Maestro: A System for Scalable OpenFlow Control. The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization

Question: What is the name of the paper?


Tool for 3D Gazebo Map Construction from Arbitrary Images and Laser Scans. Algorithms for mobile robots, such as navigation, mapping, and SLAM, require proper modelling of environment. Our paper presents an automatic tool that allows creating a realistic 3D-landscape in Gazebo simulation, which is based on the real sensor-based experimental results. The tool provides an occupancy grid map automatic filtering and import to Gazebo framework as a heightmap and enables to configure the settings for created simulation environment. In addition, the tool is capable to create a 3D Gazebo map from any arbitrary image.

Question: How to create a 3D map from an image?


Implications of modified waterfall model to the roles and education of health IT professionals. Electronic Health Records (EHRs) are believed to have the potential to enhance efficiency and provide better health care. However, the benefits could be easily compromised if EHRs are not used appropriately. This paper applies a modified waterfall life cycle model to evaluate the roles of health IT professionals in the adoption and management of EHRs. We then present our development of a Masters' program in Medical Informatics for the education of health IT professionals. We conclude that health IT professionals serve key roles in addressing the problems and concerns and help fulfill envision of EHRs.

Question: What is the modified waterfall model?


3D reconstruction of freely moving persons for re-identification with a depth sensor. In this work, we describe a novel method for creating 3D models of persons freely moving in front of a consumer depth sensor and we show how they can be used for long-term person re-identification. For overcoming the problem of the different poses a person can assume, we exploit the information provided by skeletal tracking algorithms for warping every point cloud frame to a standard pose in real time. Then, the warped point clouds are merged together to compose the model. Re-identification is performed by matching body shapes in terms of whole point clouds warped to a standard pose with the described method. We compare this technique with a classification method based on a descriptor of skeleton features and with a mixed approach which exploits both skeleton and shape features. We report experiments on two datasets we acquired for RGB-D re-identification which use different skeletal tracking algorithms and which are made publicly available to foster research in this new research branch.

Question: How to create 3D models of persons freely moving in front of a consumer depth sensor?


Simple broadband Gysel combiner with a single coupled line. A coupled-Gysel broadband combiner/divider is proposed and demonstrated. The new concept relies on using a single coupled line segment in the design. Significant improvement in bandwidth are realized while maintaining low-loss, ease of design, and flexibility. The coupled-Gysel is demonstrated with a 2.5 - 8 GHz (105% fractional bandwidth) divider with 0.1 dB divider loss, and a 3.4 - 10.2 GHz (100% fractional bandwidth) with 0.2 dB divider loss.

Question: What is a coupled-Gysel broadband combiner/divider?


An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds. We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.

Question: What is the cloud?


Deep neural networks with Elastic Rectified Linear Units for object recognition. Rectified Linear Unit (ReLU) is crucial to the recent success of deep neural networks (DNNs). In this paper, we propose a novel Elastic Rectified Linear Unit (EReLU) that focuses on processing the positive part of input. Unlike previous variants of ReLU that typically adopt linear or piecewise linear functions to represent the positive part, EReLU is characterized by that each positive value scales within a moderate range like a spring during training stage. On test time, EReLU becomes standard ReLU. EReLU improves model fitting with no extra parameters and little overfitting risk. Furthermore, we propose Elastic Parametric Rectified Linear Unit (EPReLU) by taking advantage of EReLU and parametric ReLU (PReLU). EPReLU ∗Corresponding author Email addresses: jiangxiaoheng@tju.edu.cn (Xiaoheng Jiang), pyw@tju.edu.cn (Yanwei Pang ), xuelong_li@opt.ac.cn (Xuelong Li), jingpan23@gmail.com (Jing Pan), xieyinghong@163.com (Yinghong Xie) Preprint submitted to Neurocomputing September 3, 2017

Question: What is the difference between deep neural networks and deep learning?


Recognition of Emotional Face Expressions and Amygdala Pathology *. The amygdala is often damaged in patients with temporal lobe epilepsy, either because of the primary epileptogenic disease (e.g. sclerosis or encephalitis) or because of secondary effects of surgical interventions (e.g. lobectomy). In humans, the amygdala has been associated with a range of important emotional and social functions, in particular with deficits in emotion recognition from faces. Here we review data from recent neuropsychological research illustrating the amygdala role in processing facial expressions. We describe behavioural findings subsequent to focal lesions and possible factors that may influence the nature and severity of deficits in patients. Both bilateral and unilateral amygdala damage can impair the recog nition of facial emotions, especially fear, but such deficits are not always present and not always specific. Moreover, not all components of emotion or fear processing are impaired after amygdala damage. Dissociations have been reported between deficits in the recognition of emotions from the face of other people, and intact ability in the production of emotions in one's own facial expression [...] CRISTINZIO PERRIN, Chiara, SANDER, David, VUILLEUMIER, Patrik. Recognition of emotional face expressions and amygdala pathology. Epileptologie, 2007, vol. 3, p. 130-138

Question: What is the amygdala?


Microcontroller Based Smart Helmet Using GSM & GPRS. The thought of developing this proposed model comes from social responsibility towards the society. Lack of immediate First Aid and Emergency medical services during accident are prime cause of death in majority of cases of accidents. The one of the main reasons for this may be late arrival of ambulance, no person at place of accident to give information to the ambulance. This thought of taking responsibility of society resulted in our proposed model "Microcontroller based smart helmet using GSM & GPRS". The aim of this proposed model design is to inform the responsible persons at the earliest about the accident so that they can take required actions to save the life of the injured person. Our proposed system consists of Arduino as Microcontroller, GSM for calling purpose, GPRS for tracking purpose and mainly Sensors to detect the accident. Our proposed system detects the accident and sends text message along with a voice message within a minute to the registered number.

Question: What is the difference between a microcontroller and a microprocessor?


Risk Aversion and Expected-Utility Theory : A Calibration Theorem. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: What is the difference between expected utility theory and risk aversion?


A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity. The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves. Disparate streams of research have approached nonstationarity from several angles, which make a variety of implicit assumptions that make it hard to keep an overview of the state of the art and to validate the innovation and significance of new works. This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits. Further, we reflect on the principle approaches how algorithms model and cope with this non-stationarity, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind. A wide range of state-of-the-art algorithms is classified into a taxonomy, using these categories and key characteristics of the environment (e.g., observability) and adaptation behaviour of the opponents (e.g., smooth, abrupt). To clarify even further we present illustrative variations of one domain, contrasting the strengths and limitations of each category. Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.

