A Model-Based Method for Computer-Aided Medical Decision-Making. While MYCIN and PIP were under development at Stanford and Tufts! M.I. T., a group of computer scientists at Rutgers University was developing a system to aid in the evaluation and treatment of patients with glaucoma. The group was led by Professor Casimir K ulikou1ski, a researcher with extensive background in mathematical and pattern-recognition approaches to computer-based medical decision making (Nordyke et al., 1971), working within the Rutgers Research Resource on Computers in Biomedicine headed by Professor Saul Amarel. Working collaboratively with Dr. Arin Safir, Professor of Ophthalmology, "who was then based at the Mt. Sinai School of Medicine in New York City, Kulikowski and Sholom Weiss (a graduate student at Rutgers who "went on to become a research scientist there) developed a method of computer-assisted medical decision making that was based on causal-associational network (CASNET) models of disease. Although the work was inspired by the glaucoma domain, the approach had general features that were later refined in the development of the EXPERT system-building tool (see Chapters 18 and 20). A CASNET model consists of three rnain components: observations of a patient, pathophysiological states, and disease classifications. As observations are recorded, they are associated with the appropriate intennediate states. These states, in turn, are typically causally related, thereby forming a network that summarizes the mechanisms of disease. It is these patterns of states in the network that are linked to individual disease classes. Strat-

Question: What is a causal-associational network?


Sparse concept coding for visual analysis. We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. To tackle this problem, matrix factorization techniques, such as Singular Vector Decomposition (SVD) and Non-negative Matrix Factorization (NMF), received an increasing amount of interest in recent years. Matrix factorization is an unsupervised learning technique, which finds a basis set capturing high-level semantics in the data and learns coordinates in terms of the basis set. However, the representations obtained by them are highly dense and can not capture the intrinsic geometric structure in the data. In this paper, we propose a novel method, called Sparse Concept Coding (SCC), for image representation and analysis. Inspired from the recent developments on manifold learning and sparse coding, SCC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on image clustering have shown that the proposed approach provides a better representation with respect to the semantic structure.

Question: What is the difference between SVD and NMF?


Telos: Representing Knowledge About Information Systems. We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies). We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.

Question: What is the canadian military?


Time-Bounded Authentication of FPGAs. This paper introduces a novel technique to authenticate and identify field-programmable gate arrays (FPGAs). The technique uses the reconfigurability feature of FPGAs to perform self-characterization and extract the unique timing of the FPGA building blocks over the space of possible inputs. The characterization circuit is then exploited for constructing a physically unclonable function (PUF). The PUF can accept different forms of challenges including pulsewidth, digital binary, and placement challenges. The responses from the PUF are only verifiable by entities with access to the unique timing signature. However, the authentic device is the only entity who can respond within a given time constraint. The constraint is set by the gap between the speed of PUF evaluation on authentic hardware and simulation of its behavior. A suite of authentication protocols is introduced based on the time-bounded mechanism. We ensure that the responses are robust to fluctuations in operational conditions such as temperature and voltage variations by employing: 1) a linear calibration mechanism that adjusts the clock frequency by a feedback from on-chip temperature and voltage sensor readings, and 2) a differential PUF structure with real-valued responses that cancels out the common impact of variations on delays. Security against various attacks is discussed and a proof-of-concept implementation of signature extraction and authentication are demonstrated on Xilinx Virtex 5 FPGAs.

Question: How to authenticate FPGAs?


Gradual synthesis for static parallelization of single-pass array-processing programs. Parallelizing of software improves its effectiveness and productivity. To guarantee correctness, the parallel and serial versions of the same code must be formally verified to be equivalent. We present a novel approach, called GRASSP, that automatically synthesizes parallel single-pass array-processing programs by treating the given serial versions as specifications. Given arbitrary segmentation of the input array, GRASSP synthesizes a code to determine a new segmentation of the array that allows computing partial results for each segment and merging them. In contrast to other parallelizers, GRASSP gradually considers several parallelization scenarios and certifies the results using constrained Horn solving. For several classes of programs, we show that such parallelization can be performed efficiently. The C++ translations of the GRASSP solutions sped performance by up to 5X relative to serial code on an 8-thread machine and Hadoop translations by up to 10X on a 10-node Amazon EMR cluster.

Question: How to synthesize a parallel program?


The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies.. This article evaluates the suitability of low frequency (LF) heart rate variability (HRV) as an index of sympathetic cardiac control and the LF/high frequency (HF) ratio as an index of autonomic balance. It includes a comprehensive literature review and a reanalysis of some previous studies on autonomic cardiovascular regulation. The following sources of evidence are addressed: effects of manipulations affecting sympathetic and vagal activity on HRV, predictions of group differences in cardiac autonomic regulation from HRV, relationships between HRV and other cardiac parameters, and the theoretical and mathematical bases of the concept of autonomic balance. Available data challenge the interpretation of the LF and LF/HF ratio as indices of sympathetic cardiac control and autonomic balance, respectively, and suggest that the HRV power spectrum, including its LF component, is mainly determined by the parasympathetic system.

Question: What is the utility of low frequency heart rate variability as an index of sympathetic cardiac tone?


Indicator selection for daily equity trading with recurrent reinforcement learning. Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which utilizes the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.

Question: How to select indicators for daily equity trading with recurrent reinforcement learning?


COTS-based software development: Processes and open issues. The work described in this paper is an investigation of COTS-based software development within a particular NASA environment, with an emphasis on the processes used. Fifteen projects using a COTS-based approach were studied and their actual process was documented. This process is evaluated to identify essential differences in comparison to traditional software development. The main differences, and the activities for which projects require more guidance, are requirements definition and COTS selection, high level design, integration and testing. Starting from these empirical observations, a new process and set of guidelines for COTS-based development are developed and briefly presented.

Question: What is COTS-based software development?


