Time-driven activity-based costing.. In the classroom, activity-based costing (ABC) looks like a great way to manage a company's limited resources. But executives who have tried to implement ABC in their organizations on any significant scale have often abandoned the attempt in the face of rising costs and employee irritation. They should try again, because a new approach sidesteps the difficulties associated with large-scale ABC implementation. In the revised model, managers estimate the resource demands imposed by each transaction, product, or customer, rather than relying on time-consuming and costly employee surveys. This method is simpler since it requires, for each group of resources, estimates of only two parameters: how much it costs per time unit to supply resources to the business's activities (the total overhead expenditure of a department divided by the total number of minutes of employee time available) and how much time it takes to carry out one unit of each kind of activity (as estimated or observed by the manager). This approach also overcomes a serious technical problem associated with employee surveys: the fact that, when asked to estimate time spent on activities, employees invariably report percentages that add up to 100. Under the new system, managers take into account time that is idle or unused. Armed with the data, managers then construct time equations, a new feature that enables the model to reflect the complexity of real-world operations by showing how specific order, customer, and activity characteristics cause processing times to vary. This Tool Kit uses concrete examples to demonstrate how managers can obtain meaningful cost and profitability information, quickly and inexpensively. Rather than endlessly updating and maintaining ABC data,they can now spend their time addressing the deficiencies the model reveals: inefficient processes, unprofitable products and customers, and excess capacity.

Question: What is time-driven activity-based costing?


QUASY: Quantitative Synthesis Tool. We present the tool Q UASY, a quantitative synthesis tool. Q UASY takes qualitative and quantitative specifications and automatic ally onstructs a system that satisfies the qualitative specification and optimizes the qu antitative specification, if such a system exists. The user can choose between a system that satisfies and optimi zes the specifications (a) under all possible environment behaviors or (b) under th most-likely environment behaviors given as a probability distribution on the po ssible input sequences. QUASY solves these two quantitative synthesis problems by reduct ion to instances of 2-player games and Markov Decision Processes (MDPs) with qua ntitative winning objectives. QUASY can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with meanpayoff objectives, and ergodic MDPs with mean-payoff parit y objectives.

Question: What is a quantitative synthesis tool?


Bias Field Inconsistency Correction of Motion-Scattered Multislice MRI for Improved 3D Image Reconstruction. A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.

Question: How to correct bias field inconsistency in motion-scattered multislice MRI?


Full bridge phase-shifted soft switching high-frequency inverter with boost PFC function for induction heating system. This paper is mainly concerned with a high frequency soft-switching PWM inverter suitable for consumer induction heating system. Proposed system is composed of soft switching chopper based voltage boost PFC input stage and phase shifted PWM controlled full bridge ZCZVS high frequency inverter stage. Its fundamentals of operating performances are illustrated and evaluated on the experimental results. Its effectiveness is substantially proved on the basis of the experimental results from a practical point of view.

Question: What is a high frequency soft switching inverter?


Affective state detection via facial expression analysis within a human-computer interaction context. The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users' self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.

Question: How can facial expressions be used to detect affective states?


A PatchMatch-Based Dense-Field Algorithm for Video Copy–Move Detection and Localization. We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.

Question: What is a video copy–move?


A distributed representation of temporal context. The author acknowledge support from National Institute of Health Research Grants MH55687 and AG15685. Marc Howard is now at the Department of Psychology, Boston University, 64 Cunningham Street, Boston, MA 02215. Address correspondence and reprint requests to Michael J. Kahana, Volen Center for Complex Systems, Brandeis University, MS 013, Waltham, MA 02254. E-mail: kahana@brandeis.edu. Marc W. Howard and Michael J. Kahana

Question: What is the author of this paper?


Predicting Movie Revenue from IMDb Data. Given the information known about a movie in the we ek of its release, can we predict the total gross revenue for that movie? Such information would be u s f l to marketers, theater operators, and others i n the movie industry, but it is a hard problem, even for human beings. We found that, given a set of numeric , textbased, and sentiment features from IMDb, linear reg ession outperforms class-based logistic regression at predicting gross revenue. However, neither gives su fficiently precise results to be used in practice.

Question: How can we predict movie revenue?


Earliest Deadline Scheduling for Real-Time Database Systems. Earlier studies have observed that in moderately-loaded real-time database systems, using an Earliest Deadline policy to schedule tasks results in the fewest missed deadlines. When the real-time system is overloaded, however, an Earliest Deadline schedule performs worse than most other policies. This is due to Earliest Deadline giving the highest priority to transactions that are close to missing their deadlines. In this paper, we present a new priority assignment algorithm called Adaptive Earliest Deadline (AED), which features a feedback control mechanism that detects overload conditions and modifies transaction priority assignments accordingly. Using a detailed simulation model, we compare the performance of AED with respect to Earliest Deadline and other fixed priority schemes. We also present and evaluate an extension of the AED algorithm called Hierarchical Earliest Deadline (HED), which is designed to handle applications that assign different values to transactions and where the goal is to maximize the total value of the in-time transactions.

Question: What is the earliest deadline scheduling algorithm?


PREDICTING HELPFUL POSTS IN OPEN-ENDED DISCUSSION FORUMS: A NEURAL ARCHITECTURE. Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread, to aid users comprehend long running discussion threads, which often contain repetitive or even irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on five different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.

Question: How to predict helpful posts in a forum?