Question: What is the difference between a multi-agent learning algorithm and a reinforcement learning algorithm?


A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition. This paper proposes an effective segmentation-free approach using a hybrid neural network hidden Markov model (NN-HMM) for offline handwritten Chinese text recognition (HCTR). In the general Bayesian framework, the handwritten Chinese text line is sequentially modeled by HMMs with each representing one character class, while the NN-based classifier is adopted to calculate the posterior probability of all HMM states. The key issues in feature extraction, character modeling, and language modeling are comprehensively investigated to show the effectiveness of NN-HMM framework for offline HCTR. First, a conventional deep neural network (DNN) architecture is studied with a well-designed feature extractor. As for the training procedure, the label refinement using forced alignment and the sequence training can yield significant gains on top of the frame-level cross-entropy criterion. Second, a deep convolutional neural network (DCNN) with automatically learned discriminative features demonstrates its superiority to DNN in the HMM framework. Moreover, to solve the challenging problem of distinguishing quite confusing classes due to the large vocabulary of Chinese characters, NN-based classifier should output 19900 HMM states as the classification units via a high-resolution modeling within each character. On the ICDAR 2013 competition task of CASIA-HWDB database, DNN-HMM yields a promising character error rate (CER) of 5.24% by making a good trade-off between the computational complexity and recognition accuracy. To the best of our knowledge, DCNN-HMM can achieve a best published CER of 3.53%.

Question: What is the best published CER on the ICDAR 2013 competition task of CASIA-HWDB database?


Biped gait controller for large speed variations, combining reflexes and a central pattern generator in a neuromuscular model. Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.

Question: What is a neuromuscular controller?


A short introduction to probabilistic soft logic. Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.

Question: What is a probabilistic soft logic?


Automated segmentation of cell nuclei in PAP smear images. In this paper an automated method for cell nucleus segmentation in PAP smear images is presented. The method combines the global knowledge about the cells and nuclei appearance and the local characteristics of the area of the nuclei, in order to achieve an accurate nucleus boundary. Filters and morphological operators in all three channels of a color image result in the determination of the locations of nuclei in the image, even in cases where cell overlapping occurs. The nucleus boundary is determined by a deformable model. The results are very promising, even when images with high degree of overlapping are used.

Question: How to segment cell nuclei in PAP smear images?


On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey. The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-toclustered-users-association, APs coordination techniques, nonorthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.

Question: What is the difference between a radio frequency network and a visible light network?


EchoFlex: Hand Gesture Recognition using Ultrasound Imaging. Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints.

Question: How to detect hand gestures using ultrasound imaging?


Electromagnetic energy harvesting from train induced railway track vibrations. Anelectromagnetic energy harvester is designed to harness the vibrational power from railroad track deflections due to passing trains. Whereas typical existing vibration energy harvester technologies are built for low power applications of milliwatts range, the proposed harvester will be designed for higher power applications for major track-side equipment such as warning signals, switches, and health monitoring sensors, which typically require a power supply of 10 Watts or more. To achieve this goal, we implement a new patent pending motion conversion mechanism which converts irregular pulse-like bidirectional linear vibration into regulated unidirectional rotational motion. Features of the motion mechanism include bidirectional to unidirectional conversion and flywheel speed regulation, with advantages of improved reliability, efficiency, and quality of output power. It also allows production of DC power directly from bidirectional vibration without electronic diodes. Preliminary harvester prototype testing results illustrate the features and benefits of the proposed motion mechanism, showing reduction of continual system loading, regulation of generator speed, and capability for continuous DC power generation.

Question: How does the proposed harvester work?


Parsing Algebraic Word Problems into Equations. This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.

Question: How does Alges compare to previous work?


MazeBase: A Sandbox for Learning from Games. This paper introduces an environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games based on algorithmic tasks (e.g. embodying simple if-then statements). We deploy a range of neural models (fully connected, convolutional network, memory network) on these games, with and without a procedurally generated curriculum. We show that these architectures can be trained with reinforcement to respectable performance on these tasks, but are still far from optimal, despite their simplicity. We also apply these models to games involving combat, including StarCraft, demonstrating their ability to learn non-trivial tactics which enable them to consistently beat the in-game AI.

Question: What is a sandbox for learning from games?


Oxytocin is associated with human trustworthiness. Human beings exhibit substantial interpersonal trust-even with strangers. The neuroactive hormone oxytocin facilitates social recognition in animals, and we examine if oxytocin is related to trustworthiness between humans. This paper reports the results of an experiment to test this hypothesis, where trust and trustworthiness are measured using the sequential anonymous "trust game" with monetary payoffs. We find that oxytocin levels are higher in subjects who receive a monetary transfer that reflects an intention of trust relative to an unintentional monetary transfer of the same amount. In addition, higher oxytocin levels are associated with trustworthy behavior (the reciprocation of trust). Absent intentionality, both the oxytocin and behavioral responses are extinguished. We conclude that perceptions of intentions of trust affect levels of circulating oxytocin.

Question: What is oxytocin?


Mining advertiser-specific user behavior using adfactors. Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users' behavior that leads to impressions of campaign ads, as well as users' responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions.  The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, the advertisers often seek to understand more sophisticated long-term effects of their ads on the brand awareness and the user behavior that leads to the conversion, thus creating a need for the reporting measures that can capture both the duration and the frequency of the pathways to user conversions.  In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called the adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called the adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and the user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using the MapReduce programming model and the Pregel framework.  Using an anonymous user-level dataset of sponsored search campaigns for eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining the long-term behavioral patterns in the advertising data.

Question: What is the difference between the click-through rate and the conversion rate?


Multi-task learning of dish detection and calorie estimation. In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.

Question: What is the difference between a sequential model and a multi-task model?


Personality Traits and Music Genres: What Do People Prefer to Listen To?. Personality-based personalized systems are increasingly gaining interest as personality traits has been shown to be a stable construct within humans. In order to provide a personality-based experience to the user, users' behavior, preferences, and needs in relation to their personality need to be investigated. Although for a technological mediated environment the search for these relationships is often new territory, there are findings from personality research of the real world that can be used in personalized systems. However, for these findings to be implementable, we need to investigate whether they hold in a technologically mediated environment. In this study we assess prior work on personality-based music genre preferences from traditional personality research. We analyzed a dataset consisting of music listening histories and personality scores of 1415 Last.fm users. Our results show agreements with prior work, but also important differences that can help to inform personalized systems.