The optimal training load for the development of dynamic athletic performance.. This study was performed to determine which of three theoretically optimal resistance training modalities resulted in the greatest enhancement in the performance of a series of dynamic athletic activities. The three training modalities included 1) traditional weight training, 2) plyometric training, and 3) explosive weight training at the load that maximized mechanical power output. Sixty-four previously trained subjects were randomly allocated to four groups that included the above three training modalities and a control group. The experimental groups trained for 10 wk performing either heavy squat lifts, depth jumps, or weighted squat jumps. All subjects were tested prior to training, after 5 wk of training and at the completion of the training period. The test items included 1) 30-m sprint, 2) vertical jumps performed with and without a countermovement, 3) maximal cycle test, 4) isokinetic leg extension test, and 5) a maximal isometric test. The experimental group which trained with the load that maximized mechanical power achieved the best overall results in enhancing dynamic athletic performance recording statistically significant (P < 0.05) improvements on most test items and producing statistically superior results to the two other training modalities on the jumping and isokinetic tests.

Question: What is the optimal training load for the development of dynamic athletic performance?


Super-resolution through neighbor embedding. In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.

Question: What is the difference between super-resolution and image restoration?


Peer victimization, psychosocial adjustment, and physical activity in overweight and at-risk-for-overweight youth.. OBJECTIVE To examine the relationship between peer victimization and child and parent reports of psychosocial adjustment and physical activity in a clinical sample of at-risk-for-overweight and overweight children and adolescents.   METHODS The Schwartz Peer Victimization Scale, Children's Depression Inventory-Short Form, Multidimensional Anxiety Scale for Children, Social Physique Anxiety Scale, PACE+ Adolescent Physical Activity Measure, and Asher Loneliness Scale were administered to 92 children and adolescents (54 females) aged 8-18 years. The youth's parent/guardian completed the Child Behavior Checklist.   RESULTS Peer victimization was positively related to child-reported depression, anxiety, social physique anxiety, and loneliness, and parent-reported internalizing and externalizing symptoms. Peer victimization was negatively related to physical activity. Depressive symptoms and loneliness mediated the relations between peer victimization and physical activity.   CONCLUSION Recognition of the magnitude of the problem and the means of evaluating for peer victimization is important for clinicians who work with overweight youth. Assessing peer experiences may assist in understanding rates of physical activity and/or past nonadherence to clinician recommendations.

Question: What is the relationship between peer victimization and physical activity in overweight youth?


Domestic food and sustainable design: a study of university student cooking and its impacts. In four university student kitchens over twenty-one days, we captured participants' food preparation activity, quantified the greenhouse gas emissions and direct energy connected to the food and cooking, and talked to participants about their food practices. Grounded in this uniquely detailed micro-account, our findings inform sustainable design for cooking and eating at home and quantify the potential impacts. We outline the relation of the impacts to our participants' approaches to everyday food preparation, the organisation of their time, and the role of social meals. Our technique allows evaluation of opportunities for sustainable intervention design: at the appliance, in the digitally-mediated organisation of meals and inventory management, and more broadly in reflecting upon and reshaping diet.

Question: What is the relation of the impacts to our participants' approaches to everyday food preparation?


Cloud-assisted industrial cyber-physical systems: An insight. The development of industrialization and information communication technology (ICT) has deeply changed our way of life. In particular, with the emerging theory of ''Industry 4.0", the integration of cloud technologies and industrial cyber-physical systems (ICPS) becomes increasingly important, as this will greatly improve the manufacturing chain and business services. In this paper, we first describe the development and character of ICPS. ICPS will inevitably play an important role in manufacturing, sales, and logistics. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. Then, we present a service-oriented ICPS model. With the support of the cloud, infrastructure platform and service application, ICPS will promote the manufacturing efficiency, increase quality of production, enable a sustainable industrial system and more environmentally friendly businesses. Thirdly, we focus on some key enabling technologies, which are critical in supporting smart factories. These key enabling technologies will also help companies to realize high quality, high output, and low cost. Finally, we talk about some challenges of ICPS implementation and the future work. 2015 Elsevier B.V. All rights reserved.

Question: What is cloud-assisted industrial cyber-physical systems?


Hidden Markov model-based speech emotion recognition. In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English language is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. As a basis of comparison the similar judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.

Question: What is the difference between a continuous and discrete hidden Markov model?


Anatomical Basis for Safe and Effective Volumization of the Temple.. BACKGROUND One of the earliest but often unaddressed signs of facial aging is volume loss in the temple. Treatment of the area can produce satisfying results for both patient and practitioner.   OBJECTIVE Safe injection requires explicit knowledge of the anatomy to avoid complications related to the multitude of vessels that course throughout the region at various depths. The authors aim to detail the anatomy of the area and provide a safe and easy-to-follow method for injection.   MATERIALS AND METHODS The authors review the relevant anatomy of the temporal region and its application to cosmetic filler injections.   RESULTS The authors describe an easy-to-follow approach for a safe and effective injection window based on numerous anatomical studies. Injection in this area is not without risk, including potential blindness. The authors review the potential complications and their treatments.   CONCLUSION Hollowing of the temple is an early sign of aging that, when corrected, can lead to significant patient and practitioner satisfaction. Proper anatomically knowledge is required to avoid potentially severe complications. In this study, the authors present a reliable technique to safely and effectively augment this often undertreated area of the aging face.

Question: What is the anatomy of the temple?


Modeling Human Education Data: From Equation-Based Modeling to Agent-Based Modeling. Agent-based simulation is increasingly used to analyze the performance of complex systems. In this paper we describe results of our work on one specific agent-based model, showing how it can be validated against the equation-based model from which it was derived, and demonstrating the extent to which it can be used to derive additional results over and above those that the equation-based model can provide.

Question: How can agent-based modeling be used to analyze the performance of complex systems?


Repeated labeling using multiple noisy labelers. This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.

Question: What is the cost of processing the unlabeled data?


Opinion observer: analyzing and comparing opinions on the Web. The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.

Question: What is the difference between a customer review and a consumer review?