The reduction of graphene oxide. Graphene has attracted great interest for its excellent mechanical, electrical, thermal and optical properties. It can be produced by micro-mechanical exfoliation of highly ordered pyrolytic graphite, epitaxial growth, chemical vapor deposition, and the reduction of graphene oxide (GO). The first three methods can produce graphene with a relatively perfect structure and excellent properties, while in comparison, GO has two important characteristics: (1) it can be produced using inexpensive graphite as raw material by cost-effective chemical methods with a high yield, and (2) it is highly hydrophilic and can form stable aqueous colloids to facilitate the assembly of macroscopic structures by simple and cheap solution processes, both of which are important to the large-scale uses of graphene. A key topic in the research and applications of GO is the reduction, which partly restores the structure and properties of graphene. Different reduction processes result in different properties of reduced GO (rGO), which in turn affect the final performance of materials or devices composed of rGO. In this contribution, we review the state-of-art status of the reduction of GO on both techniques and mechanisms. The development in this field will speed the applications of graphene. 2011 Elsevier Ltd. All rights reserved.

Question: What is the reduction of graphene oxide?


Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph. Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm's existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

Question: What is the difference between a deduction graph and a deduction tree?


A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid. Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.

Question: What is a Markov random field?


A Deep Learning Approach to IoT Authentication. At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.

Question: What is a deep learning approach to IoT authentication?


CPLR: Collaborative pairwise learning to rank for personalized recommendation. Compared with explicit feedback data, implicit feedback data is easier to be collected and more widespread. However, implicit feedback data is also more difficult to be analyzed due to the lack of negative examples. Bayesian personalized ranking (BPR) is a well-known algorithm for personalized recommendation on implicit feedback data due to its high performance. However, it (1) treats all the unobserved feedback the same as negative examples which may be just caused by unseen, (2) treats all the observed feedback the same as positive examples which may be just caused by noisy action, and (3) assumes the preferences of users are independent which is difficult to achieve in reality. To solve all these problems, we propose a novel personalized recommendation algorithm called collaborative pairwise learning to rank (CPLR), which considers the influence between users on the preferences for both items with observed feedback and items without. To take these information into consideration, we try to optimize a generalized AUC instead of the standard AUC used in BPR. CPLR can be seen as a generalized BPR. Besides BPR, several extension algorithms of BPR, like social BPR (SBPR) and group preference based BPR (GBPR), are special cases of CPLR. Extensive experiments demonstrate the promise of our approach in comparison with traditional collaborative filtering methods and state-of-the-art pairwise learning to rank algorithms. Compared with the performance of baseline algorithms on five real-world data sets, the improvements of CPLR are over 17%, 23% and 22% for Pre@5, MAP and NDCG, respectively. © 2018 Published by Elsevier B.V.

Question: What is the difference between collaborative filtering and pairwise learning to rank?


An Axiomatic Basis for Computer Programming. In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference which can be used in proofs of the properties of computer programs. Examples are given of such axioms and rules, and a formal proof of a simple theorem is displayed. Finally, it is argued that important advantage, both theoretical and practical, may follow from a pursuance of these topics.

Question: What is an axiomatic basis for computer programming?


Discovering Available Drinks Through Natural , Robot-Led , Human-Robot Interaction Between a Waiter and a Bartender. This research focuses on natural, robot-led, human-robot interaction that enables a robot to discover what drinks a barman can prepare through continuous application of speech recognition, understanding and generation. Speech was recognised using Google Cloud's speech to text API, understood by matching either the object or main verb of a sentence against a list of key words and, finally, generated using templates with variable parts. The difficulty lies in the large quantity of key words, as they are based on the properties of the ordered drinks. The results show that having the aforementioned interaction works well to some extent, i.e. the naturalness of the interaction was ranked 5.5 on average. Furthermore, the obtained precision when identifying the unavailable drinks was 0.625 and the obtained recall was 1, resulting in an F1 measure of 0.769.

Question: What drinks can a waiter prepare?


A probabilistic fuzzy logic system for modeling and control. In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.

Question: What is a probabilistic fuzzy logic system?


Graph Based Relational Features for Collective Classification. Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.

Question: What is the difference between collective inference and standard ML?


Learning to fly by crashing. How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see:

Question: How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles?


Confidence Measures for Neural Network Classifiers. Neural Networks are commonly used in classification and decision tasks. In this paper, we focus on the problem of the local confidence of their results. We review some notions from statistical decision theory that offer an insight on the determination and use of confidence measures for classification with Neural Networks. We then present an overview of the existing confidence measures and finally propose a simple measure which combines the benefits of the probabi-listic interpretation of network outputs and the estimation of the quality of the model by bootstrap error estimation. We discuss empirical results on a real-world application and an artificial problem and show that the simplest measure behaves often better than more sophisticated ones, but may be dangerous under certain situations.

Question: What is the confidence of a neural network?


Par4: very high speed parallel robot for pick-and-place. This paper introduces a four-degree-of-freedom parallel manipulator dedicated to pick-and-place. It has been developed with the goal of reaching very high speed. This paper shows that its architecture is particularly well adapted to high dynamics. Indeed, it is an evolution of Delta, H4 and 14 robots architectures: it keeps the advantages of these existing robots, while overcoming their drawbacks. In addition, an optimization method based on velocity using adept motion has been developed and applied to this high speed parallel robot. All these considerations led to experimentations that proved we can reach high accelerations (13 G) and obtain a cycle time of 0.28 s.

Question: What is a high speed parallel robot?


SQL Database Primitives for Decision Tree Classifiers. Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.

Question: What is a decision tree classifier?


Manual Evaluation of the Pelvic Floor : State of the Art and Evaluative Hypothesis. The pelvic floor is an anatomical area where the balance of different visceral, muscular and liquid pressures play a fundamental role in the physiological pursuit of the functions of all the structures contained therein. When the balance is broken, multiple disorders and pathologies arise, requiring conservative or surgical multidisciplinary treatments. The focus of this article is to propose a manual evaluation of the musculoskeletal structures of the pelvic floor since a complete palpatory examination taking into account the muscular, articular and ligamentous aspect of the pelvic area is currently missing. The detection of the abnormal area is a determining factor to organize properly the therapeutic work because, potentially resulting in better results. According to the Authors' knowledge, this is the first article in the current scientific landscape proposing a complete manual evaluation.

Question: What is the pelvic floor?


