BakerSFIeld : Bringing software fault isolation to x 64. We present BakerSFIeld, a Software Fault Isolation (SFI) framework for the x64 instruction set architecture. BakerSFIeld is derived from PittSFIeld, and retains the assembly language rewriting style of sandboxing, but has been substantially re-architected for greater clarity, separating the concerns of rewriting the program for memory safety, and re-aligning the code for control-flow safety. BakerSFIeld also corrects several significant security flaws we found in PittSFIeld. We measure the performance of BakerSFIeld on a modern 64-bit desktop machine and determine that it has significantly lower overhead than its 32-bit cousin.

Question: What is the performance of BakerSFIeld?


Marital status and health: United States, 1999-2002.. OBJECTIVE This report presents prevalence estimates by marital status for selected health status and limitations, health conditions, and health risk behaviors among U.S. adults, using data from the 1999-2002 National Health Interview Surveys (NHIS).   METHODS Data for the U.S. civilian noninstitutionalized population were collected using computer-assisted personal interviews (CAPI). The household response rate for the NHIS was 88.7%. This report is based on a total of 127,545 interviews with sample adults aged 18 years and over, representing an overall response rate of 72.4% for the 4 years combined. Statistics were age-adjusted to the 2000 U.S. standard population. Marital status categories shown in this report are: married, widowed, divorced or separated, never married, and living with a partner.   RESULTS Regardless of population subgroup (age, sex, race, Hispanic origin, education, income, or nativity) or health indictor (fair or poor health, limitations in activities, low back pain, headaches, serious psychological distress, smoking, or leisure-time physical inactivity), married adults were generally found to be healthier than adults in other marital status categories. Marital status differences in health were found in each of the three age groups studied (18-44 years, 45-64 years, and 65 years and over), but were most striking among adults aged 18-44 years. The one negative health indicator for which married adults had a higher prevalence was overweight or obesity. Married adults, particularly men, had high rates of overweight or obesity relative to adults in other marital status groups across most population subgroups studied. Never married adults were among the least likely to be overweight or obese.

Question: What is the difference in health between married and unmarried adults?


Mixing Confidential Transactions: Comprehensive Transaction Privacy for Bitcoin. The public nature of the blockchain has been shown to be a severe threat for the privacy of Bitcoin users. Even worse, since funds can be tracked and tainted, no two coins are equal, and fungibility, a fundamental property required in every currency, is at risk. With these threats in mind, several privacyenhancing technologies have been proposed to make Bitcoin more private. However, they either require a deep redesign of the currency, breaking many currently deployed features, or they address only specific privacy issues and consequently provide only very limited guarantees when deployed separately. The goal of this work is to overcome this trade-off. Building on CoinJoin, we design ValueShuffle, the first coin mixing protocol compatible with Confidential Transactions, a proposal to hide payment values in transactions. ValueShuffle ensures a mixing participant's anonymity and the confidentiality of her payment values not only against an attacker observing the blockchain but also against the other possibly malicious mixing participants and against network attackers. By combining ValueShuffle with the proposal for Confidential Transactions and additionally Stealth Addresses, our solution provides comprehensive privacy (payer anonymity, payee anonymity, and payment value privacy) without breaking with the design or the features of Bitcoin. We demonstrate that the combination of these three privacyenhancing technologies creates synergies that overcome the two major obstacles which so far have prohibited the deployment of coin mixing in practice, namely that users need to mix funds of the same value, and need to do so before they can actually spend the funds. As a result, our approach unleashes the full potential of coin mixing as a privacy solution for Bitcoin.

Question: What is the first large-scale Canadian peacekeeping mission?


A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

Question: What is the underlying mechanism of interactions?


Dude, where's my card?: RFID positioning that works with multipath and non-line of sight. RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.

Question: How do you use a radar to locate an object?