Effects of prior hamstring strain injury on strength, flexibility, and running mechanics.. BACKGROUND Previous studies have shown evidence of residual scar tissue at the musculotendon junction following a hamstring strain injury, which could influence re-injury risk. The purpose of this study was to investigate whether bilateral differences in strength, neuromuscular patterns, and musculotendon kinematics during sprinting are present in individuals with a history of unilateral hamstring injury, and whether such differences are linked to the presence of scar tissue.   METHODS Eighteen subjects with a previous hamstring injury (>5 months prior) participated in a magnetic resonance (MR) imaging exam, isokinetic strength testing, and a biomechanical assessment of treadmill sprinting. Bilateral comparisons were made for peak knee flexion torque, angle of peak torque, and the hamstrings:quadriceps strength ratio, as well as muscle activations and peak hamstring stretch during sprinting. MR images were used to measure the volumes of the proximal tendon/aponeurosis of the biceps femoris, with asymmetries considered indicative of scar tissue.   FINDINGS A significantly enlarged proximal biceps femoris tendon volume was measured on the side of prior injury. However, no significant differences between the previously injured and uninjured limbs were found in strength measures, peak hamstring stretch, or muscle activation patterns. Further, the degree of asymmetry in tendon volume was not correlated to any of the functional measures.   INTERPRETATION Injury-induced changes in morphology do not seem discernable from strength measures, running kinematics, or muscle activation patterns. Further research is warranted to ascertain whether residual scarring alters localized musculotendon mechanics in a way that may contribute to the high rates of muscle re-injury that are observed clinically.

Question: What is the volume of the proximal tendon/aponeurosis of the biceps femoris?


Adding Chinese Captions to Images. This paper extends research on automated image captioning in the dimension of language, studying how to generate Chinese sentence descriptions for unlabeled images. To evaluate image captioning in this novel context, we present Flickr8k-CN, a bilingual extension of the popular Flickr8k set. The new multimedia dataset can be used to quantitatively assess the performance of Chinese captioning and English-Chinese machine translation. The possibility of re-using existing English data and models via machine translation is investigated. Our study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world. Data is publicly available at http://tinyurl.com/flickr8kcn

Question: How to add chinese captions to images?


The one-time pad revisited. The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.

Question: What is the one-time pad?


Integration of face recognition and sound localization for a smart door phone system. This paper proposes a smart system using both face recognition and sound localization techniques to identify the faces of visitors from a door phone in much efficient and accurate ways. This system is effectively used to recognize the faces when their locations are out of the boundaries of the camera scope of the door phone. The smart door phone system proposed in this paper uses a visitor's voice source to rotate the camera to his face and then to recognize the face accurately. The integrated system has been designed with one FPGA(Field Programmable Gate Array) chip and tested for actual use in door phone environments.

Question: How to integrate face recognition and sound localization for a smart door phone system?


Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria. This paper proposes a classification technique using conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria which improves the accuracy of detecting Arrhythmia using Electrocardiogram (ECG) data. ECG is the most widely used first line clinical instrument to record the electrical activities of the heart. The data-set from UC Irvine (UCI) Machine Learning Repository was used to implement a multi-class classification for different types of heart abnormalities. After implementing rigorous data preprocessing and feature selection techniques,different machine learning algorithms such as Neural Networks, Decision trees, Random Forest, Gradient Boosting and Support Vector Machines were used. Maximum experimental accuracy of 84.82% was obtained via the conjunction of SVM and Gradient Boosting. A further improvement in accuracy was obtained by validating the factors which were important for doctors to decide between normal and abnormal heart conditions.The performance of classification is evaluated using measures such as confusion matrix, kappa-score, confidence interval, Area under curve (AUC) and overall-accuracy. Key–Words: Machine Learning, Arrhythmia Classification, ECG, Neural Networks, SVM, Gradient Boosting

Question: How to classify arrhythmia using machine learning?


Blockstack : A New Decentralized Internet. The traditional internet has many central points of failure and trust, like (a) the Domain Name System (DNS) servers, (b) public-key infrastructure, and (c) end-user data stored on centralized data stores. We present the design and implementation of a new internet, called Blockstack, where users don't need to trust remote servers. We remove any trust points from the middle of the network and use blockchains to secure critical data bindings. Blockstack implements services for identity, discovery, and storage and can survive failures of underlying blockchains. The design of Blockstack is informed by three years of experience from a large blockchain-based production system. Blockstack gives comparable performance to traditional internet services and enables a much-needed security and reliability upgrade to the traditional internet.

Question: What is blockstack?


Multilayered substrate integrated waveguide (MSIW) elliptic filter. This letter presents the design and experiment of a novel elliptic filter based on the multilayered substrate integrated waveguide (MSIW) technique. A C-band elliptic filter with four folded MSIW cavities is simulated by using high frequency structure simulator software and fabricated with a two-layer printed circuit board process, the measured results show good performance and in agreement with the simulated results.

Question: What is a multilayered substrate integrated waveguide?


User involvement: a review of the benefits and challenges. Users can be involved in the design process in several ways. The author focuses on user-centred design, participatory design, ethnography, and contextual design. She briefly describes each approach and cites the corresponding literature. The expected benefits of these approaches highly overlap. Methods based on early user involvement promise to avoid the implementation of costly features that users do not want or cannot use. Participatory methods promise to improve the user satisfaction and the acceptance of a system. Nearly all approaches intend to improve the overall usability of the system and by this means increase the productivity and decrease the training costs and the need of user support.

Question: What are the benefits of user involvement?


SECURITY ENHANCEMENT OF ADVANCED ENCRYPTION STANDARD (AES) USING TIME-BASED DYNAMIC KEY GENERATION. Login is the first step which is conducted every time we access to a system and only granted to those who are entitled. Login is very important and it is a part of the security of a system. Registered user or guest and administrator are two kinds of users that have the privilege and access. Nowadays, wrongdoers are always on the dark side and frequently try to gain access to the system. To provide a better security, it is important to enhance the access mechanism and to evaluate the authentication process. Message-Digest 5 (MD5) is one of the algorithms that commonly used in the login system. Although it has been so popular, but the algorithm is still vulnerable to dictionary attacks and rainbow tables. In addition to the hash function algorithm, Advanced Encryption Standard (AES) algorithm alternatively can be the best choice in the login authentication process. This study is proposed to develop a dynamic key generation on the AES algorithm using the function of time. The experimental results obtained that AES key can be generated at random based on the value of the time when a user logs in with a particular active period. In this implementation, the security authentication becomes stronger because of changes in key generating chipertext changes for each encryption process. Based on the time as a valuable benchmark, the result shown that AES encryption-decryption process is relatively fast with a time average about 0.0023 s.

Question: How to generate a dynamic key for AES encryption?