Question: What is the personality of a person who listens to classical music?


An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model. Faculty of Economics in Universitas Atma Jaya Yogyakarta (UAJY) has replaced printed textbooks with e-textbooks for its academic activities since 2015. These e-textbooks can be accessed via iPad which is given to each student. Hence, the objective of this study is to test the proposed research model based on integrated UTAUT2 and TTF. Questionnaires were distributed to 326 students in 10 classes. Only junior and sophomore students were eligible to fulfill the questionnaires. The result of the study shows that performance expectancy, effort expectancy, social influence, facilitating condition, and habit have a direct significant relationship on behavioral intention to use mobile e-textbook. The result also shows that both Task technology and technology characteristic positively affect task technology fit. There is also a direct significant relationship between Task technology fit and performance expectancy. We suggest that further studies include experimentation to investigate performance expectancy of mobile e-book. 2) More evaluation should be held in the future in more mature environment. The finding of this study can help policymakers in the Faculty of Economics UAJY to evaluate the policy and formulate better strategy. In addition, these findings can be used by others faculties and universities as mobile e-textbook adoption case reference.

Question: What is the difference between UTAUT2 and TTF?


Coloring-based coalescing for graph coloring register allocation. Graph coloring register allocation tries to minimize the total cost of spilled live ranges of variables. Live-range splitting and coalescing are often performed before the coloring to further reduce the total cost. Coalescing of split live ranges, called sub-ranges, can decrease the total cost by lowering the interference degrees of their common interference neighbors. However, it can also increase the total cost because the coalesced sub-ranges can become uncolorable. In this paper, we propose coloring-based coalescing, which first performs trial coloring and next coalesces all copyrelated sub-ranges that were assigned the same color. The coalesced graph is then colored again with the graph coloring register allocation. The rationale is that coalescing of differently colored sub-ranges could result in spilling because there are some interference neighbors that prevent them from being assigned the same color. Experiments on Java programs show that the combination of live-range splitting and coloring-based coalescing reduces the static spill cost by more than 6% on average, comparing to the baseline coloring without splitting. In contrast, well-known iterated and optimistic coalescing algorithms, when combined with splitting, increase the cost by more than 20%. Coloring-based coalescing improves the execution time by up to 15% and 3% on average, while the existing algorithms improve by up to 12% and 1% on average.

Question: What is the cost of spilling?


Deep Context: End-to-end Contextual Speech Recognition. In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E) ASR system that utilizes such context. Our approach, which we refer to as Contextual Listen, Attend and Spell (CLAS) jointly-optimizes the ASR components along with embeddings of the context n-grams. During inference, the CLAS system can be presented with context phrases which might contain-of-vocabulary (OOV) terms not seen during training. We compare our proposed system to a more traditional contextualization approach, which performs shallow-fusion between independently trained LAS and contextual n-gram models during beam search. Across a number of tasks, we find that the proposed CLAS system outperforms the baseline method by as much as 68% relative WER, indicating the advantage of joint optimization over individually trained components.

Question: What is the difference between a shallow fusion and a joint optimization?


Loving yourself abundantly: relationship of the narcissistic personality to self- and other perceptions of workplace deviance, leadership, and task and contextual performance.. The authors report results from 2 studies assessing the extent to which narcissism is related to self- and other ratings of leadership, workplace deviance, and task and contextual performance. Study 1 results revealed that narcissism was related to enhanced self-ratings of leadership, even when controlling for the Big Five traits. Study 2 results also revealed that narcissism was related to enhanced leadership self-perceptions; indeed, whereas narcissism was significantly positively correlated with self-ratings of leadership, it was significantly negatively related to other ratings of leadership. Study 2 also revealed that narcissism was related to more favorable self-ratings of workplace deviance and contextual performance compared to other (supervisor) ratings. Finally, as hypothesized, narcissism was more strongly negatively related to contextual performance than to task performance.

Question: What is the relationship between narcissism and workplace deviance?


Question: What is the difference between delayed and immediate rewards?


Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices : Application of a Bivariate Kalman Filter. In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained using the Kalman filter and smoother to a linearization of a statespace representation of the model. The method is illustrated using the Heston (1993) stochastic volatility model applied to Australian News Corporation spot and option price data. Alternative models nested in the Heston framework are ranked via Bayes Factors and via fit, predictive and hedging performance.

Question: How to estimate the volatility of a stock?


The SLAM project: debugging system software via static analysis. The goal of the SLAM project is to check whether or not a program obeys "API usage rules" that specify what it means to be a good client of an API. The SLAM toolkit statically analyzes a C program to determine whether or not it violates given usage rules. The toolkit has two unique aspects: it does not require the programmer to annotate the source program (invariants are inferred); it minimizes noise (false error messages) through a process known as "counterexample-driven refinement". SLAM exploits and extends results from program analysis, model checking and automated deduction. We have successfully applied the SLAM toolkit to Windows XP device drivers, to both validate behavior and find defects in their usage of kernel APIs.

Question: What is the SLAM project?


outputs Semantic patterns for sentiment analysis of Twitter Conference Item. Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweetand entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Question: How does the proposed approach compare to other approaches?


Improved Algorithm for Intrusion Detection Using Genetic Algorithm and SNORT. Intrusion Detection Systems (IDSs) detects the network attacks by scanning each data packets. There are various approaches were done for making better IDS, though traditional IDS approaches are insufficient in terms of automating detection of attacks as attackers evolved. This research is focused on Genetic Algorithm approach used in Intrusion Detection. This research shows that how the traditional SNORT and Genetic Algorithm combined together so that the number of rules of SNORT is decreased. Experimental results Shows That IDS using Genetic Algorithm and SNORT can decrease the amount of detection time, CPU Utilization and Memory Utilization. Keywords—Genetic Algorithm, SNORT, Fitness Function,

Question: How to make better IDS?


Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet <xref ref-type="bibr" rid="ref1">[1]</xref> and Fast R-CNN <xref ref-type="bibr" rid="ref2">[2]</xref> have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a <italic>Region Proposal Network</italic> (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model <xref ref-type="bibr" rid="ref3">[3]</xref> , our detection system has a frame rate of 5 fps (<italic>including all steps</italic>) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

Question: What is the fastest object detection network?