CHAPTER 5 SOFTWARE TESTING. As more extensively discussed in the Software Quality chapter of the Guide to the SWEBOK, the right attitude towards quality is one of prevention: it is obviously much better to avoid problems, rather than repairing them. Testing must be seen as a means primarily for checking whether the prevention has been effective, but also for identifying anomalies in those cases in which, for some reason, it has been not. It is perhaps obvious, but worth recognizing, that even after successfully completing an extensive testing campaign, the software could still contain faults; nor is defect free code a synonymous for quality product. The remedy to system failures that are experienced after delivery is provided by (corrective) maintenance actions. Maintenance topics are covered into the Software Maintenance chapter of the Guide to the SWEBOK.

Question: What is the difference between testing and maintenance?


Sensor Log: A mobile data collection and annotation application. Smartphones equipped with sensors can be used for monitoring simple daily activities (e.g., walking, climbing stairs etc.). The data collected via sensors need to be partially or completely annotated in order to be used in the training phase of learning algorithms. In order to ease the time consuming and burdensome annotation process, a mobile data collection and annotation application named Sensor Log is developed. The annotation feature and its ease of use make the developed application superior to existing systems. The application can be downloaded from the Android applications distribution platform.

Question: What is the name of the application?


A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report on a large scale experiment over half a million runs of C and a Naive Bayes algorithm to estimate the e ects of di erent parameters on these al gorithms on real world datasets For cross validation we vary the number of folds and whether the folds are strati ed or not for boot strap we vary the number of bootstrap sam ples Our results indicate that for real word datasets similar to ours the best method to use for model selection is ten fold strati ed cross validation even if computation power allows using more folds

Question: How to estimate the accuracy of a classifier?


The Virtual Human Interface: A Photorealistic Digital Human. o mimic the quality of everyday human communication, future computer interfaces must combine the benefits of high visual fidelity with conversational intelligence and—above all—the ability to modulate the emotions of their users. From a computer graphics or visual perspective, achieving this goal requires creating synthetic digital humans that have photorealistic faces capable of expressing the finest shades of emotion. From a perceptual point of view, the same system must also read users' emotional reactions and adapt the behavior of the digital human accordingly. Our research focuses on implementing this concept to create a face-to-face system called the Virtual Human Interface (VHI). Photorealistic virtual humans— in contrast to nonrealistic virtual characters—make a more effective communication interface between computers and humans because they can stimulate declarative and procedural memory in the human brain. Declarative memory is our memory of information and events. Procedural memory, on the other hand, is the memory of how to do something, such as riding a bike. Most computer interfaces primarily access users' declarative memory; the person learns information about a particular topic. The VHI system, however, engages the user through entertaining emotional responses that help engrave knowledge in the user's mind and even modify the user's behavior through procedural memory. During the past several decades, researchers have conducted countless studies on agents and human animation. Early work mainly addressed low-polygon virtual humans that users could animate in real time. 1 Many of these animation systems initially addressed purposes other than the needs of human–computer interaction. However, the requirements of facial ani-mation—and especially speech synthesis—demand a different underlying architecture that can effectively model how real faces move and change. 2-4 As a result, research began t

Question: What is the difference between a virtual human and a virtual character?


Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation. In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.

Question: How to stabilize the microgrid during islanded operation?


A Deep Learning Approach to Universal Skin Disease Classification. Skin diseases are very common in people's daily life. Each year, millions of people in American are affected by all kinds of skin disorders. Diagnosis of skin diseases sometimes requires a high-level of expertise due to the variety of their visual aspects. As human judgment are often subjective and hardly reproducible, to achieve a more objective and reliable diagnosis, a computer aided diagnostic system should be considered. In this paper, we investigate the feasibility of constructing a universal skin disease diagnosis system using deep convolutional neural network (CNN). We train the CNN architecture using the 23,000 skin disease images from the Dermnet dataset and test its performance with both the Dermnet and OLE, another skin disease dataset, images. Our system can achieve as high as 73.1% Top-1 accuracy and 91.0% Top-5 accuracy when testing on the Dermnet dataset. For the test on the OLE dataset, Top-1 and Top-5 accuracies are 31.1% and 69.5%. We show that these accuracies can be further improved if more training images are used.