High quality lip-sync animation for 3D photo-realistic talking head. We propose a new 3D photo-realistic talking head with high quality, lip-sync animation. It extends our prior high-quality 2D photo-realistic talking head to 3D. An a/v recording of a person speaking a set of prompted sentences with good phonetic coverage for ~20-minutes is first made. We then use a 2D-to-3D reconstruction algorithm to automatically adapt a general 3D head mesh model to the person. In training, super feature vectors consisting of 3D geometry, texture and speech are augmented together to train a statistical, multi-streamed, Hidden Markov Model (HMM). The HMM is then used to synthesize both the trajectories of head motion animation and the corresponding dynamics of texture. The resultant 3D talking head animation can be controlled by the model predicted geometric trajectory while the articulator movements, e.g., lips, are rendered with dynamic 2D texture image sequences. Head motions and facial expression can also be separately controlled by manipulating corresponding parameters. In a real-time demonstration, the life-like 3D talking head can take any input text, convert it into speech and render lip-synced speech animation photo-realistically.

Question: How to make a 3D talking head?


The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger. We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first1 for all but two languages (Hungarian and Romanian).

Question: What is the system for automatic identification of verbal multiword expressions?


Smart Health Monitoring Systems: An Overview of Design and Modeling. Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

Question: What is the difference between a smart health monitoring system and a smart home monitoring system?


Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. We introduce the first temporal-difference learning algorithms that converge with smooth value function approximators, such as neural networks. Conventional temporal-difference (TD) methods, such as TD(λ), Q-learning and Sarsa have been used successfully with function approximation in many applications. However, it is well known that off-policy sampling, as well as nonlinear function approximation, can cause these algorithms to become unstable (i.e., the parameters of the approximator may diverge). Sutton et al. (2009a, 2009b) solved the problem of off-policy learning with linear TD algorithms by introducing a new objective function, related to the Bellman error, and algorithms that perform stochastic gradient-descent on this function. These methods can be viewed as natural generalizations to previous TD methods, as they converge to the same limit points when used with linear function approximation methods. We generalize this work to nonlinear function approximation. We present a Bellman error objective function and two gradient-descent TD algorithms that optimize it. We prove the asymptotic almost-sure convergence of both algorithms, for any finite Markov decision process and any smooth value function approximator, to a locally optimal solution. The algorithms are incremental and the computational complexity per time step scales linearly with the number of parameters of the approximator. Empirical results obtained in the game of Go demonstrate the algorithms' effectiveness.

Question: What is the asymptotic almost-sure convergence of the algorithms?


Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching. Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150m probe vs. 1m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.

Question: How large is the canadian military?


Statistical language modeling using the CMU-cambridge toolkit. The CMU Statistical Language Modeling toolkit was re leased in in order to facilitate the construction and testing of bigram and trigram language models It is currently in use in over academic government and industrial laboratories in over countries This paper presents a new version of the toolkit We outline the con ventional language modeling technology as implemented in the toolkit and describe the extra e ciency and func tionality that the new toolkit provides as compared to previous software for this task Finally we give an exam ple of the use of the toolkit in constructing and testing a simple language model

Question: What is the difference between the old and new version of the toolkit?


Learning Attitudes and Attributes from Multi-aspect Reviews. Most online reviews consist of plain-text feedback together with a single numeric score. However, understanding the multiple `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audio book presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we uncover which parts of a review discuss which of the rated aspects. Second, we summarize reviews by finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we recover ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.

Question: What is the difference between a review and a review?


Phase III clinical trial of thalidomide plus dexamethasone compared with dexamethasone alone in newly diagnosed multiple myeloma: a clinical trial coordinated by the Eastern Cooperative Oncology Group.. PURPOSE To determine if thalidomide plus dexamethasone yields superior response rates compared with dexamethasone alone as induction therapy for newly diagnosed multiple myeloma.   PATIENTS AND METHODS Patients were randomly assigned to receive thalidomide plus dexamethasone or dexamethasone alone. Patients in arm A received thalidomide 200 mg orally for 4 weeks; dexamethasone was administered at a dose of 40 mg orally on days 1 to 4, 9 to 12, and 17 to 20. Cycles were repeated every 4 weeks. Patients in arm B received dexamethasone alone at the same schedule as in arm A.   RESULTS Two hundred seven patients were enrolled: 103 were randomly assigned to thalidomide plus dexamethasone and 104 were randomly assigned to dexamethasone alone; eight patients were ineligible. The response rate with thalidomide plus dexamethasone was significantly higher than with dexamethasone alone (63% v 41%, respectively; P = .0017). The response rate allowing for use of serum monoclonal protein levels when a measurable urine monoclonal protein was unavailable at follow-up was 72% v 50%, respectively. The incidence rates of grade 3 or higher deep vein thrombosis (DVT), rash, bradycardia, neuropathy, and any grade 4 to 5 toxicity in the first 4 months were significantly higher with thalidomide plus dexamethasone compared with dexamethasone alone (45% v 21%, respectively; P < .001). DVT was more frequent in arm A than in arm B (17% v 3%); grade 3 or higher peripheral neuropathy was also more frequent (7% v 4%, respectively).   CONCLUSION Thalidomide plus dexamethasone demonstrates significantly superior response rates in newly diagnosed myeloma compared with dexamethasone alone. However, this must be balanced against the greater toxicity seen with the combination.

Question: What is the difference between thalidomide and dexamethasone?


Analysing B2B electronic procurement benefits: information systems perspective. Abstract Purpose – This paper aims to present electronic procurement benefits identified in four case companies from the information technology (IT), hi-tech sector. Design/methodology/approach – Multi-case study design was applied. The benefits reported in the companies were analysed and classified according to taxonomies from the information systems discipline. Finally, a new benefits classification was proposed. The framework was developed based on information systems literature. Findings – The research confirmed difficulties with benefits evaluation, as, apart from operational benefits, non-financial, intangible benefits at strategic level were also identified. Traditional evaluation methods are unable to capture all benefits categories, especially at strategic level. New taxonomy was created, which allows evaluation of the complex e-procurement impact. In the proposed taxonomy, e-procurement benefits are classified according to their level (operational, tactical, strategic), area of impact, applying scorecard dimensions (customer, process, financial, learning and growth). In addition the benefits characteristic is captured (tangible, intangible, financial and non-financial). Research limitations/implications – Research is based on four case studies only. Findings are specific to case companies and the environment in which they operate. The framework should be tested further in different contexts. Practical implications – The new taxonomy allows evaluation of the complex e-procurement impact, demonstrating that benefits achieved do not concern merely the financial impact. The framework can be applied to preparing new systems implementation as well as to evaluating existing systems. Originality/value – The paper applies information systems frameworks to the electronic procurement field, which allows one to look at e-procurement systems considering its complex impact. The framework can

Question: What is the difference between a taxonomy and a framework?