A novel activation function for multilayer feed-forward neural networks. Traditional activation functions such as hyperbolic tangent and logistic sigmoid have seen frequent use historically in artificial neural networks. However, nowadays, in practice, they have fallen out of favor, undoubtedly due to the gap in performance observed in recognition and classification tasks when compared to their well-known counterparts such as rectified linear or maxout. In this paper, we introduce a simple, new type of activation function for multilayer feed-forward architectures. Unlike other approaches where new activation functions have been designed by discarding many of the mainstays of traditional activation function design, our proposed function relies on them and therefore shares most of the properties found in traditional activation functions. Nevertheless, our activation function differs from traditional activation functions on two major points: its asymptote and global extremum. Defining a function which enjoys the property of having a global maximum and minimum, turned out to be critical during our design-process since we believe it is one of the main reasons behind the gap observed in performance between traditional activation functions and their recently introduced counterparts. We evaluate the effectiveness of the proposed activation function on four commonly used datasets, namely, MNIST, CIFAR-10, CIFAR-100, and the Pang and Lee's movie review. Experimental results demonstrate that the proposed function can effectively be applied across various datasets where our accuracy, given the same network topology, is competitive with the state-of-the-art. In particular, the proposed activation function outperforms the state-of-the-art methods on the MNIST dataset.

Question: What is the difference between a novel activation function and a traditional activation function?


Scale Space Technique for Word Segmentation in Handwritten Documents. Indexing large archives of historical manuscripts, like the papers of George Washington, is required to allow rapid perusal by scholars and researchers who wish to consult the original manuscripts. Presently, such large archives are indexed manually. Since optical character recognition (OCR) works poorly with handwriting, a scheme based on matching word images called word spotting has been suggested previously for indexing such documents. The important steps in this scheme are segmentation of a document page into words and creation of lists containing instances of the same word by word image matching. We have developed a novel methodology for segmenting handwritten document images by analyzing the extent of "blobs" in a scale space representationof the image. We believe this is the first application of scale space to this problem. The algorithm has been applied to around 30 grey level images randomly picked from different sections of the George Washington corpus of 6,400 handwritten document images. An accuracy of 77 − 96 percent was observed with an average accuracy of around 87 percent. The algorithm works well in the presence of noise, shine through and other artifacts which may arise due aging and degradation of the page over a couple of centuries or through the man made processes of photocopying and scanning.

Question: How to segment handwritten documents?


3D convolutional neural networks by modal fusion. We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes. This is especially useful for enhancing volumetric ConvNets and classifying 3D scans with insufficient surface details. Experimental results on CAD and real-world scan datasets showed that our technique outperforms previous approaches.

Question: How does this work?


Anomaly detection using DBSCAN clustering technique for traffic video surveillance. Detecting anomalies such as rule violations, accidents, unusual driving and other suspicious action increase the need for automatic analysis in Traffic Video Surveillance (TVS). Most of the works in Traffic rule violation systems are based on probabilistic methods of classification for detecting the events as normal and abnormal. This paper proposes an un-supervised clustering technique namely Novel Anomaly Detection-Density Based Spatial Clustering of Applications with Noise (NAD-DBSCAN) which clusters the trajectories of moving objects of varying sizes and shapes. A trajectory is said to be abnormal if the event that never fit with the trained model. Epsilon (Eps) and Minimum Points (MinPts) are essential parameters for dynamically calculating the sum of clusters for a data point. The proposed system is validated using benchmark traffic dataset and found to perform accurately in detecting anomalies.

Question: How does anomaly detection work?


Fault classification and fault signature production for rolling element bearings in electric machines. Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies. This has lead to the common practice of categorizing bearing faults according to fault location (i.e., inner race, outer race, ball, or cage fault). While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings. This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness. These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault. Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current. Experimental results are provided from bearings failed in situ via a shaft current. These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults. This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults.

Question: What is the difference between a single-point defect and a generalized roughness fault?


Mars Sample Return campaign status. The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2

Question: What is the status of the Mars Sample Return campaign?


Mid-level deep pattern mining. Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.

Question: What is mid-level deep pattern mining?


Machine Learning for Semantic Parsing in Review. Spoken Language Understanding (SLU) and more specifically, semantic parsing is an indispensable task in each speech-enabled application. In this survey, we review the current research on SLU and semantic parsing with emphasis on machine learning techniques used for these tasks. Observing the current trends in semantic parsing, we conclude our discussion by suggesting some of the most promising future research trends.

Question: What is the current research on semantic parsing?


SATIRE: a software architecture for smart AtTIRE. Personal instrumentation and monitoring services that collect and archive the physical activities of a user have recently been introduced for various medical, personal, safety, and entertainment purposes. A general software architecture is needed to support different categories of such monitoring services. This paper presents a software architecture, implementation, and preliminary evaluation of SATIRE, a wearable personal monitoring service transparently embedded in user garments. SATIRE records the owner's activity and location for subsequent automated uploading and archiving. The personal archive can later be searched for particular events to answer questions regarding past and present user activity, location, and behavior patterns. A short feasibility and usage study of a prototype based on MicaZ motes provides a proof of concept for the SATIRE architecture.

Question: What is a proof of concept?


IT worker turnover: an empirical examination of intrinsic motivation. This study examined intrinsic motivation's influence on information technology (IT) workers' attitudes and intentions. Drawing on Human Resource management research (Eby & Freeman, 1999), we model Intrinsic motivation as mediating the influence of motivators (i.e., intrinsic job characteristics) and hygiene factors (i.e., pay and supervisory satisfaction) on workplace attitudes (i.e., job satisfaction and affective organizational commitment). In turn, workplace attitudes mediate the influence of intrinsic motivation on turnover intention. The model was tested using data collected from public sector IT workers in the Southeastern United States. Although intrinsic motivation did not fully mediate the influence of motivators and hygiene factors, findings suggest that intrinsic motivation positively influences workplace attitudes and has a mediated influence on turnover intent. Implications for research and practice are offered.

Question: What is the influence of intrinsic motivation on turnover intention?