Image-Dependent Gamut Mapping as Optimization Problem. We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests.

Question: What is gamut mapping?


Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-theart performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO.

Question: How to generate captions for images?


A Comprehensive Survey on Bengali Phoneme Recognition.  SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed.

Question: What is the bengali phoneme recognition?


Using web security scanners to detect vulnerabilities in web services. Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.

Question: What is the difference between a web vulnerability scanner and a web vulnerability scanner?


Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition. Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.

Question: What is the classification of motor imagery BCI using multivariate empirical mode decomposition?


Estimation of land surface temperature over Delhi using Landsat-7 ETM +. Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.

Question: How to estimate land surface temperature over Delhi using Landsat-7 ETM+ satellite data?


Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research.. Progress and issues in the study of coping with stress during childhood and adolescence are reviewed. Definitions of coping are considered, and the relationship between coping and other aspects of responses to stress (e.g., temperament and stress reactivity) is described. Questionnaire, interview, and observation measures of child and adolescent coping are evaluated with regard to reliability and validity. Studies of the association of coping with symptoms of psychopathology and social and academic competence are reviewed. Initial progress has been made in the conceptualization and measurement of coping, and substantial evidence has accumulated on the association between coping and adjustment. Problems still remain in the conceptualization and measurement of coping in young people, however, and aspects of the development and correlates of coping remain to be identified. An agenda for future research on child-adolescent coping is outlined.

Question: What is the definition of coping?


A Dataset for Web-Scale Knowledge Base Population. For many domains, structured knowledge is in short supply, while unstructured text is plentiful. Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope. However, existing datasets for KBP are all limited by multiple issues: small in size, not open or accessible, only capable of benchmarking a fraction of the KBP process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset to remain current as new crawls and DBpedia dumps are released. Also, the modularity of the released tool set resolves a crucial tension between the ease that a dataset can be used for a particular subtask in KBP and the number of different subtasks it can be used to train or benchmark.

Question: What is a web-scale dataset for knowledge base population?


Integration of Modular Process Units Into Process Control Systems. The modularization of process plants is regarded as a promising approach to cope with upcoming requirements in the process industry regarding flexibility. Within the Decentralized Intelligence for Modular Assets Project, a concept has been developed to overcome current deficiencies in implementing modular plants and especially their automation. On one hand, the approach considers a clear separation of engineering efforts into plant-independent module engineering and plant-specific integration engineering. On the other hand, the concept provides a method for the fast integration of a module's automation system into a higher level process control system. To do so, each module provides its process functionality in encapsulated services. The core of the integration method is a file-based description of the module including its services, operating screens, and communication variables. The so-called "module-type package" is currently under standardization by different organizations. This paper presents the general approach as well as current results of the standardization efforts. Furthermore, the results are executed on an application demonstrator to allow a practical assessment. Thus, module engineering and integration engineering, as well as corresponding software tools can be demonstrated.

Question: What is the process of modularization?


Ceiling continuum arm with extensible pneumatic actuators for desktop workspace. We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.

Question: What is the maximum elongation ratio of the arm?


Sixth sense technology: Comparisons and future predictions. This paper discusses a technology that is expected to widely spread in the near future, which is the Sixth Sense Technology. With this new technology yet to be introduced in the market, different implementation approaches are discussed. Such approaches are demonstrated through other inventions similar to the Sixth Sense (SS). They all fall into the same category, which is Augmented Reality Technologies. The paper focuses on the possible applications and opportunities of such technology. Furthermore, the implementation approaches are compared and the pros/cons of each approach are explored. Most importantly, technical challenges and open issues regarding each implementation approach are brought out. Furthermore, predictions are made on which approach is expected to succeed in the coming years. Finally, solutions are discussed to improve the Sixth Sense Technology with regards to its implementation approaches to insure new and better ways of human-computer interaction.

Question: What is the sixth sense technology?


Crowdfunding: A Spimatic application of digital fandom. The digital environment has opened up new opportunities for fans to engage with the production process of their favoured texts. The successful Kickstarter campaigns for Rob Thomas and Kristen Bell's Veronica Mars film and Zach Braff's film Wish I Was Here, and the failed Kickstarter campaign for Melissa Joan Hart's film Darci's Walk of Shame, each illustrate how the entertainers drew (or did not draw) on specific support from his or her own fan communities to generate funds and word-of-mouth publicity. In this paper I augment studies of digital fandom by integrating the technosocial concept of the Spime into the relationships forged between the technology of crowdfunding and the affect of particular audiences. I illustrate how 'crowdfunding fandom' changes the unique posthuman experience of 21 century participatory culture by interpreting fandom in a Spimatic network. This ultimately more fully integrates the fan into the production process, creating a truly 'participatory culture'.

Question: What is a Spimatic?


The Role of Gender Stereotypes in Perceptions of Entrepreneurs and Intentions to Become an Entrepreneur. In this study we examine the role of socially constructed gender stereotypes in entrepreneurship and their influence on men and women's entrepreneurial intentions. Data on characteristics of males, females, and entrepreneurs were collected from young adults in three countries. As hypothesized, entrepreneurs were perceived to have predominantly masculine characteristics. Additional results revealed that although both men and women perceive entrepreneurs to have characteristics similar to those of males (masculine genderrole stereotype), only women also perceived entrepreneurs and females as having similar characteristics (feminine gender-role stereotype). Further, though men and women did not differ in their entrepreneurial intentions, those who perceived themselves as more similar to males (high on male gender identification) had higher entrepreneurial intentions than those who saw themselves as less similar to males (low male gender identification). No such difference was found for people who saw themselves as more or less similar to females (female gender identification). The results were consistent across the three countries. Practical implications and directions for future research are discussed.

Question: What is the role of gender stereotypes in perceptions of entrepreneurs?