Question: What is the accuracy of the deep learning approach to skin disease diagnosis?


The use of Social Networking Sites (SNSs) by the faculty members of the School of Library & Information Science, PAAET, Kuwait. Purpose – The purpose of this study is to describe the usage of Social Networking Sites (SNSs) by the faculty members of the School of Library and Information Science (SLIS), at the College of Basic Education, the Public Authority for Applied Education and Training (PAAET), Kuwait. Design/methodology/approach – A survey conducted to collect data from 33 faculty members of whom only 21 members were using SNSs, representing 63.6 per cent of the total sample, and 12 members were not using SNSs, representing 36.4 per cent of the total sample. This study revealed that SNSs are used moderately by the faculty members. Findings – This study showed that faculty members who were using SNSs tend to be males, aged between 41 and 50 years, PhD holders, ranked as assistant professors, full-time members, specialized in information technologies with a relatively new experience of teaching ranged from one to five years, and most of the faculty members who were not using SNSs tended to be also males, aged between 41 and 60 years, PhD holders, ranked as lecturers, full-time members specialized in organization of information with a teaching experience ranged from 16 to 20 years. More than half of the faculty members were using SNSs for three years to less than six years, and a large number of them were using SNSs several times a week and were accessing these sites more from their school office, home and school laboratory. There are no any statistical significant differences between the demographic data of participants (gender, age and education level) and either their use or non-use of SNSs. There are no significant differences between the academic rank, teaching status and teaching experience of faculty and their use of SNSs. However, there is a significant relation between the faculty's area of teaching and their use of SNSs. Faculty members were interested

Question: What is the use of social networking sites by faculty members of the school of library and information science?


DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer. Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.

Question: What is logo recognition?


Stable Marriage with Ties and Unacceptable Partners. An instance of the classical stable marriage problem involves n men and n women, and each person ranks all n members of the opposite sex in strict order of preference. The effect of allowing ties in the preference lists has been investigated previously, and three natural definitions of stability arise. In this paper, we extend this study by allowing a preference list to involve ties and/or be incomplete. We show that, under the weakest notion of stability, the stable matchings need not be all of the same cardinality, and the decision problem related to finding a maximum cardinality stable matching is NP-complete, even if the ties occur in the preference lists of one sex only. This result has important implications for practical matching schemes such as the well-known National Resident Matching Program [9]. In the cases of the other two notions of stability, Irving [5] has described algorithms for testing whether a stable matching exists, and for constructing such a matching if one does exist, where a preference list is complete but may involve ties. We demonstrate how to extend these algorithms to the case where a preference list may be incomplete and/or involve ties.

Question: What is the stable marriage problem?


Multilevel single-phase inverter based on TNPC-type. A new single-phase 7-level pulse-width modulation (PWM) inverter using T-type Neutral-point-clamped (TNPC) celIs is presented to obtain high quality output voltage minimizing the number of switching devices. It includes two TNPC cells series-connected together and connected to with different voltage levels. To avoid inherent problems caused by voltage unbalance, modified Buck-boost converters are added at DC-link side to make DC voltage sources for the proposed inverter. Operational principles and the switching pattern are analyzed in this paper. To verify the performance of the proposed inverter, PSIM simulation and experimental results are also shown in this paper.

Question: What is the difference between a buck-boost converter and a PWM inverter?


External localization system for mobile robotics. We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

Question: What is the name of the algorithm?


A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics. Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common subgraph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.

Question: What is the block-and-bridge preserving subgraph isomorphism?


CONDENSATION—Conditional Density Propagation for Visual Tracking. The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Question: What is the problem of tracking curves in dense visual clutter?