An In Vitro Study on the Effects of Post-Core Design and Ferrule on the Fracture Resistance of Endodontically Treated Maxillary Central Incisors. BACKGROUND Endodontically treated teeth have significantly different physical and mechanical properties compared to vital teeth and are more prone to fracture. The study aims to compare the fracture resistance of endodontically treated teeth with and without post reinforcement, custom cast post-core and prefabricated post with glass ionomer core and to evaluate the ferrule effect on endodontically treated teeth restored with custom cast post-core.   MATERIALS AND METHODS A total of 40 human maxillary central incisors with similar dimensions devoid of any root caries, restorations, previous endodontic treatment or cracks were selected from a collection of stored extracted teeth. An initial silicone index of each tooth was made. They were treated endodontically and divided into four groups of ten specimens each. Their apical seal was maintained with 4 mm of gutta-percha. Root canal preparation was done and then post core fabrication was done. The prepared specimens were subjected to load testing using a computer coordinated UTM. The fracture load results were then statistically analyzed. One-way ANOVA was followed by paired t-test.   RESULTS 1. Reinforcement of endodontically treated maxillary central incisors with post and core, improved their fracture resistance to be at par with that of endodontically treated maxillary central incisor, with natural crown. 2. The fracture resistance of endodontically treated maxillary central incisors is significantly increased when restored with custom cast post-core and 2 mm ferrule.   CONCLUSION With 2 mm ferrule, teeth restored with custom cast post-core had a significantly higher fracture resistance than teeth restored with custom cast post-core or prefabricated post and glass ionomer core without ferrule.

Question: What is the difference between a prefabricated post and a custom cast post?


The peculiarities of knowledge management processes in SMEs: the case of Singapore. Purpose – The objectives of this study are two-fold. The first is to examine the peculiarities of KM processes that are unique in SMEs from three perspectives, namely knowledge creation, knowledge sharing and knowledge reuse. Secondly, to identify enablers and impediments of these KM processes that influence the competitiveness of SMEs. Design/methodology/approach – The study adopted a case study approach involving 21 participants comprising management staff and front-line employees from four Singaporean SMEs. Findings – The SME owner, rather than the employees, was found to be the key source and creator of knowledge and the sole driver of the KM processes. In SMEs, knowledge creation takes the form of innovative customized solutions to meet customers' needs; knowledge sharing occurs through cross functionality, overlapping roles, and facilitated by close physical proximity in open workspaces; and knowledge reuse is often made tacitly, where common knowledge is prevalently embedded within the KM processes of SMEs. The enablers of knowledge creation process rested largely on the owner's innovativeness, creativity and ability to acquire knowledge of the industry. Knowledge sharing processes are enabled by the awareness of roles, mutual respect and the level of trust among employees within the SME while knowledge reuse is fostered by close proximity of employees and the willingness and openness of the owner to impart his knowledge. The lack of the above enablement factors mentioned will hinder these KM processes. Research limitations/implications – The study is limited by the fact that data was collected from four SMEs in Singapore. Furthermore, only a small sample of staff from these SMEs was interviewed. Hence the findings need to be interpreted in light of such a scope. Practical implications – For SMEs, this research provides perspectives on the factors influencing KM processes, in

Question: What is the peculiarities of knowledge management processes in SMEs?


Depression and Psychosocial Risk Factors among Community-Dwelling Older Adults in Singapore.. Depression is the most common mental and emotional disorder that emerges in the late stages of life. It is closely associated with poor health, disability, mortality, and suicide. The study examines the risk factors of depression in late life, especially the psychosocial factors, among a sample comprising 162 community-dwelling Singaporean adults aged 65 years and above. An interview-based structured survey was conducted in multiple senior activity centers located in different parts of Singapore. Results from the hierarchical regression analysis show that 32.9% of the variance in geriatric depression can be explained by the three psychosocial factors, among which loneliness, perceived social support, and the emotional regulation component of resilience are significantly associated with depression in older adults. Large-scale studies should be conducted to confirm the findings of the present study, and to further examine the predictive effects of these psychosocial factors on depression among older adults.

Question: What are the risk factors of depression in older adults?


DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

Question: What is the difference between REST and DPARSF?


Learning Structured Representation for Text Classification via Reinforcement Learning. Representation learning is a fundamental problem in natural language processing. This paper studies how to learn a structured representation for text classification. Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures automatically. We demonstrate two attempts to build structured representation: Information Distilled LSTM (ID-LSTM) and Hierarchically Structured LSTM (HS-LSTM). ID-LSTM selects only important, taskrelevant words, and HS-LSTM discovers phrase structures in a sentence. Structure discovery in the two representation models is formulated as a sequential decision problem: current decision of structure discovery affects following decisions, which can be addressed by policy gradient RL. Results show that our method can learn task-friendly representations by identifying important words or task-relevant structures without explicit structure annotations, and thus yields competitive performance.

Question: What is the difference between the two methods?


Design of a new counter-balancing stackable mechanism. This paper describes design and application of a novel counter-balancing stackable parallel mechanism. The stackable mechanism is designed by combining a five-bar and a couple of parallelograms. This design allows a compact design and wide workspace as compared to prior parallel type counter-balancing mechanisms. A linear geometry of this mechanism guarantees static balancing in the whole workspace. This mechanism was successfully applied to otologic.

Question: What is a counter-balancing mechanism?


A Survey of Procedural Noise Functions. Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.

Question: What is a procedural noise function?


Scenario-Based Performance Analysis of Routing Protocols for Mobile ad-hoc Networks. This study is a comparison of three routing protocols proposed for wireless mobile ad-hoc networks. The protocols are: Destination Sequenced Distance Vector (DSDV), Ad-hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Extensive simulations are made on a scenario where nodes moves randomly. Results are presented as a function of a novel mobility metric designed to reflect the relative speeds of the nodes in a scenario. Furthermore, three realistic scenarios are introduced to test the protocols in more specialized contexts. In most simulations the reactive protocols (AODV and DSR) performed significantly better than DSDV. At moderate traffic load DSR performed better than AODV for all tested mobility values, while AODV performed better than DSR at higher traffic loads. The latter is caused by the source routes in DSR data packets, which increase the load on the network. routers and hosts, thus a node may forward packets between other nodes as well as run user applications. Mobile ad-hoc networks have been the focus of many recent research and development efforts. Ad-hoc packet radio networks have so far mainly concerned military applications, where a decentralized network configuration is an operative advantage or even a necessity. Networks using ad-hoc configuration concepts can be used in many military applications, ranging from interconnected wireless access points to networks of wireless devices carried by individuals, e.g., digital maps, sensors attached to the body, voice communication, etc. Combinations of wide range and short range ad-hoc networks seek to provide robust, global coverage, even during adverse operating conditions.

