. if the ui are scalar, x(i) = (ui, u ~ + ~ , ..., u ~ + ~ ~ ) ) . Next, one chooses r(i) such that the ball of radius r(i) centred at x (i) in Rd contains a reasonable number of other points x (j) of the orbit (in the examples below, we have chosen a sequence of increasing radii and stopped when we found at least 10 neighbours). In our computation we have allowed the radius r(i) to depend on the point x ( i ) , and r(i) has been selected by a routine used in our alghorithm for the determination of the Liapunov exponents-see ref. [2]. Finally, one p

Question: What is the recurrence plot?


Cerebral mechanisms of word masking and unconscious repetition priming. We used functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) to visualize the cerebral processing of unseen masked words. Within the areas associated with conscious reading, masked words activated left extrastriate, fusiform and precentral areas. Furthermore, masked words reduced the amount of activation evoked by a subsequent conscious presentation of the same word. In the left fusiform gyrus, this repetition suppression phenomenon was independent of whether the prime and target shared the same case, indicating that case-independent information about letter strings was extracted unconsciously. In comparison to an unmasked situation, however, the activation evoked by masked words was drastically reduced and was undetectable in prefrontal and parietal areas, correlating with participants' inability to report the masked words.

Question: What is the cerebral mechanism of word masking?


A Study of Translation Edit Rate with Targeted Human Annotation. We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We show that the single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU. We also define a human-targeted TER (or HTER) and show that it yields higher correlations with human judgments than BLEU—even when BLEU is given human-targeted references. Our results indicate that HTER correlates with human judgments better than HMETEOR and that the four-reference variants of TER and HTER correlate with human judgments as well as—or better than—a second human judgment does.

Question: What is the correlation between human and machine translation?


Dual Band Microstrip Patch Antenna for Sar Applications. Microstrip patch antennas offer the advantages of thin profile, light weight, low cost, ease of fabrication and compatibility with integrated circuitry, so the antennas are widely used to satisfy demands for polarization diversity and dual-frequency. This paper presents a coaxilly-fed single-layer compact dual band (Cand X-band) microstrip patch antenna for achieving dual-polarized radiation suitable for applications in airborne synthetic aperture radar (SAR) systems. The designed antenna consists of three rectangular patches which are overlapped along their diagonals. Full-wave electromagnetic simulations are performed to accurately predict the frequency response of the antenna. The fabricated antenna achieves an impedance bandwidth of 154 MHz (f0 = 6.83 GHz) and 209 MHz (f0 = 9.73 GHz) for VSWR < 2. Simultaneous use of both frequencies should drastically improve data collection and knowledge of the targets in the SAR system.

Question: What is a dual band microstrip patch antenna?


Where2Stand: A Human Position Recommendation System for Souvenir Photography. People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of "souvenir photo" is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.

Question: How to take a good picture of a scene?


A Fast and Accurate Dependency Parser using Neural Networks. Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.

Question: What is a dependency parser?


A Comprehensive Framework for Image Inpainting. Inpainting is the art of modifying an image in a form that is not detectable by an ordinary observer. There are numerous and very different approaches to tackle the inpainting problem, though as explained in this paper, the most successful algorithms are based upon one or two of the following three basic techniques: copy-and-paste texture synthesis, geometric partial differential equations (PDEs), and coherence among neighboring pixels. We combine these three building blocks in a variational model, and provide a working algorithm for image inpainting trying to approximate the minimum of the proposed energy functional. Our experiments show that the combination of all three terms of the proposed energy works better than taking each term separately, and the results obtained are within the state-of-the-art.

Question: What is the minimum of the proposed energy functional?


Practice Makes Perfect ? When Does Massed Learning Improve Product Usage Proficiency ?. Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.

Question: How does massed learning affect product usage proficiency?


Accuracy of topographic maps derived from ERS-1 interferometric radar. A interferometric radar technique for topographic mapping of surfaces promises a high-resolution approach to the generation of digital elevation models. We present here analyses of data collected by the synthetic aperture radar instrument on-board the ERS-1 satellite on successive orbits. Use of a single satellite in a nearly repeating orbit is attractive for reducing cost and spaceborne hardware complexity; also it permits inference of changes in the surface from the correlation properties of the radar echoes. The data have been reduced to correlation maps and digital elevation models. The correlation maps show that temporal correlation decreases significantly with time, but not necessarily at a constant well-defined rate, likely depending on environmental factors. When correlation among passes remains high, however, it is possible to form digital elevation models. Analyses of noise expected in ERS-1 interferometric data collected over Alaska and the southwestern United States indicate that maps with relative errors less than 5 m rms are possible in some regions. However, orbit uncertainties imply that tie points are required in order to reduce absolute height errors to a similar magnitude. We find that about 6 tie points per 40 X 40 km scene with 5 m rms or better height accuracy are needed to keep systematic map height errors below 5 m rms. The performance of the ERS-1 radar system for topographic applications, though useful for a variety of regional and local discipline studies, may be improved with respect to temporal decorrelation errors and absolute height acuity by modifying the orbit repeat period and incorporating precise orbit determination techniques. The resulting implementation will meet many, but not all, objectives of a global mapping mission.

Question: What is the accuracy of topographic maps derived from ERS-1 interferometric radar?


Fuzzy clustering with artificial bee colony algorithm. In this work, performance of the Artificial Bee Colony Algorithm which is a recently proposed algorithm, has been tested on fuzzy clustering. We applied the Artificial Bee Colony (ABC) Algorithm fuzzy clustering to classify different data sets; Cancer, Diabetes and Heart from UCI database, a collection of classification benchmark problems. The results indicate that the performance of Artificial Bee Colony Optimization Algorithm is successful in fuzzy clustering.

Question: What is the performance of the artificial bee colony algorithm in fuzzy clustering?


Smart Locks: Lessons for Securing Commodity Internet of Things Devices. We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.

Question: What is the security of smart locks?


Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts. The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. This journal requires that authors assign a level of evidence to each submission

Question: What is the purpose of this study?