Design considerations and performance evaluation of 1200 V, 100 a SiC MOSFET based converter for high power density application. Silicon Carbide (SiC) MOSFET is capable of achieving better efficiency, power density and reliability of power converters due to its low on-state resistance, high temperature operation capability and lower switching losses compared to silicon (Si) IGBT. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200 V, 100 A SiC MOSFET module and compares efficiency of a Two Level Voltage Source Converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200 V, 100 A SiC MOSFET based 2L-VSC including gate drive design, bus bar packaging and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20 kHz switching frequency with DC bus voltage at 800 V and the experimental results are presented.

Question: What is the voltage rating of the SiC MOSFET?


MiningZinc: A declarative framework for constraint-based mining. We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism. First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions. Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined. Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.

Question: What is a declarative framework for constraint-based data mining?


Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review. Site-specific crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. Even though the SSCM is very widely used in row crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various crop characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing OPEN ACCESS Remote Sensing 2010, 2 1974 blueberry in Georgia, U.S. The study also provides a list of spectral reflectance curves developed for some fruit and nut crops, blueberry, crowberry, redblush citrus, orange, prickly pear, and peach. The study also explains these curves in detail to help researchers choose the image platform, sensor, and spectrum wavelength for various fruit and nut crops SSC

Question: What is the difference between a fruit and a nut?


Computational approaches to fMRI analysis. Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.

Question: What is the difference between a machine learning and an algorithmic optimization?


Data science is science's second chance to get causal inference right: A classification of data science tasks. 1. Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 2. Harvard-MIT Division of Health Sciences and Technology, Boston, MA 3. Mongan Institute, Massachusetts General Hospital, Boston, MA 4. Department of Health Care Policy, Harvard Medical School, Boston, MA 5. Department of Neurology, Harvard Medical School, Partners MS Center, Brigham and Women's Hospital, Boston, MA 6. Biostatistics Center, Massachusetts General Hospital, Boston, MA

Question: What is the canadian military?


Functional imaging of human crossmodal identification and object recognition. The perception of objects is a cognitive function of prime importance. In everyday life, object perception benefits from the coordinated interplay of vision, audition, and touch. The different sensory modalities provide both complementary and redundant information about objects, which may improve recognition speed and accuracy in many circumstances. We review crossmodal studies of object recognition in humans that mainly employed functional magnetic resonance imaging (fMRI). These studies show that visual, tactile, and auditory information about objects can activate cortical association areas that were once believed to be modality-specific. Processing converges either in multisensory zones or via direct crossmodal interaction of modality-specific cortices without relay through multisensory regions. We integrate these findings with existing theories about semantic processing and propose a general mechanism for crossmodal object recognition: The recruitment and location of multisensory convergence zones varies depending on the information content and the dominant modality.

Question: What is the dominant modality?


Gender violence: transgender experiences with violence and discrimination.. There is a pervasive pattern of discrimination and prejudice against transgendered people within society. Both economic discrimination and experiencing violence could be the result of a larger social climate that severely sanctions people for not conforming to society's norms concerning gender; as such, both would be strongly associated with each other. Questionnaires were distributed to people either through events or through volunteers, and made available upon the World Wide Web. A sample of 402 cases was collected over the span of 12 months (April 1996-April 1997). We found that over half the people within this sample experienced some form of harassment or violence within their lifetime, with a quarter experiencing a violent incident. Further investigation found that experiencing economic discrimination because one is transgendered had the strongest association with experiencing a transgender related violent incident. Economic discrimination was related to transgendered people's experience with violence. Therefore, both hate crimes legislation and employment protections are needed for transgendered individuals.

Question: What is the largest transgender organization in the world?


Face recognition: A literature survey. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Question: What is the difference between still and video face recognition?


Path Reducing Watershed for the GPU. The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.

Question: What is a watershed transform?


An Empirical Study of Rich Subgroup Fairness for Machine Learning. Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal, Beygelzeimer, Dudik, Langford, and Wallach [ICML 2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.