Memory Interference as a Determinant of Language Comprehension. The parameters of the human memory system constrain the operation of language comprehension processes. In the memory literature, both decay and interference have been proposed as causes of forgetting; however, while there is a long history of research establishing the nature of interference effects in memory, the effects of decay are much more poorly supported. Nevertheless, research investigating the limitations of the human sentence processing mechanism typically focus on decay-based explanations, emphasizing the role of capacity, while the role of interference has received comparatively little attention. This paper reviews both accounts of difficulty in language comprehension by drawing direct connections to research in the memory domain. Capacity-based accounts are found to be untenable, diverging substantially from what is known about the operation of the human memory system. In contrast, recent research investigating comprehension difficulty using a retrieval-interference paradigm is shown to be wholly consistent with both behavioral and neuropsychological memory phenomena. The implications of adopting a retrieval-interference approach to investigating individual variation in language comprehension are discussed.

Question: What is the difference between decay and interference?


Compatibility Family Learning for Item Recommendation and Generation. Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions. However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as "Compatibility Family". In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.

Question: How do I learn to generate images of compatible items?


Emotion-based music recommendation by affinity discovery from film music. 0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.09.042 q Part of the content of this paper has been publi International Conference on Multimedia, 2005. * Corresponding author. Tel.: +886 2 29393091x67 E-mail address: mkshan@cs.nccu.edu.tw (M.-K. Sh With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user's preference on music. However, sometimes, it might better meet users' requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average. 2008 Elsevier Ltd. All rights reserved.

Question: What is the music emotion model?


On the Existence of a Spectrum of Policies that Subsumes the Least Recently Used (LRU) and Least Frequently Used (LFU) Policies. Sam H. Nohs Sang Lyul Mint t Department of Computer Engineering Seoul National University Seoul 151-742, Korea http://ssrnet.snu.ac.kr http://archi.snu.ac.kr We show that there exists a spectrum of block replacement policies that subsumes both the Least Recently Used (LRU) and the Least Frequently Used (LFU) policies. The spectrum is formed according to how much more weight we give to the recent history than to the older history, and is referred to as the LRFU (Least Recently/Frequently Used) policy. Unlike many previous policies that use limited history to make block replacement decisions, the LRFU policy uses the complete reference history of blocks recorded during their cache residency. Nevertheless, the LRFU requires only a few words for each block to maintain such history. This paper also describes an implementation of the LRFU that again subsumes the LRU and LFU implementations. The LRFU policy is applied to buffer caching, and results from trace-driven simulations show that the LRFU performs better than previously known policies for the workloads we considered. This point is reinforced by results from our integration of the LRFU into the FreeBSD operating system.

Question: What is the spectrum of policies that subsumes the least recently used and least frequently used policies?


Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context. The most effective superresolution methods proposed in the literature require precise knowledge of the so-called point spread function of the imager, while in practice its accurate estimation is nearly impossible. This paper presents a new superresolution method, whose main feature is its ability to account for the scant knowledge of the imager point spread function. This ability is based on representing this imprecise knowledge via a non-additive neighborhood function. The superresolution reconstruction algorithm transfers this imprecise knowledge to output by producing an imprecise (interval-valued) high-resolution image. We propose some experiments illustrating the robustness of the proposed method with respect to the imager point spread function. These experiments also highlight its high performance compared with very competitive earlier approaches. Finally, we show that the imprecision of the high-resolution interval-valued reconstructed image is a reconstruction error marker.

Question: How to estimate the point spread function of an image sensor?


Exemplar-Based Portrait Style Transfer. Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits. Previous methods use facial landmarks to densely align the content face with the style face to reduce the artifacts. However, they can only handle the facial region. As for the whole image, building the dense correspondence is difficult and may easily introduce errors. In this paper, we propose a robust approach for portrait style transfer that gets rid of dense correspondence. Our approach is based on the guided image synthesis framework. We propose three novel guidance maps for the synthesis process. Contrary to former methods, these maps do not require the dense correspondence between content image and style image, which allows our method to handle the whole portrait photo instead of facial region only. In comparison with recent neural style transfer methods, our method achieves more pleasing results and preserves more texture details. Extensive experiments demonstrate our advantage over former methods on portrait style transfer.

Question: How to transfer the style of an example image to a content image?


Improvement of pixel electrode circuit for active-matrix OLED by application of reversed-biased voltage. In this paper, an improved ac pixel electrode circuit for active-matrix organic light-emitting display (AMOLED) has been proposed by adding a thin-film transistor. This circuit can provide an ac driving mode for AMOLED and makes the OLED in a reversed-biased voltage during the reverse cycle. And a circuit design for understanding ac driving mode was presented. The circuit simulation results indicate that this circuit is feasible. The circuit structure is practical for the AMOLED pixel driver; it can improve the performance of OLED.

Question: What is the purpose of the circuit?


Body Area Sensor Networks: Requirements, Operations, and Challenges. This article provides an overview of body area sensor networks (BASNs), an application of wireless technology that changed health care to suit the comfort of the population. There are various applications of BASNs and attempts have been made to make the human body a channel for wireless communication. BASNs employ a three-tiered architectural system that requires various technical requirements for its optimal and efficient operation. Energy consumption is one of the major issues that is currently being addressed through self-harvesting and many other techniques. Even with the benefits at hand, there are various issues such as interference and eavesdropping that BASNs have to tackle. Biometrics is a widely used solution. Researchers are also working on various ambitious projects that deal with improving deep brain simulation, heart regulation, drug delivery, and prosthetic actuation to use BASN effectively.

Question: What is a body area sensor network?