Question: What is the difference between DSR and AODV?


Keyphrase Extraction Using Knowledge Graphs. Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

Question: What is the difference between the co-occurrence-based methods and the statistic-based methods?


Active current balancing for parallel-connected silicon carbide MOSFETs. In high power applications of silicon carbide (SiC) MOSFETs where parallelism is employed, current unbalance can occur and affect the performance and reliability of the power devices. In this paper, factors which cause current unbalance in these devices are analyzed. Among them, the threshold voltage mismatch is identified as a major factor for dynamic current unbalance. The threshold distribution of SiC MOSFETs is investigated, and its effect on current balance is studied in experiments. Based on these analyses, an active current balancing scheme is proposed. It is able to sense the unbalanced current and eliminate it by actively controlling the gate drive signal to each device. The features of fine time resolution and low complexity make this scheme attractive to a wide variety of wide-band-gap device applications. Experimental and simulation results verify the feasibility and effectiveness of the proposed scheme.

Question: What is the threshold voltage mismatch?


Camera parameters auto-adjusting technique for robust robot vision. How to make vision system work robustly under dynamic light conditions is still a challenging research focus in computer/robot vision community. In this paper, a novel camera parameters auto-adjusting technique based on image entropy is proposed. Firstly image entropy is defined and its relationship with camera parameters is verified by experiments. Then how to optimize the camera parameters based on image entropy is proposed to make robot vision adaptive to the different light conditions. The algorithm is tested by using the omnidirectional vision in indoor RoboCup Middle Size League environment and the perspective camera in outdoor ordinary environment, and the results show that the method is effective and color constancy to some extent can be achieved.

Question: How to make vision system work robustly under dynamic light conditions is still a challenging research focus in computer/robot vision community.


Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge. Destination image significantly influences a tourist's decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives—from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations extracts tourism knowledge automatically, and represents this knowledge as ontological structures.

Question: How to analyze news media coverage to acquire and structure tourism knowledge?


Color reduction based on ant colony. In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make the final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method. 2007 Elsevier B.V. All rights reserved.

Question: How to reduce color based on ant colony algorithm?


Parent and family impact of autism spectrum disorders: a review and proposed model for intervention evaluation.. Raising a child with an autism spectrum disorder (ASD) can be an overwhelming experience for parents and families. The pervasive and severe deficits often present in children with ASD are associated with a plethora of difficulties in caregivers, including decreased parenting efficacy, increased parenting stress, and an increase in mental and physical health problems compared with parents of both typically developing children and children with other developmental disorders. In addition to significant financial strain and time pressures, high rates of divorce and lower overall family well-being highlight the burden that having a child with an ASD can place on families. These parent and family effects reciprocally and negatively impact the diagnosed child and can even serve to diminish the positive effects of intervention. However, most interventions for ASD are evaluated only in terms of child outcomes, ignoring parent and family factors that may have an influence on both the immediate and long-term effects of therapy. It cannot be assumed that even significant improvements in the diagnosed child will ameliorate the parent and family distress already present, especially as the time and expense of intervention can add further family disruption. Thus, a new model of intervention evaluation is proposed, which incorporates these factors and better captures the transactional nature of these relationships.

Question: What is the impact of autism on parents and families?


The research and implementation of metadata cache backup technology based on CEPH file system. Based on research and analysis of the Ceph file system, the log cache backup scheme is proposed. The log cache backup scheme reduces metadata access delays by caching logs and reducing the time of storing logs to server clusters. In order to prevent the loss of cached data in metadata servers, a cached data backup scheme is proposed. Compared to the metadata management subsystem of the Ceph, the log cache backup scheme can effectively improve the access performance of metadata. Finally, based on open source codes of the Ceph file system, the log cache backup scheme is implemented. Compared to the performance of the Ceph metadata management subsystem, experiment results show that performance improvements of the log cache backup scheme are up to 11.5%.

Question: What is metadata cache backup technology based on CEPH file system?


The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis. In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from fine-grained user profiling to device security and network optimization). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: 1) the goal of the analysis; 2) the point where the network traffic is captured; and 3) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi access points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges, and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.

Question: What is the dark side of mobile devices?


f3.js: A Parametric Design Tool for Physical Computing Devices for Both Interaction Designers and End-users. Although the exploration of design alternatives is crucial for interaction designers and customization is required for end-users, the current development tools for physical computing devices have focused on single versions of an artifact. We propose the parametric design of devices including their enclosure layouts and programs to address this issue. A Web-based design tool called f3.js is presented as an example implementation, which allows devices assembled from laser-cut panels with sensors and actuator modules to be parametrically created and customized. It enables interaction designers to write code with dedicated APIs, declare parameters, and interactively tune them to produce the enclosure layouts and programs. It also provides a separate user interface for end-users that allows parameter tuning and dynamically generates instructions for device assembly. The parametric design approach and the tool were evaluated through two user studies with interaction designers, university students, and end-users.

Question: What is the parametric design of devices?


A case study of a five-year-old child with pervasive developmental disorder-not otherwise specified using sound-based interventions.. The aim of this study was to determine the efficacy of The Listening Program (TLP) in treating a child with pervasive developmental disorder-not otherwise specified (PDD-NOS). Using a single-subject case study design, one child with PDD-NOS was administered a 20-week TLP intervention focused on improving sensory processing and language function. Data collection included pre- and post-evaluations using video footage, and Sensory Profile and Listening Checklist questionnaires. Results of the study indicated improved behaviour and sensory tolerance in the post-intervention video footage, including active participation in singing and movements to song. Sensory Profile and Listening Checklist questionnaires indicated significant improvements in sensory processing, receptive/expressive listening and language, motor skills, and behavioural/social adjustment at the post-intervention assessment. Although small in scope, this study highlights the need for continued research by occupational therapists into sound-based interventions. Particularly, occupational therapists need to perform larger-scale studies utilizing TLP to verify the efficacy of this alternative treatment method.

Question: What is the aim of this study?


Quantitative trait loci for glucosinolate accumulation in Brassica rapa leaves.. Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F(1)s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. Both comparative genomic analyses based on Arabidopsis-Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.

Question: What is the difference between glucosinolates and glucosinolates?


Solid Modeling: A Historical Summary and Contemporary Assessment. A new generation of industrial geometry systems is emerging based on the technology of the 1970's. The generations of the eighties and nineties will require more research.