Question: What is the VC dimension of a function?


NLAST: A natural language assistant for students. This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.

Question: What is a natural language interface?


Learning in Artificial Neural Networks: A Statistical Perspective. The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.

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


Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System. This paper analyzes the main challenges associated with noninvasive, continuous, wearable, and long-term breathing monitoring. The characteristics of an acoustic breathing signal from a miniature sensor are studied in the presence of sources of noise and interference artifacts that affect the signal. Based on these results, an algorithm has been devised to detect breathing. It is possible to implement the algorithm on a single integrated circuit, making it suitable for a miniature sensor device. The algorithm is tested in the presence of noise sources on five subjects and shows an average success rate of 91.3% (combined true positives and true negatives).

Question: What is the average success rate of the algorithm?


Ontology Development 101 : A Guide to Creating Your First Ontology. 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

Question: What is the difference between ontology and ontology development?


Explanation-Based Learning: A Problem Solving Perspective. This article outlines explanation-based learning (EBL) and its role in improving problem-solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the PRODIGY problem-solving system. PRODIGY'S EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem-solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. PRODIGY'S EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving. 'Present address: Artificial Intelligence Research Branch, NASA Ames Research Center, Sterling Federal Systems, Mail Stop 244-17, Moffett Field CA 94035. This research was sponsored in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract number F33615-87-C-1499, monitored by the Air Force Avionics Laboratory, in part by the Office of Naval Research under contracts N00014-84-K-0345 (N91) and N00014-86-K-0678-N123, in part by NASA under contract NCC 2-463, in part by the Army Research Institute under contract MDA903-85-C-0324, under subcontract 487650-25537 through the University of California, Irvine, and in part by small contributions from priv

Question: What is the difference between inductive and explanation-based learning?


Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search. This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and limitations and their area of application. The results of our experiments show a very strong relation between the nature of the data and the behavior of both systems. The Importance Score method is more efficient when dealing with little noise and small number of interacting features, while the genetic algorithms can provide a more robust solution at the expense of increased computational effort.

Question: What is the difference between greedy-like search and genetic algorithms?


Topic hierarchy construction for the organization of multi-source user generated contents. User generated contents (UGCs) carry a huge amount of high quality information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources by a topic hierarchy which is automatically generated and updated using the UGCs. We explore the unique characteristics of UGCs like blogs, cQAs, microblogs, etc., and introduce a novel scheme to combine them. We also propose a graph-based method to enable incremental update of the generated topic hierarchy. Using the hierarchy, users can easily obtain a comprehensive, in-depth and up-to-date picture of their topics of interests. The experiment results demonstrate how information from multiple heterogeneous sources improves the resultant topic hierarchies. It also shows that the proposed method achieves better F1 scores in hierarchy generation as compared to the state-of-the-art methods.

Question: How to construct a topic hierarchy for multi-source user generated contents?


A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

Question: What is a model of saliency-based visual attention for rapid scene analysis?


Enhanced differential super class-AB OTA. A fully differential Super Class AB Operational Transconductance Amplifier (OTA) is presented. To provide additional dynamic current boosting and increased gain-bandwidth product (GBW), not only adaptive biasing techniques has been applied at the differential input stage, but also Local Common Mode Feedback (LCMFB). Additionally, Quasi Floating Gate (QFG) transistors are employed, enhancing the performance of the amplifier. The OTA has been fabricated in a standard 0.5-μm CMOS process. Simulation results yield a slew rate improvement by factor 86 and a GBW enhancement by factor 16 when compared to the class A OTA while driving the same 70-pF load. Supply voltages are ±1-V and the value of the quiescent current is 10 μA. The overhead in terms of quiescent power, silicon area, and noise level is small.

Question: What is a differential super class-AB OTA?