Robust Inference via Generative Classifiers for Handling Noisy Labels. Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier (RoG), applicable to any discriminative (e.g., softmax) neural classifier pre-trained on noisy datasets. In particular, we induce a generative classifier on top of hidden feature spaces of the pre-trained DNNs, for obtaining a more robust decision boundary. By estimating the parameters of generative classifier using the minimum covariance determinant estimator, we significantly improve the classification accuracy with neither re-training of the deep model nor changing its architectures. With the assumption of Gaussian distribution for features, we prove that RoG generalizes better than baselines under noisy labels. Finally, we propose the ensemble version of RoG to improve its performance by investigating the layer-wise characteristics of DNNs. Our extensive experimental results demonstrate the superiority of RoG given different learning models optimized by several training techniques to handle diverse scenarios of noisy labels.

Question: How to handle noisy labels in deep learning?


Question: What is a holistically-nested edge detection?


Representation Learning: A Review and New Perspectives. The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

Question: What is representation learning?


A Naturalistic Open Source Movie for Optical Flow Evaluation. Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the imageand flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

Question: What is a naturalistic open source movie?


Facial Component-Landmark Detection With Weakly-Supervised LR-CNN. In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.

Question: Facial component-landmark detection with weakly supervised LR-CNN


Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix. This paper presents a novel size reduction technique for designing a Butler matrix using a new type of coupler. This coupler is half the length of a conventional coupler and has a swapped port characteristic wherein the locations of the isolation and coupled ports are switched. These features make the new coupler, called the "swapped port coupler," effective for reducing the size of the Butler matrix. The behavior of the swapped port coupler was mathematically analyzed, and its design equation was derived. Furthermore, the proposed coupler was employed to design a miniaturized Butler matrix. The area of this Butler matrix was 70 × 70 mm2, and its operating frequency was 1 GHz. When the area was normalized to the frequency, the area of the proposed Butler matrix was 10% of a conventional Butler matrix.

Question: What is a Butler matrix?


Explaining consumer satisfaction of services: The role of innovativeness and emotion in an electronic mediated environment. a r t i c l e i n f o The services section has started to dominate economic activity in many industrialized economies since the last decade. The growth of services in Electronic Mediated Environment (EME) has changed the manner in which firms and consumers interact. Drawing on such theoretical foundations as trust, risk perceptions, trying, emotion, and satisfaction, this study proposes an integrative research model to investigate the relationships among these constructs in the EME context. We collected data from 415 consumers and found that consumer innovativeness and emotion are significant antecedents of trust, risk perception of service, perceived benefits, and service quality. These important factors will influence consumer satisfaction in EME. This empirical investigation breaks a new ground for future studies to understand and gauge the influence of innovativeness and emotion in emerging IT artifacts such as EME services. The service sector has become quantitatively the most essential sector in the world economy over the last decade. It now leads economic activities in a wide variety of industrialized economies. According to CIA World Factbook [14], the service industry accounted for 76.8% and 73.1% of the U.S. and European Union gross domestic product in 2011, respectively. Concurrently, there are more service companies on the list of Future 500 companies than before. Given the precipitously growing rate of the service section, technologically empowered advancements of information technology (IT) and the ever developing Internet are constantly galvanizing today's organizations to more effectively and efficiently coordinate with their constituents via the value-added electronic services (e-services) conduit. Furthermore, the mushrooming growth of e-commerce reflects the increasingly paramount role of IT in e-services while IT spurs the i

Question: What is the role of innovativeness and emotion in an electronic mediated environment?


A Future-Adaptable Password Scheme. Many authentication schemes depend on secret passwords. Unfortunately, the length and randomness of user-chosen passwords remain xed over time. In contrast, hardware improvements constantly give attackers increasing computational power. As a result, password schemes such as the traditional UNIX user-authentication system are failing with time. This paper discusses ways of building systems in which password security keeps up with hardware speeds. We formalize the properties desirable in a good password system, and show that the computational cost of any secure password scheme must increase as hardware improves. We present two algorithms with adaptable cost|eksblow sh, a block cipher with a purposefully expensive key schedule, and bcrypt, a related hash function. Failing a major breakthrough in complexity theory, these algorithms should allow password-based systems to adapt to hardware improvements and remain secure well into the future.

Question: What is a password scheme?


Sugar: Secure GPU Acceleration in Web Browsers. Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

Question: What is the difference between a virtual graphics plane and a virtual GPU?


Comparison of Shunt and Series/Shunt nMOS Single-Pole Double-Throw Switches for X-Band Phased Array T/R Modules. This paper compares the performance of shunt and series/shunt single-pole double-throw nMOS switches designed in a 0.13 mum SiGe BiCMOS process for X-band phased array transmit/receive modules. From 8.5 to 10.5 GHz, the worst case return loss, insertion loss, and isolation are 14.5, 1.89, and 20.5 dB, respectively, for the reflective shunt switch, and 22.2, 2.33, and 22.5 dB, respectively, for the absorptive series/shunt switch. Both switches exhibit an IIP3 of about 28 dBm and dissipate no dc power. The performance of these switches are comparable to other CMOS switches found in triple well technologies, on non-standard substrates, using special device structures, or using extra dc biases

Question: What is the difference between a shunt and series/shunt switch?


A micro-video recommendation system based on big data. With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, this paper proposes a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Bid Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, this paper improves the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system.

Question: What is a micro-video recommendation system?


DeepDefense: Training Deep Neural Networks with Improved Robustness. Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in securitycritical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results will be made publicly available.

Question: How to train a deep neural network to resist adversarial attacks?


Mobile application testing matrix and challenges. The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge from.

Question: What is the difference between automated and manual testing?


Multiresolution Tree Networks for 3D Point Cloud Processing. We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed treestructured encoders can be used to classify shapes and outperform existing pointbased architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.

Question: What is a tree-structured network?


Crohn's disease of the vulva in a 10-year-old girl.. Crohn's disease may involve all parts of the gastrointestinal tract and may often involve other organs as well. These non-intestinal affections are termed extraintestinal manifestations. Vulval involvement is an uncommon extraintestinal manifestation of Crohn's disease, and it is very rare in children. Patients with vulval CD typically present with erythema and edema of the labia majora, which progresses to extensive ulcer formation. Vulval Crohn's disease can appear before or after intestinal problems or it may occur simultaneously. We present a 10-year-old girl with intestinal Crohn's disease complicated with perianal skin tags and asymptomatic unilateral labial hypertrophy. The course of her lesion was independent of the intestinal disease and responded significantly to medical treatment including azathioprine and topical steroid. We emphasize that although vulval involvement in childhood is uncommon, Crohn's disease must be considered in the differential diagnosis of nontender, red, edematous lesions of the genital area.