Beyond the PDP-11: Architectural Support for a Memory-Safe C Abstract Machine. We propose a new memory-safe interpretation of the C abstract machine that provides stronger protection to benefit security and debugging. Despite ambiguities in the specification intended to provide implementation flexibility, contemporary implementations of C have converged on a memory model similar to the PDP-11, the original target for C. This model lacks support for memory safety despite well-documented impacts on security and reliability.  Attempts to change this model are often hampered by assumptions embedded in a large body of existing C code, dating back to the memory model exposed by the original C compiler for the PDP-11. Our experience with attempting to implement a memory-safe variant of C on the CHERI experimental microprocessor led us to identify a number of problematic idioms. We describe these as well as their interaction with existing memory safety schemes and the assumptions that they make beyond the requirements of the C specification. Finally, we refine the CHERI ISA and abstract model for C, by combining elements of the CHERI capability model and fat pointers, and present a softcore CPU that implements a C abstract machine that can run legacy C code with strong memory protection guarantees.

Question: What is the CHERI experimental microprocessor?


Computationally efficient low-pass FIR filter design using Cuckoo Search with adaptive Levy step size. This paper looks into efficient implementation of one dimensional low pass Finite Impulse Response filters using certain commonly used and state-of-the-art optimization techniques. Methods like Parks-McClellan (PM) equiripple design, Quantum-behaved Particle Swarm Optimization (QPSO) and Cuckoo Search Algorithm (CSA) with Levy Flight are employed and overall performance is further improved by hybridization and adaptive step size update. Various performance metrics are analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect correlation between the message and recovered signals.

Question: What is a frequency modulated radio transmission?


Bilingualism, Biliteracy, and Learning to Read: Interactions Among Languages and Writing Systems. Four groups of children in first grade were compared on early literacy tasks. Children in three of the groups were bilingual, each group representing a different combination of language and writing system, and children in the fourth group were monolingual speakers of English. All the bilingual children used both languages daily and were learning to read in both languages. The children solved decoding and phonological awareness tasks, and the bilinguals completed all tasks in both languages. Initial differences between the groups in factors that contribute to early literacy were controlled in an analysis of covariance, and the results showed a general increment in reading ability for all the bilingual children but a larger advantage for children learning two alphabetic systems. Similarly, bilinguals transferred literacy skills across languages only when both languages were written in the same system. Therefore, the extent of the bilingual facilitation for early reading depends on the relation between the two languages and writing systems.

Question: What is the difference between biliteracy and bilingualism?


GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.

Question: What is a knowledge model?


The Impact of Indoor Lighting on Students ' Learning Performance in Learning Environments : A knowledge internalization perspective. The purpose of this study is to identify the influence of indoor lighting on students' learning performance within learning environments from knowledge internalization perspective. This study is a comprehensive review of literatures base on the influence of indoor lighting on people's productivity and performance especially students' learning performance. The result that comes from this study shows that it is essential to improve lighting in learning environments to enhance students' learning performance and also motivate them to learn more. In this study the researchers utilized Pulay (2010) survey and measured the influence of lighting on students' learning performance. Utilizing survey data collected from 150 students from Alpha course in Malaysia. This study found significant impact between lighting quality and students' learning performance this finding is also supported by interview from two experts.

Question: What is the impact of indoor lighting on students' learning performance?


Model predictive control for three-level boost converter in photovoltaic systems. In this paper, a three-level Boost (TLB) converter maximum power point tracking (MPPT) control strategy for a two-stage photovoltaic (PV) system based on model predictive control (MPC) is proposed. This method realizes fast and precise control through establishment of a prediction model and minimization of a cost function. In the work, first a three-level predictive model for the boost converter is extracted, then a predictive control algorithm using the Perturb and Observe (P&O) method is proposed, and subsequently voltage balance of the neutral point of the converter is added to the control loop. Based on the proposed method, since the inverter is not obligated to control the DC-link neutral point voltage, its modulation can be used to realize other controls on the AC side to improve grid operation. Results of the control scheme have been verified through Matlab / Simulink, and the results have been in agreement with the expectations.

Question: What is the difference between a three-level boost converter and a two-level boost converter?


Pneumatic muscle actuators within robotic and mechatronic systems. Pneumatic artificial muscles (PAMs) as soft, lightweight and compliant actuators have great potential in applications for the actuations of new types of robots and manipulators. The favourable characteristics of fluidic muscles, such as high power-to-weight ratio and safe interaction with humans are also very suitable during the process of musculoskeletally rehabilitating patients and are often used in making artificial orthoses. This technology, despite the problems of control relatng to nonlinear phenomena, may also have wide future applications within industrial and mechatronic systems. This paper presents several experimental systems actuated by PAMs, which have been designed as test models within the fields of mobile robots, mechatronics, fluid power systems and the feedback control education of mechanical engineering students. This paper first presents the design and construction of a four legged walking robot actuated by pneumatic muscles. The robot has a fully autonomous system with a wireless application platform and can be controlled using a cell phone. Then the paper describes the design and construction of the prototype of an actively-powered ankle foot orthosis. This orthosis device actuated by a single PAM is able to provide the appropriate functions required during the rehabilitations of patients and the loss of mobility. Then the paper focuses on the design and control of a ball and beam system with an antagonistic muscle pair for generating the necessary torque for beam rotation. This mechatronic balancing mechanism falls into the category of unstable, under-actuated, multivariable systems with highly nonlinear dynamics. The final section of the article presents the design and control of a single-joint manipulator arm with pneumatic muscle actuators that enable some new features for the controlled systems. Fluid Power 2015 176

Question: What is the difference between a pneumatic muscle and a hydraulic muscle?


Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex. In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.

Question: How to improve the image dehazing algorithm?


Generative Deep Neural Networks for Dialogue: A Short Review. Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.

Question: What is a generative deep neural network for dialogue?


LoRa Scalability: A Simulation Model Based on Interference Measurements. LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data.

Question: How many nodes can be connected to a gateway?