Data Governance Framework for Big Data Implementation with a Case of Korea. Big Data governance requires a data governance that can satisfy the needs for corporate governance, IT governance, and ITA/EA. While the existing data governance focuses on the processing of structured data, Big Data governance needs to be established in consideration of a broad sense of Big Data services including unstructured data. To achieve the goals of Big Data, strategies need to be established together with goals that are aligned with the vision and objective of an organization. In addition to the preparation of the IT infrastructure, a proper preparation of the components is required to effectively implement the strategy for Big Data services. We propose the Big Data Governance Framework in this paper. The Big Data governance framework presents criteria different from existing criteria at the data quality level. It focuses on timely, reliable, meaningful, and sufficient data services, focusing on what data attributes should be achieved based on the data attributes of Big Data services. In addition to the quality level of Big Data, the personal information protection strategy and the data disclosure/accountability strategy are also needed to achieve goals and to prevent problems. This paper performed case analysis based on the Big Data Governance Framework with the National Pension Service of South Korea. Big Data services in the public sector are an inevitable choice to improve the quality of people's life. Big Data governance and its framework are the essential components for the realization of Big Data service.

Question: What is the data governance framework for big data implementation?


Optimization, Learning and Natural Algorithms.

Question: What is the difference between optimization, learning and natural algorithms?


Introduction Advances in Text Comprehension : Model , Process and Development. To a very large extent, children learn in and out of school from written text. Information Communications Technologies (ICT) offers many possibilities to facilitate learning by confronting children with multimodal texts. In order to be able to implement learning environments that optimally facilitate children's learning, insight is needed into the cognitive processes underlying text comprehension. In this light, the aim of this special issue is to report on new advances in text comprehension research in perspective of educational implications. Starting from recent theoretical frameworks on the cognitive processes underlying text comprehension, the online processing of text will be discussed in adults and in school children. Copyright # 2008 John Wiley & Sons, Ltd.

Question: What is the cognitive process underlying text comprehension?


Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition. Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional subword based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.

Question: How to train an A2W model?


Social Exchange in Organizations : Perceived Organizational Support , Leader-Member Exchange , and Employee Reciprocity. Social exchange (P. Blau, 1964) and the norm of reciprocity (A. W. Gouldner, 1960) have been used to explain the relationship of perceived organizational support and leader-member exchange with employee attitudes and behavior. Recent empirical research suggests that individuals engage in different reciprocation efforts depending on the exchange partner (e.g., B. L. McNeely & B. M. Meglino, 1994). The purpose of the present study was to further investigate these relationships by examining the relative contribution of indicators of employee-organization exchange and subordinate-supervisor exchange. Structural equation modeling was used to compare nested models. Results indicate that perceived organizational support is associated with organizational commitment, whereas leader-member exchange is associated with citizenship and in-role behavior.

Question: What is the purpose of the study?


Latent Variable Dialogue Models and their Diversity. We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the 'boring output' issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.

Question: How do I generate diverse outputs?


Contingencies of self-worth.. Research on self-esteem has focused almost exclusively on level of trait self-esteem to the neglect of other potentially more important aspects such as the contingencies on which self-esteem is based. Over a century ago, W. James (1890) argued that self-esteem rises and falls around its typical level in response to successes and failures in domains on which one has staked self-worth. We present a model of global self-esteem that builds on James' insights and emphasizes contingencies of self-worth. This model can help to (a) point the way to understanding how self-esteem is implicated in affect, cognition, and self-regulation of behavior; (b) suggest how and when self-esteem is implicated in social problems; (c) resolve debates about the nature and functioning of self-esteem; (d) resolve paradoxes in related literatures, such as why people who are stigmatized do not necessarily have low self-esteem and why self-esteem does not decline with age; and (e) suggest how self-esteem is causally related to depression. In addition, this perspective raises questions about how contingencies of self-worth are acquired and how they change, whether they are primarily a resource or a vulnerability, and whether some people have noncontingent self-esteem.

Question: What is the difference between self-esteem and self-worth?


Issues and challenges of data security in a cloud computing environment. Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.