Question: What is the course of the lesion?


Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing. Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to learn the communication strategy and the control policy for multiple agents. However, when implementing the communication for real-world multi-agent applications, there is a more practical limited-bandwidth restriction, which has been largely ignored by the existing DRL-based methods. Specifically, agents trained by most previous methods keep sending messages incessantly in every control cycle; due to emitting too many messages, these methods are unsuitable to be applied to the real-world systems that have a limited bandwidth to transmit the messages. To handle this problem, we propose a gating mechanism to adaptively prune unprofitable messages. Results show that the gating mechanism can prune more than 80% messages with little damage to the performance. Moreover, our method outperforms several state-of-the-art DRL-based and rule-based methods by a large margin in both the real-world packet routing tasks and four benchmark tasks.

Question: How to learn communication strategy for multi-agent?


Applying Business Intelligence Concepts to Medicaid Claim Fraud Detection. U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state's existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data.

Question: How can we detect fraud in healthcare-related data?


Learning continuous grasp stability for a humanoid robot hand based on tactile sensing. Grasp stability estimation with complex robots in environments with uncertainty is a major research challenge. Analytical measures such as force closure based grasp quality metrics are often impractical because tactile sensors are unable to measure contacts accurately enough especially in soft contact cases. Recently, an alternative approach of learning the stability based on examples has been proposed. Current approaches of stability learning analyze the tactile sensor readings only at the end of the grasp attempt, which makes them somewhat time consuming, because the grasp can be stable already earlier. In this paper, we propose an approach for grasp stability learning, which estimates the stability continuously during the grasp attempt. The approach is based on temporal filtering of a support vector machine classifier output. Experimental evaluation is performed on an anthropomorphic ARMAR-IIIb. The results demonstrate that the continuous estimation provides equal performance to the earlier approaches while reducing the time to reach a stable grasp significantly. Moreover, the results demonstrate for the first time that the learning based stability estimation can be used with a flexible, pneumatically actuated hand, in contrast to the rigid hands used in earlier works.

Question: How do you estimate grasp stability?


Morbidity in patients with hidradenitis suppurativa.. BACKGROUND Although skin diseases are often immediately visible to both patients and society, the morbidity they cause is only poorly defined. It has been suggested that quality-of-life measures may be a relevant surrogate measure of skin disease. Hidradenitis suppurativa (HS) leads to painful eruptions and malodorous discharge and is assumed to cause a significant degree of morbidity. The resulting impairment of life quality has not previously been quantitatively assessed, although such an assessment may form a pertinent measure of disease severity in HS.   OBJECTIVES To measure the impairment of life quality in patients with HS.   METHODS In total, 160 patients suffering from HS were approached. The following data were gathered: quality-of-life data (Dermatology Life Quality Index, DLQI questionnaire), basic demographic data, age at onset of the condition and the average number of painful lesions per month.   RESULTS One hundred and fourteen patients participated in the study. The mean +/- SD age of the patients was 40.9 +/- 11.7 years, the mean +/- SD age at onset 21.8 +/- 9.9 years and the mean +/- SD duration of the disease 18.8 +/- 11.4 years. Patients had a mean +/- SD DLQI score of 8.9 +/- 8.3 points. The highest mean score out of the 10 DLQI questions was recorded for question 1, which measures the level of pain, soreness, stinging or itching (mean 1.55 points, median 2 points). Patients experienced a mean of 5.1 lesions per month.   CONCLUSIONS HS causes a high degree of morbidity, with the highest scores obtained for the level of pain caused by the disease. The mean DLQI score for HS was higher than for previously studied skin diseases, and correlated with disease intensity as expressed by lesions per month. This suggests that the DLQI may be a relevant outcome measure in future therapeutic trials in HS.

Question: What is the morbidity of hidradenitis suppurativa?


User Identity Linkage by Latent User Space Modelling. User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem --- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.

Question: How to link users across multiple social platforms?


Operations for Learning with Graphical Models. This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model perspective. Well-known examples of graphical models include Bayesian networks , directed graphs representing a Markov chain, and undirected networks representing a Markov eld. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, diierentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation max-imization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical speciication. This includes versions of linear regression , techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Question: What is a graphical model?


Design , Analysis and Performance of a Rotary Wing MAV. An initial design concept for a micro-coaxial rotorcraft using custom manufacturing techniques and commercial off-the-shelf components is discussed in this paper. Issues associated with the feasibility of achieving hover and fully functional flight control at small scale for a coaxial rotor configuration are addressed. Results from this initial feasibility study suggest that it is possible to develop a small scale coaxial micro rotorcraft weighing approximately 100 grams, and that available moments are appropriate for roll, yaw and lateral control. A prototype vehicle was built and its rotors were tested in a custom hover stand used to measure Thrust and power. The radio controlled vehicle was flown untethered with its own power source and exhibited good flight stability and control dynamics. The best achievable rotor performance was measured to be 42%.

Question: What is a micro-coaxial rotorcraft?


Treatment of Hyaluronic Acid Filler-Induced Impending Necrosis With Hyaluronidase: Consensus Recommendations.. Injection-induced necrosis is a rare but dreaded consequence of soft tissue augmentation with filler agents. It usually occurs as a result of injection of filler directly into an artery, but can also result from compression or injury. We provide recommendations on the use of hyaluronidase when vascular compromise is suspected. Consensus recommendations were developed by thorough discussion and debate amongst the authors at a roundtable meeting on Wednesday June 18, 2014 in Las Vegas, NV as well as significant ongoing written and verbal communications amongst the authors in the months prior to journal submission. All authors are experienced tertiary care providers. A prompt diagnosis and immediate treatment with high doses of hyaluronidase (at least 200 U) are critically important. It is not felt necessary to do a skin test in cases of impending necrosis. Some experts recommend dilution with saline to increase dispersion or lidocaine to aid vasodilation. Additional hyaluronidase should be injected if improvement is not seen within 60 minutes. A warm compress also aids vasodilation, and massage has been shown to help. Some experts advocate the use of nitroglycerin paste, although this area is controversial. Introducing an oral aspirin regimen should help prevent further clot formation due to vascular compromise. In our experience, patients who are diagnosed promptly and treated within 24 hours will usually have the best outcomes.