Question: How to scale question answering to the Web?


Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot. In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s−1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot's performance and the validity of the proposed

Question: What is the difference between a quadruped and a legged robot?


An expanding reference library for Peer-to-Peer content. Many Peer-to-Peer (P2P) networks have emerged as scalable platforms for enabling file-sharing and content-distribution applications. Due to their reliance on decentralized models encompassing millions of users, P2P networks are subjected to being infested with contraband material, copyright infringing content, as well as malicious viruses and programs. Moreover, the dynamics involved as users join and leave these networks make investigating them quite complex and time-dependent. To that end, this paper presents PxP, a tool that creates an expanding reference library for content that appears on P2P networks. PxP continuously searches the P2P networks for new files, downloads and fingerprints them, deletes them, and add their meta data to a public reference library. We envision that this library will aid investigators in criminal investigations that involve contraband material, civil investigations with copyright infringement content. In addition, this library can be used to speed up forensics examination of media through skipping over some of the files shared on P2P networks and in validating carving tools. We assess the performance of our framework through analysis, simulations and real experiments for content collected from the Gnutella P2P network.

Question: What is the size of the canadian military?


Enhancing the quality of life through wearable technology. An overview of the key challenges facing the practice of medicine today is presented along with the need for technological solutions that can "prevent" problems. Then, the development of the Wearable Motherboard/spl trade/ (Smart Shirt) as a platform for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life-from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare - all aimed at enhancing the quality of life for humans - are presented.

Question: What is the future of wearable technology?


Sentiment Analysis and Summarization of Twitter Data. Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is product-based sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented (i.e. aspect-based) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features.

Question: What is the difference between sentiment analysis and summarization?


Examining the psychometric properties of a sport-related concussion survey: a Rasch measurement approach. Awareness of sport-related concussion (SRC) is an essential step in increasing the number of athletes or parents who report on SRC. This awareness is important, as there is no established data on medical care at youth-level sports and may be limited to individuals with only first aid training. In this circumstance, aside from the coach, it is the players and their parents who need to be aware of possible signs and symptoms. The aim of this study was to examine the psychometric properties of a parent and player concussion survey intended for use before and after an education campaign regarding SRC. 1441 questionnaires were received from parents and 284 questionnaires from players. The responses to the sixteen-item section of the questionnaire's 'recognition of signs and symptoms' were submitted to psychometric analysis using the dichotomous and polytomous Rasch model via the Rasch Unidimensional Measurement Model software RUMM2030. The Rasch model of Modern Test Theory can be considered a refinement of, or advance on, traditional analyses of an instrument's psychometric properties. The main finding is that these sixteen items measure two factors: items that are symptoms of concussion and items that are not symptoms of concussion. Parents and athletes were able to identify most or all of the symptoms, but were not as good at distinguishing symptoms that are not symptoms of concussion. Analyzing these responses revealed differential item functioning for parents and athletes on non-symptom items. When the DIF was resolved a significant difference was found between parents and athletes. The main finding is that the items measure two 'dimensions' in concussion symptom recognition. The first dimension consists of those items that are symptoms of concussion and the second dimension of those items that are not symptoms of concussion. Parents and players were able to iden

Question: What is the difference between a symptom and a non-symptom?


Developing Leadership Character in Business Programs. Our objective is to encourage and enable leadership character development in business education. Building on a model of character strengths and their link to virtues, values, and ethical decision making, we describe an approach to develop leadership character at the individual, group, and organizational levels. We contrast this approach to existing practices that have focused on teaching functional content over character and address how business educators can enable leadership character development through their own behaviors, relationships, and structures. Most important, we provide concrete suggestions on how to integrate a focus on character development into existing business programs, both in terms of individual courses as well as the overall curriculum. We highlight that the development of leadership character must extend beyond student engagement in a course since "it takes a village" to develop character. ........................................................................................................................................................................

Question: How to develop leadership character in business programs?


The Development of Honda Humanoid Robot. I n this paper. we present the mechanism, system cor$guration, basic control algorithm and integrated ,functions of the Nonda humanoid robot. Like its human counterpart, this robot lzas the ability to move forward and backward, sideways to the right or the left, as well as diagonally. I n addition, the robot can turn in any direction, walk up and down stairs continuously. Furthermore, due to its unique posture stability control, the robot is able to maintain its balance despite unexpected complications such as uneven ground su faces As a part of its integrated functions, this robot is able to inove on a planned path autonomously and to perform simple operations via wireless tele-operation.

Question: What is the development of honda humanoid robot?


Blood pressure-lowering effect of Shinrin-yoku (Forest bathing): a systematic review and meta-analysis. BACKGROUND Shinrin-yoku (experiencing the forest atmosphere or forest bathing) has received increasing attention from the perspective of preventive medicine in recent years. Some studies have reported that the forest environment decreases blood pressure. However, little is known about the possibility of anti-hypertensive applications of Shinrin-yoku. This study aimed to evaluate preventive or therapeutic effects of the forest environment on blood pressure.   METHODS We systematically reviewed the medical literature and performed a meta-analysis.Four electronic databases were systematically searched for the period before May 2016 with language restriction of English and Japanese. The review considered all published, randomized, controlled trials, cohort studies, and comparative studies that evaluated the effects of the forest environment on changes in systolic blood pressure. A subsequent meta-analysis was performed.   RESULTS Twenty trials involving 732 participants were reviewed. Systolic blood pressure of the forest environment was significantly lower than that of the non-forest environment. Additionally, diastolic blood pressure of the forest environment was significantly lower than that of the non-forest environment.   CONCLUSIONS This systematic review shows a significant effect of Shinrin-yoku on reduction of blood pressure.

Question: What is the effect of forest bathing on blood pressure?


Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition. Recently, deep learning techniques have been successfully applied to automatic speech recognition tasks -first to phonetic recognition with context-independent deep belief network (DBN) hidden Markov models (HMMs) and later to large vocabulary continuous speech recognition using context-dependent (CD) DBN-HMMs. In this paper, we report our most recent experiments designed to understand the roles of the two main phases of the DBN learning -pre-training and fine tuning -in the recognition performance of a CD-DBN-HMM based large-vocabulary speech recognizer. As expected, we show that pre-training can initialize weights to a point in the space where fine-tuning can be effective and thus is crucial in training deep structured models. However, a moderate increase of the amount of unlabeled pre-training data has an insignificant effect on the final recognition results as long as the original training size is sufficiently large to initialize the DBN weights. On the other hand, with additional labeled training data, the fine-tuning phase of DBN training can significantly improve the recognition accuracy.

Question: What is the role of pre-training in context-dependent deep belief networks?


Arabic language sentiment analysis on health services. The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.

Question: What is the process of collecting data from Twitter?


MigCEP: operator migration for mobility driven distributed complex event processing. A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure.  This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns.

Question: What is the migration of operators for mobility driven distributed complex event processing?


Show Me You Care: Trait Empathy, Linguistic Style, and Mimicry on Facebook. Linguistic mimicry, the adoption of another's language patterns, is a subconscious behavior with pro-social benefits. However, some professions advocate its conscious use in empathic communication. This involves mutual mimicry; effective communicators mimic their interlocutors, who also mimic them back. Since mimicry has often been studied in face-to-face contexts, we ask whether individuals with empathic dispositions have unique communication styles and/or elicit mimicry in mediated communication on Facebook. Participants completed Davis's Interpersonal Reactivity Index and provided access to Facebook activity. We confirm that dispositional empathy is correlated to the use of particular stylistic features. In addition, we identify four empathy profiles and find correlations to writing style. When a linguistic feature is used, this often "triggers" use by friends. However, the presence of particular features, rather than participant disposition, best predicts mimicry. This suggests that machine-human communications could be enhanced based on recently used features, without extensive user profiling.

Question: What is the difference between mimicry and empathy?


Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player's risk of subsequent injury.. The return to sport from injury is a difficult multifactorial decision, and risk of reinjury is an important component. Most protocols for ascertaining the return to play status involve assessment of the healing status of the original injury and functional tests which have little proven predictive ability. Little attention has been paid to ascertaining whether an athlete has completed sufficient training to be prepared for competition. Recently, we have completed a series of studies in cricket, rugby league and Australian rules football that have shown that when an athlete's training and playing load for a given week (acute load) spikes above what they have been doing on average over the past 4 weeks (chronic load), they are more likely to be injured. This spike in the acute:chronic workload ratio may be from an unusual week or an ebbing of the athlete's training load over a period of time as in recuperation from injury. Our findings demonstrate a strong predictive (R(2)=0.53) polynomial relationship between acute:chronic workload ratio and injury likelihood. In the elite team setting, it is possible to quantify the loads we are expecting athletes to endure when returning to sport, so assessment of the acute:chronic workload ratio should be included in the return to play decision-making process.

Question: What is the acute:chronic workload ratio?


Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud. Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.

Question: Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud


Correntropy: Properties and Applications in Non-Gaussian Signal Processing. The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are presented. As such correntropy has vastly different properties compared with second-order statistics that can be very useful in non-Gaussian signal processing, especially in the impulsive noise environment. Examples are presented to illustrate the technique.

Question: What is the difference between correntropy and kernel methods?


Main-memory foreign key joins on advanced processors: design and re-evaluations for OLAP workloads. The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the surrogate key index oriented foreign key join as schema-conscious and OLAP workload customized design foreign key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign key join algorithm with surrogate key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables surrogate key index to be effective for foreign key joins in data warehousing workloads for both CPU and hardware accelerators.

Question: How to improve foreign key join performance?


Fast Supervised Discrete Hashing. Learning-based hashing algorithms are "hot topics" because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called "fast supervised discrete hashing" (FSDH) based on "supervised discrete hashing" (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.

Question: What is the difference between supervised discrete hashing and fast supervised discrete hashing?


Generating multi-fingered robotic grasps via deep learning. This paper presents a deep learning architecture for detecting the palm and fingertip positions of stable grasps directly from partial object views. The architecture is trained using RGBD image patches of fingertip and palm positions from grasps computed on complete object models using a grasping simulator. At runtime, the architecture is able to estimate grasp quality metrics without the need to explicitly calculate the given metric. This ability is useful as the exact calculation of these quality functions is impossible from an incomplete view of a novel object without any tactile feedback. This architecture for grasp quality prediction provides a framework for generalizing grasp experience from known to novel objects.

Question: How to generate multi-fingered robotic grasps via deep learning?


FIRST EXPERIMENTAL INVESTIGATIONS ON WHEEL-WALKING FOR IMPROVING TRIPLE-BOGIE ROVER LOCOMOTION PERFORMANCES. Deployment actuators of a triple-bogie rover locomotion platform can be used to perform Wheel-Walking (WW) manoeuvres. How WW could affect the traversing capabilities of rovers is a recurrent debate in the planetary robotics community. The Automation and Robotics Section of ESTEC has initiated a long term project to evaluate the performance of WW manoeuvres in different scenarios. This paper presents the first experimental results on this project, obtained during the test campaign run on November 2014 at the Planetary Robotics Lab (PRL) of ESTEC, and shows the performance analysis made when comparing WW with standard rolling. The PRL rover prototype ExoTeR was used to test three different scenarios: entrapment in loose soil, up-slope traverse and lander egressing. WW locomotion showed increased capabilities in all scenarios and proved its relevance and advantages for planetary exploration missions.

Question: What is the first experimental results on this project?


Paxos made live: an engineering perspective. We describe our experience in building a fault-tolerant data-base using the Paxos consensus algorithm. Despite the existing literature in the field, building such a database proved to be non-trivial. We describe selected algorithmic and engineering problems encountered, and the solutions we found for them. Our measurements indicate that we have built a competitive system.