Question: What are the security issues associated with cloud computing?


Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage. Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports. We observe that when the type set spans several domains, detection of entity mention becomes a limitation for supervised learning models. The primary reason being lack of dataset where entity boundaries are properly annotated while covering a large spectrum of entity types. Our work directly addresses this issue. We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task. HAnDS framework exploits the high interlink among Wikipedia and Freebase in a pipelined manner, reducing annotation errors introduced by naively using distant supervision approach. Using HAnDS framework, we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy. Our extensive empirical experimentation warrants the quality of the generated datasets. Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.

Question: How to automatically construct a quality dataset suitable for the fine-grained entity recognition task?


Discovering actionable patterns in event data. Applications such as those for systems management and intrusion detection employ an automated real-time operation system in which sensor data are collected and processed in real time. Although such a system effectively reduces the need for operation staff, it requires constructing and maintaining correlation rules. Currently, rule construction requires experts to identify problem patterns, a process that is timeconsuming and error-prone. In this paper, we propose reducing this burden by mining historical data that are readily available. Specifically, we first present efficient algorithms to mine three types of important patterns from historical event data: event bursts, periodic patterns, and mutually dependent patterns. We then discuss a framework for efficiently mining events that have multiple attributes. Last, we present Event Correlation Constructor—a tool that validates and extends correlation knowledge.

Question: How to mine patterns from historical data?


Managing Portfolios of Development Projects in a Complex Environment How the UN assign priorities to Programs at the Country.

Question: How to manage portfolios of development projects in a complex environment?


A Pattern Language - Towns, Buildings, Construction. When there are many people who don't need to expect something more than the benefits to take, we will suggest you to have willing to reach all benefits. Be sure and surely do to take this a pattern language towns buildings construction that gives the best reasons to read. When you really need to get the reason why, this a pattern language towns buildings construction book will probably make you feel curious.

Question: What is a pattern language?


Do RNNs learn human-like abstract word order preferences?. RNN language models have achieved state-ofthe-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs' performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations. The best-performing models for many natural language processing tasks in recent years have been recurrent neural networks (RNNs) (Elman, 1990; Sutskever et al., 2014; Goldberg, 2017), but the black-box nature of these models makes it hard to know exactly what generalizations they have learned about their linguistic input: Have they learned generalizations stated over hierarchical structures, or only dependencies among relatively local groups of words (Linzen et al., 2016; Gulordava et al., 2018; Futrell et al., 2018)? Do they represent structures analogous to syntactic dependency trees (Williams et al., 2018), and can they represent complex relationships such as filler–gap dependencies (Chowdhury and Zamparelli, 2018; Wilcox et al., 2018)? In order to make progress with RNNs, it is crucial to determine what RNNs actually learn given currently standard practices; then we can des

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


Generating Natural Questions About an Image. There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks focus on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image often address abstract events that the objects evoke. In this paper, we introduce the novel task of 'Visual Question Generation' (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, providing different and more abstract training data than the stateof-the-art captioning systems have used thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions given various images, there is still a wide gap with human performance. Our proposed task offers a new challenge to the community which we hope can spur further interest in exploring deeper connections between vision & language.

Question: What is the task of generating natural questions about an image?


Sensitivity enhancement of MEMS fluid flow rate and flow direction sensor. MEMS based flow rate sensor and flow direction sensor is a crucial to the determination of exact fluid flow path. Conventional rectangular cantilever beam detects based on the surface strain of the beam with respect to the mass flow. Different structured beam will greatly affect the sensitivity. In this paper different geometries of cantilever beam such as rectangular, inverted trapezoidal, triangular are designed and simulated using the finite element software INTELLISUITE 8.7. The simulation results show that the inverted trapezoidal geometries having the more deflection at same stress of the conventional beam and thus increase the sensitivity by approx. 1.45 times.

Question: What is the sensitivity of the MEMS based flow rate and flow direction sensor?