Question: What is the treatment of hyaluronic acid filler-induced impending necrosis?


Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams. Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.

Question: How does distributed Scrum work?


Penetration state transition analysis: A rule-based intrusion detection approach. | This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transition analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams, the graphical representation of penetrations, identify precisely the requirements for and the compromise of a penetration and present only the critical events that must occur for the successful completion of the penetration. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, called the State Transition Analysis Tool (STAT). The design and implementation of a UNIX-speci c prototype of this expert system, called USTAT, is also presented. This prototype provides a further illustration of the overall design and functionality of this intrusion detection approach. Lastly, STAT is compared to the functionality of comparable intrusion detection tools. Keywords|Security, Intrusion Detection, Expert Systems

Question: What is the difference between a rule-based expert system and a rule-based intrusion detection system?


Introduction to Modern Information Retrieval. Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.

Question: What is the new book coming recently?


Enterprise Architecture Characteristics in Context Enterprise Governance Base On COBIT 5 Framework. The existence of the enterprise architecture is an attempt of managing and planning over the evolution of information systems in the sphere of an enterprise with model-based. In developing the enterprise architecture, there are several tools definition of components in the system. This tool is known as enterprises architecture (EA) framework. In this paper, we present a method to build a model of enterprise architecture in accordance with the needs of the Organization by Understanding the characteristics of the EA framework such as Zachman, TOGAF, and FEAF. They are selected as the EA framework will be used to determine the characteristics of an EA because the framework is most widely used in corporate or Government. In COBIT 5 framework, there is a process associated with enterprise architecture it is APO03 Manage Enterprise Architecture. At this stage of the research, we describe the link between the characteristics of the EA with one process in COBIT 5 framework. The results contribute to give a recommendation how to design EA for organization based on the characteristic of EA in Context Enterprise Governance using COBIT 5 Framework.

Question: What is enterprise architecture?


Deep code comment generation. During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.

Question: How to generate deep code comments?


Random telegraph noise (RTN) in scaled RRAM devices. The random telegraph noise (RTN) related read instability in resistive random access memory (RRAM) is evaluated by employing the RTN peak-to-peak (P-p) amplitude as a figure of merit (FoM). Variation of the FoM value over multiple set/reset cycles is found to follow the log-normal distribution. P-p decreases with the reduction of the read current, which allows scaling of the RRAM operating current. The RTN effect is attributed to the mechanism of activation/deactivation of the electron traps in (in HRS) or near (in LRS) the filament that affects the current through the RRAM device.

Question: What is the mechanism of activation/deactivation of the electron traps in (in HRS) or near (in LRS) the filament that affects the current through the RRAM device?


Treatment of posterolateral tibial plateau fractures with modified Frosch approach : a cadaveric study and case series. This study aimed to investigate the surgical techniques and the clinical efficacy of a modified Frosch approach in the treatment of posterolateral tibial plateau fractures. The standard Frosch approach was performed on 5 fresh-frozen cadavers. The mean bony surface area was measured upon adequate exposure of the proximal tibial cortex. Lateral proximal tibial plate and posterolateral T-plates were placed and the ease of the procedure was noted. In the study, 12 clinical cases of posterolateral tibial plateau fractures were treated via modified or standard Frosch approaches. The outcome was assessed over short to medium follow-up period. Cadaver studies allowed the inspection of the posterolateral joint surface of all specimens from the lateral side. The mean bony surface areas of the exposed lateral and posterolateral tibial plateau were (6.78 ± 1.13) cm2 and (3.59 ± 0.65) cm2, respectively. Lateral and posterolateral plates were implanted successfully. Lateral proximal tibial plates were fixed in 10 patients via a modified Frosch approach while posterolateral plates were fixed in 2 patients via a standard Frosch approach. Patients were followed up for 10 to 24 months (average: 15.7 months) and no complications were observed during this period. Based on the Rasmussen knee function score system, the results were recorded as excellent, good, and fair in 6, 4, and 2 patients, respectively. In conclusion, the modified Frosch approach has offers advantages of clear clarity in exposure, convenient for reduction and internal fixation of a fracture, and good positive clinical results over the normal approach.

Question: What is the modified Frosch approach?


Privacy in electronic commerce and the economics of immediate gratification. Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained. We apply lessons from the research on behavioral economics to understand the individual decision making process with respect to privacy in electronic commerce. We show that it is unrealistic to expectindividual rationality in this context. Models of self-control problems and immediate gratification offer more realistic descriptions of the decision process and are more consistent with currently available data. In particular, we show why individuals who may genuinely want to protect their privacy might not do so because of psychological distortions well documented in the behavioral literature; we show that these distortions may affect not only 'naive' individuals but also 'sophisticated' ones; and we prove that this may occur also when individuals perceive the risks from not protecting their privacy as significant.

Question: What is the difference between privacy attitudes and privacy behavior?


Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting. Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever-increasing data sources, such as ubiquitous smart devices and sensors, while mimicking various approaches previously known in software, hardware, and bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government.

Question: What is the future of intelligent buildings?


Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the accelerat

Question: What is the difference between the particle swarm optimization and the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients?


SamzaSQL: Scalable Fast Data Management with Streaming SQL. As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often low-level, requiring substantial custom code that adds to the programmer learning curve and maintenance overhead. Additionally, these systems often lack SQL querying capabilities that have proven popular on Big Data systems like Hive, Impala or Presto. We define a minimal set of extensions to standard SQL for data stream querying and manipulation. These extensions are prototyped in SamzaSQL, a new tool for streaming SQL that compiles streaming SQL into physical plans that are executed on Samza, an open-source distributed stream processing framework. We compare the performance of streaming SQL queries against native Samza applications and discuss usability improvements. SamzaSQL is a part of the open source Apache Samza project and will be available for general use.

Question: What is SamzaSQL?