Question: What is the Paxos consensus algorithm?


Nonparametric belief propagation. Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation (BP), have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.

Question: What is the nonparametric belief propagation?


Causal discovery and inference: concepts and recent methodological advances. This paper aims to give a broad coverage of central concepts and principles involved in automated causal inference and emerging approaches to causal discovery from i.i.d data and from time series. After reviewing concepts including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models, we present the constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, and discuss the assumptions underlying its validity. We then focus on causal discovery based on structural equations models, in which a key issue is the identifiability of the causal structure implied by appropriately defined structural equation models: in the two-variable case, under what conditions (and why) is the causal direction between the two variables identifiable? We show that the independence between the error term and causes, together with appropriate structural constraints on the structural equation, makes it possible. Next, we report some recent advances in causal discovery from time series. Assuming that the causal relations are linear with nonGaussian noise, we mention two problems which are traditionally difficult to solve, namely causal discovery from subsampled data and that in the presence of confounding time series. Finally, we list a number of open questions in the field of causal discovery and inference.

Question: What is the difference between causal discovery and causal inference?


3D Human Pose Estimation via Deep Learning from 2D Annotations. We propose a deep convolutional neural network for 3D human pose and camera estimation from monocular images that learns from 2D joint annotations. The proposed network follows the typical architecture, but contains an additional output layer which projects predicted 3D joints onto 2D, and enforces constraints on body part lengths in 3D. We further enforce pose constraints using an independently trained network that learns a prior distribution over 3D poses. We evaluate our approach on several benchmark datasets and compare against state-of-the-art approaches for 3D human pose estimation, achieving comparable performance. Additionally, we show that our approach significantly outperforms other methods in cases where 3D ground truth data is unavailable, and that our network exhibits good generalization properties.

Question: How does the proposed network perform?


Captology: A Critical Review. This critical review of B.J. Fogg's book Persuasive Technology regards captology as an eclectic and formative work. It summarises two other reviewers' work and identifies several new strengths. It scrutinises Fogg's functional triad computers functioning as tools, media and social actors and some categorical changes are recommended. It investigates further Johnson's concerns about specific ethical omissions, nominating a new term, compusuasion, for the resultant but unintended, exogenous behaviour/attitude change effects of captological design. The review commences to more carefully define what constitutes persuasion and draws attention to the distinction between persuasion techniques in general and the behavioural changes that result from advocacy and education. The reviewer concludes that a fundamental ethic be that the designer's intent be exposed at the commencement of the user's engagement with the program and proffers the idea of persuasion resulting in a new conviction, induced by others, as a helpful definition of

Question: What is the difference between persuasion and education?


A review of recent advances in visual speech decoding. Visual speech information plays an important role in automatic speech recognition (ASR) especially when audio is corrupted or even inaccessible. Despite the success of audio-based ASR, the problem of visual speech decoding remains widely open. This paper provides a detailed review of recent advances in this research area. In comparison with the previous survey [97] which covers the whole ASR system that uses visual speech information, we focus on the important questions asked by researchers and summarize the recent studies that attempt to answer them. In particular, there are three questions related to the extraction of visual features, concerning speaker dependency, pose variation and temporal information, respectively. Another question is about audio-visual speech fusion, considering the dynamic changes of modality reliabilities encountered in practice. In addition, the state-of-the-art on facial landmark localization is briefly introduced in this paper. Those advanced techniques can be used to improve the region-of-interest detection, but have been largely ignored when building a visual-based ASR system. We also provide details of audio-visual speech databases. Finally, we discuss the remaining challenges and offer our insights into the future research on visual speech decoding.

Question: What is the state of the art in visual speech decoding?


A low-power Adaboost-based object detection processor using Haar-like features. This paper presents an architecture of a low-power real-time object detection processor using Adaboost with Haar-Like features. We employ a register array based architecture, and introduce two architectural-level power optimization techniques; signal gating domain for integral image extraction, and low-power integral image update. The power efficiency of our proposed architecture including nine classifiers is estimated to be 0.64mW/fps when handling VGA(640 × 480) 70fps video.

Question: What is a low-power Adaboost-based object detection processor using Haar-like features?


Summarization from medical documents: a survey. OBJECTIVE The aim of this paper is to survey the recent work in medical documents summarization.   BACKGROUND During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc.   METHODOLOGY This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics.   DISCUSSION AND CONCLUSIONS The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

Question: What is the background of medical documents summarization?


Real-time wireless communication with per-packet deadlines. The last decades' tremendous advances in wireless communications have been driven mainly by personal communications. Radio resource allocation mechanisms for optimizing key metrics, such as average throughput and delay, for such traffic are by now rather well-developed. However, with the increased interest in wireless machine-to-machine communication, e.g. for industrial control or monitoring of large-scale infrastructures, new challenges emerge. The performance of an estimator or closed-loop control system that operates over an unreliable wireless network depends on the full latency and loss distributions and not only on their averages. For these applications, more suitable performance metrics are per-packet guarantees on latency and reliability (on-time delivery). This thesis studies optimal forwarding of deadline-constrained traffic over lossy multi-hop networks. We assume a routing topology in the form of a directed graph with packet loss on links described by finite-state Markov chains, and focus on a single transient packet scenario. Two problems are considered: maximizing the probability that packets are delivered within their specified deadline; and minimizing the expected energy cost with a guaranteed probability of on-time delivery. The first problem can be formulated as a finite-horizon Markov decision process (MDP), while the second problem is a finite-horizon constrained Markov decision process (CMDP). A general dynamic programming framework that solves a weighted sum of reliability and energy maximization problem is proposed. The maximum deadline-constrained reliability problem is solved by studying a simplification of the general dynamic programming. The minimum energy optimal policy is a random selection between two deterministic and computable forwarding policies, each of which can be found via a dynamic programming framework. Particular instances with Bernoulli and Gilbert-Elliot loss mode

Question: What is the minimum energy optimal policy?