Question: What is the technology of the 1970's?


An ubiquitous miniaturized android based ECG monitoring system. Miniaturization of devices has brought a lot of improvements in the healthcare delivery as a result of advancenments in Integrated circuit technologies. As a result, proper and effective way of analyzing various body conditions and diseases can be achieved. When the heart muscles repolarize and depolarize, it generates electrical impulses which could help the doctor or the health professional to diagnose various heart abnormalities. The aim of this project is to design and develop a miniaturized ECG system displayed on both PC and Tablet for preliminary diagnoses of the heart. This system utilizes a programmable single-chip microcontroller for analysing the biosignals to indicate the condition of the heart. The system is incomporated with alarm systems which prompt the doctor if there is any abnormality from a patient's heart. These abnormalities could be bradycardia or tachycardia. This system is built with a provision of enabling wireless transmission of ECG signals to a PC or TABLET through BLUETOOTH and ANDROID PLATFORM. The android application can be installed and tested on any smart phone or tablet and screen resolution can be adjusted later as per requirement. This helps the doctor to have visual description of the patient ECG without the need of mounted monitors. The experimental results show that device is clinically useful, compact, cost effective and user friendly.

Question: What is a microcontroller?


Kinematics and workspace analysis for a novel 6-PSS parallel manipulator. In this paper, a novel 6-PSS parallel manipulator is designed and the three dimensional structure model is obtained with the Solidworks. The inverse kinematics analysis is performed based on the designed geometric parameters. And according to this analysis, we obtain the theoretical simulation results of virtual structures with different positions and orientations by using MATLAB software. These simulation structures verify the feasibility of this novel 6-PSS parallel manipulator. With a given orientation, a numerical search method is adopted in finding the reachable workspace with the judgment conditions of physical constraints, and the relationship between the reachable workspace and the size of the structure is studied in virtual simulation. Therefore, the range including the largest reachable workspace is achieved and a possible further application of this novel 6-PSS parallel manipulator is proposed, especially in some fields of different structure size requirements.

Question: What is the relationship between the reachable workspace and the size of the structure?


A BIOLOGICALLY INSPIRED VISUAL WORKING MEMORY. The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our HebbRosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the 'black-box' of deep learning.

Question: What is a biologically inspired visual working memory?


The 500MHz band low power rectenna for DTV in the Tokyo area. In this paper, the 500MHz band low power rectenna with high sensitivity characteristic is described. The rectenna is consisting of the folded dipole antenna with a 1.6kΩ impedance and the Cockcroft-Walton type rectifier for improvement of the rectification efficiency and the output dc voltage. Furthermore an L-Type LPF is added to reduce the negative influence due to the junction capacitance of the SBD. The developed rectennas (stage number: 1, 2) achieve the rectification efficiency of 48.9 % (stage number: 1) and 43.1 % (stage number: 2) at -15dBm of the input power. In addition to above, the top level efficiency of 8.7 % (stage number:1) at the input power of -30dBm can be achieved. In the Tokyo are, the output dc voltage of 0.22 V can be measured at the 25 km point from the antenna tower.

Question: What is the 500MHz band low power rectenna for DTV in the Tokyo area?


Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and diabetes mellitus is the ninth major cause of death. About 1 in 11 adults worldwide now have diabetes mellitus, 90% of whom have type 2 diabetes mellitus (T2DM). Asia is a major area of the rapidly emerging T2DM global epidemic, with China and India the top two epicentres. Although genetic predisposition partly determines individual susceptibility to T2DM, an unhealthy diet and a sedentary lifestyle are important drivers of the current global epidemic; early developmental factors (such as intrauterine exposures) also have a role in susceptibility to T2DM later in life. Many cases of T2DM could be prevented with lifestyle changes, including maintaining a healthy body weight, consuming a healthy diet, staying physically active, not smoking and drinking alcohol in moderation. Most patients with T2DM have at least one complication, and cardiovascular complications are the leading cause of morbidity and mortality in these patients. This Review provides an updated view of the global epidemiology of T2DM, as well as dietary, lifestyle and other risk factors for T2DM and its complications.

Question: What is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus?


Frostbite following cryolipolysis treatment in a beauty salon: a case study.. This case study describes frostbite, a previously unreported complication following cryolipolysis, which resulted in substantial necrosis of the flank. Medical attention was not sought until one week after treatment. On examination, two distinct areas of significant frostbite in the left flank with surrounding erythema were revealed. Surgical intervention was avoided, as is recommended in cases of frostbite, and conservative treatment resulted in recovery of the affected area. Here, the authors highlight the adverse effects related to cryolipolysis, analysing the pathogenesis, clinical manifestations and management of this injury. The necessity of regulation within the cosmetic sector and the challenges associated with its implementation are also described. The authors believe emphasis must be placed on increasing patient awareness on the potential hazards of seeking cosmetic treatment from unregulated providers.

Question: What is cryolipolysis?


Maximum margin planning. Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.

Question: What is maximum margin planning?


Coupled hidden Markov models for complex action recognition. We present algorithms for coupling and training hidden Markov models ( HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal—that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.

Question: What is a coupled HMM?


Spatial as Deep: Spatial CNN for Traffic Scene Understanding. Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.

Question: What is the spatial relationship?


Virtual Dermatologist: An application of 3D modeling to tele-healthcare. In this paper, we present preliminary results towards the development of the Virtual Dermatologist: A 3D image and tactile database for virtual examination of dermatology patients. This system, which can be installed and operated by non-dermatologists in remotes areas where access to a dermatologist is difficult, will enhance and broaden the application of tele-healthcare, and it will greatly facilitate the surveillance and consequent diagnosis of various skin diseases. Unlike other systems that monitor the progress of skin diseases using qualitative data on simple baseline (2D) photography, the proposed system will also allow for the quantitative assessment of the progress of the disease over time (e.g. thickness, size, roughness, etc). In fact, the 3D model created by the proposed system will let the dermatologist perform dermatoscopic-like examinations over specially annotated areas of the 3D model of the patient's body (i.e. higher definition areas of the 3D model). As part of its future development, the system will also allow the dermatologist to virtually touch and feel the lesion through a haptic interface. In its current form, the system can detect skin lesions smaller than 1mm, as we demonstrate in the result section.

Question: What is the difference between a dermatologist and a virtual dermatologist?