GaN-based high frequency totem-pole bridgeless PFC design with digital implementation. Totem-pole bridgeless PFC is a very promising topology for GaN devices because of very low reverse recovery. However, inherent challenges exist for this topology at zerocrossing point of AC voltage where inductor, current spike is observed which may cause higher harmonics and EMI issue. This paper looks deeply into this phenomena and found two main causes: the transition delay of low frequency leg and the minimum pulse width limit of high frequency leg. A DSP-based digital modulator is designed to address those two issues by applying lead time to the low frequency leg and digital dithering to increase equivalent resolution at the high frequency leg. Some other rules are also summarized to enable robust modulator design, which is the main challenge in this topology. A 500 kHz GaN totem-pole bridgeless PFC converter operating in continuous conduction mode verified the effectiveness of those methods to eliminate the current spike at zero-crossing. All those findings will help the implementation of wide band gap devices in very high frequency PFC circuit.

Question: What is the totem-pole bridgeless PFC topology?


A Quantitative Comparison of Semantic Web Page Segmentation Approaches. This paper explores the effectiveness of different semantic web page segmentation algorithms on modern websites. We compare three known algorithms each serving as an example of a particular approach to the problem, and one self-developed algorithm, WebTerrain, that combines two of the approaches. With our testing framework we have compared the performance of four algorithms for a large benchmark we have constructed. We have examined each algorithm for a total of eight different configurations (varying datasets, evaluation metric and the type of the input HTML documents). We found that all algorithms performed better on random pages on average than on popular pages, and results are better when running the algorithms on the HTML obtained from the DOM rather than on the plain HTML. Overall there is much room for improvement as we find the best average F-score to be 0.49, indicating that for modern websites currently available algorithms are not yet of practical use.

Question: What is the best algorithm for semantic web page segmentation?


Security challenges in software defined network and their solutions. The main purpose of Software Defined Networking (SDN) is to allow network engineers to respond quickly to changing network industrial requirements. This network technology focuses on making network as adaptable and active as virtual server. SDN is physical separation of Control plane from Data plane and control plane is centralized to manage underlying infrastructure. Hence, the SDN permit network administrator to adjust wide traffic flow from centralized control console without having to touch Switches and Routers, and can provide the services to wherever they are needed in the network. As in SDN the control plane is disassociated from underlying forwarding plane, however, susceptible to many security challenges like Denial of Service (DoS) attack, Distributed DoS (DDoS) attack, Volumetric attack. In this paper, we highlight some security challenges and evaluate some security solutions.

Question: What are some security challenges in software defined network?


Tools for ontology matching practical considerations. There exists a large body of scienti c literature devoted to ontology matching, aligning, mapping translating and merging. With it, comes a long list (90+) of tools that support various aspects of these operations. We have approached such tools from the perspective of the INTER-IoT project, in which one of the goals is to facilitate semantic interoperability of Internet of Things platforms. Thus, we had to answer a question: what is actually available when one needs to align/merge ontologies. Here, we summarize our ndings.

Question: What tools are available for ontology matching?


The impact of modified Hatha yoga on chronic low back pain: a pilot study.. PURPOSE The purpose of this randomized pilot study was to evaluate a possible design for a 6-week modified hatha yoga protocol to study the effects on participants with chronic low back pain.   PARTICIPANTS Twenty-two participants (M = 4; F = 17), between the ages of 30 and 65, with chronic low back pain (CLBP) were randomized to either an immediate yoga based intervention, or to a control group with no treatment during the observation period but received later yoga training.   METHODS A specific CLBP yoga protocol designed and modified for this population by a certified yoga instructor was administered for one hour, twice a week for 6 weeks. Primary functional outcome measures included the forward reach (FR) and sit and reach (SR) tests. All participants completed Oswestry Disability Index (ODI) and Beck Depression Inventory (BDI) questionnaires. Guiding questions were used for qualitative data analysis to ascertain how yoga participants perceived the instructor, group dynamics, and the impact of yoga on their life.   ANALYSIS To account for drop outs, the data were divided into better or not categories, and analyzed using chi-square to examine differences between the groups. Qualitative data were analyzed through frequency of positive responses.   RESULTS Potentially important trends in the functional measurement scores showed improved balance and flexibility and decreased disability and depression for the yoga group but this pilot was not powered to reach statistical significance. Significant limitations included a high dropout rate in the control group and large baseline differences in the secondary measures. In addition, analysis of the qualitative data revealed the following frequency of responses (1) group intervention motivated the participants and (2) yoga fostered relaxation and new awareness/learning.   CONCLUSION A modified yoga-based intervention may benefit individuals with CLB

Question: What is the impact of modified hatha yoga on chronic low back pain?


Color and Texture Based Identification and Classification of food Grains using different Color Models and Haralick features. This paper presents the study on identification and classification of food grains using different color models such as L*a*b, HSV, HSI and YCbCr by combining color and texture features without performing preprocessing. The K-NN and minimum distance classifier are used to identify and classify the different types of food grains using local and global features. Texture and color features are the important features used in the classification of different objects. The local features like Haralick features are computed from co-occurrence matrix as texture features and global features from cumulative histogram are computed along with color features. The experiment was carried out on different food grains classes. The non-uniformity of RGB color space is eliminated by L*a*b, HSV, HSI and YCbCr color space. The correct classification result achieved for different color models is quite good. KeywordsFeature Extraction; co-occurrence matrix; texture information; Global Features; cumulative histogram; RGB, L*a*b, HSV, HSI and YCbCr color model.

Question: What is the difference between RGB and L*a*b color space?


Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure. The introduction of "smart grid" solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from "islands of automation" to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with "openness" requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for "smart grid" solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a "smart grid system".

Question: What is cyber security?