Zika virus impairs growth in human neurospheres and brain organoids. Since the emergence of Zika virus (ZIKV), reports of microcephaly have increased considerably in Brazil; however, causality between the viral epidemic and malformations in fetal brains needs further confirmation. We examined the effects of ZIKV infection in human neural stem cells growing as neurospheres and brain organoids. Using immunocytochemistry and electron microscopy, we showed that ZIKV targets human brain cells, reducing their viability and growth as neurospheres and brain organoids. These results suggest that ZIKV abrogates neurogenesis during human brain development.

Question: What is the effect of the zika virus on the brain?


A 25-kHz 3rd-order continuous-time Delta-Sigma modulator using tri-level quantizer. This paper presents a 3rd order continuous-time Delta-Sigma modulator with a tri-level quantizer, which provides 3-dB reduction of quantization noise without dynamic element matching (DEM). The tri-level DAC linearity is analyzed and it shows that a highly linear tri-level DAC can be realized in fully-differential active-RC Delta-Sigma modulator. The performance of the tri-level continuous-time Delta-Sigma modulator has been verified through simulations using a standard 0.18-μm CMOS process. It achieves 81-dB SNDR at 3.2-MS/s sampling rate and consumes 1.14-μW power with ideal amplifier.

Question: What is a tri-level quantizer?


A Chebyshev-Response Step-Impedance Phase-Inverter Rat-Race Coupler Directly on Lossy Silicon Substrate and Its Gilbert Mixer Application. This paper focuses on the analysis and the design methodology of the step-impedance phase-inverter rat-race coupler on a silicon-based process. The issues of impedance limitation and bandwidth are discussed in detail. Our proposed concept utilizes a high silicon dielectric constant, phase-inverter structure, step-impedance technique, and Chebyshev response to make the rat-race coupler more compact (~ 64% reduction) and highly balanced over a wide operating bandwidth. Moreover, the inter-digital coplanar stripline used in the step-impedance section effectively reduces the characteristic impedance of the transmission line for large size shrinkage and insertion-loss reduction. The demonstrated step-impedance rat-race coupler directly on silicon substrate has 6- ~ 7-dB insertion loss from 5 to 15 GHz and small variations in amplitude/phase balance. Compared with our previous work, the proposed rat-race coupler achieves a 3-dB improvement in the insertion loss. Thus, a 0.13-μm CMOS Gilbert down-converter with a miniature phase-inverter rat-race coupler at the RF path for wideband single-to-differential signal conversion achieves a noise figure of 16 dB.

Question: What is the noise figure of the proposed rat-race coupler?


Demanding Customers : Consumerist Patients and Quality of Care. Consumerism arises when patients acquire and use medical information from sources other than their physicians. This practice has been hailed as a means of improving quality. This need not be the result. Our theoretical model identifies a channel through which consumerism may reduce quality: consumerist patients place additional demands on their doctors' time, thus imposing a negative externality on other patients. Relative to a world in which consumerism does not exist, consumerism may harm other consumerists, non-consumerists, or both. Data from a large national survey of physicians confirm the negative effects of consumerism: high levels of consumerist patients are associated with lower perceived quality among physicians.

Question: What is the negative externality of consumerism?


Adversarial Examples for Generative Models. We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

Question: Adversarial Examples for Generative Models


Why are some people happier than others? The role of cognitive and motivational processes in well-being.. Addressing the question of why some people are happier than others is important for both theoretical and practical reasons and should be a central goal of a comprehensive positive psychology. Following a construal theory of happiness, the author proposes that multiple cognitive and motivational processes moderate the impact of the objective environment on well-being. Thus, to understand why some people are happier than others, one must understand the cognitive and motivational processes that serve to maintain, and even enhance, enduring happiness and transient mood. The author's approach has been to explore hedonically relevant psychological processes, such as social comparison, dissonance reduction, self-reflection, self-evaluation, and person perception, in chronically happy and unhappy individuals. In support of a construal framework, self-rated happy and unhappy people have been shown to differ systematically in the particular cognitive and motivational strategies they use. Promising research directions for positive psychology in pursuit of the sources of happiness, as well as the implications of the construal approach for prescriptions for enhancing well-being, are discussed.

Question: What is the role of cognitive and motivational processes in well-being?


Predicting inter-thread cache contention on a chip multi-processor architecture. This paper studies the impact of L2 cache sharing on threads that simultaneously share the cache, on a chip multi-processor (CMP) architecture. Cache sharing impacts threads nonuniformly, where some threads may be slowed down significantly, while others are not. This may cause severe performance problems such as sub-optimal throughput, cache thrashing, and thread starvation for threads that fail to occupy sufficient cache space to make good progress. Unfortunately, there is no existing model that allows extensive investigation of the impact of cache sharing. To allow such a study, we propose three performance models that predict the impact of cache sharing on co-scheduled threads. The input to our models is the isolated L2 cache stack distance or circular sequence profile of each thread, which can be easily obtained on-line or off-line. The output of the models is the number of extra L2 cache misses for each thread due to cache sharing. The models differ by their complexity and prediction accuracy. We validate the models against a cycle-accurate simulation that implements a dual-core CMP architecture, on fourteen pairs of mostly SPEC benchmarks. The most accurate model, the inductive probability model, achieves an average error of only 3.9%. Finally, to demonstrate the usefulness and practicality of the model, a case study that details the relationship between an application's temporal reuse behavior and its cache sharing impact is presented.

Question: What is the impact of cache sharing on threads?


Delay Analysis of Hybrid WiFi-LiFi System. Heterogeneous wireless networks are capable of effectivel y leveraging different access technologies to provide a wid e variety of coverage areas. In this paper, the coexistence of WiFi and visible light communication (VLC) is investigated as a para digm. The delay of two configurations of such heterogeneous system has been evaluated. In the first configuration, the non-aggregat ed system, any request is either allocated to WiFi or VLC. While in the se cond configuration, the aggregated system, each request is s plit into two pieces, one is forwarded to WiFi and the other is forw arded to VLC. Under the assumptions of Poisson arrival proce ss of requests and the exponential distribution of requests si ze, it is mathematically proved that the aggregated system p rovides lower minimum average system delay than that of the non-aggregate d system. For the non-aggregated system, the optimal traffic allo ation ratio is derived. For the aggregated system, an efficient sol ution for the splitting ratio is proposed. Empirical result show that the solution proposed here incurs a delay penalty (less than 3%) over the optimal result.

Question: What is the delay of a heterogeneous wireless network?