Mid-face Rejuvenation Mr Dalvi Humzah and. With age, the mid-face changes due to a combination of remodelling of the maxilla and pyriform aperture, volumetric and positional changes of the superficial and deep mid-facial fat pads, and changes in skin tone and texture. It is widely accepted that these changes significantly influence many of the visual changes in the lower face. These may be effectively treated with a variety of injectable techniques to rejuvenate the mid-face. The current literature concerning the pertinent anatomy for mid-facial rejuvenation is therefore discussed within this article. Introduction In recent years, the way we analyse and plan the non-surgical approach to the mid-face has changed dramatically. The focus has shifted away from treating visible lines/folds as they present, and is now centred on treating the age-related changes at the root cause. The restoration of a dynamic and harmonious volume distribution is a key factor in non-surgical rejuvenation of the mid-face. There is a growing body of literature demonstrating the multifactorial nature of facial ageing. These indicate that facial analysis and subsequent rejuvenation should initially address such anatomical changes. An understanding of the anatomical changes associated with ageing is required to formulate effective strategies to rejuvenate the ageing mid-face.

Question: What is the multifactorial nature of facial ageing?


Efficacy and safety of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency: a 24 month study. BACKGROUND Hyaluronic acid (HA) fillers are an established intervention for correcting facial volume deficiency. Few studies have evaluated treatment outcomes for longer than 6 months. The purpose of this study was to determine the durability of an HA filler in the correction of midface volume deficiency over 24 months, as independently evaluated by physician investigators and subjects.   METHODS Subjects received treatment with Juvéderm(™) Voluma(™) to the malar area, based on the investigators' determination of baseline severity and aesthetic goals. The treatment was administered in one or two sessions over an initial 4-week period. Supplementary treatment was permissible at week 78, based on protocol-defined criteria. A clinically meaningful response was predefined as at least a one-point improvement on the MidFace Volume Deficit Scale (MFVDS) and on the Global Aesthetic Improvement Scale (GAIS).   RESULTS Of the 103 subjects enrolled, 84% had moderate or significant volume deficiency at baseline. At the first post-treatment evaluation (week 8), 96% were documented to be MFVDS responders, with 98% and 100% graded as GAIS responders when assessed by the subjects and investigators, respectively. At week 78, 81.7% of subjects were still MFVDS responders, with 73.2% and 78.1% being GAIS responders, respectively. Seventy-two subjects completed the 24-month study, of whom 45 did not receive supplementary Voluma(™) at week 78. Forty-three of the 45 (95.6%) subjects were MFVDS responders, with 82.2% and 91.1% being GAIS responders, respectively. At end of the study, 66/72 subjects were either satisfied or very satisfied with Voluma(™), with 70/72 indicating that they would recommend the product to others. Adverse events were transient and infrequent, with injection site bruising and swelling being the most commonly reported.   CONCLUSION Volu

Question: What is the durability of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency?


Multichannel-Sniffing-System for Real-World Analysing of Wi-Fi-Packets. Wireless technologies like Wi-Fi send their data using multiple channels. To analyze an environment and all Wi-Fi packets inside, a sniffing system is needed, which can sniff on all used channels of the wireless technology at the same time. This allows catching most packets on each channel. In this paper, a way to build up a multi-channel-sniffing-system (MCSS) is described. The test system uses several single board computers (SBC) with an external Wi-Fi adapter (USB), 19 SBCs are sniffing nodes (SFN) and one SBC as sending node (SN). The sniffing SBCs are placed in a cycle around the sender so that every node has the same chance to receive the simulated packets from the SN. For the control of all 20 SBCs, a self-developed software is used, which connects from the host to the clients and is used for configuring the experiments. The configuration is sent to each client and will initiate their start, so that their times are also synchronized, for this all clients are synchronised using a time

Question: How to build a multi-channel sniffing system?


Surpassing Humans in Boundary Detection using Deep Learning. In this work we show that Deep Convolutional Neural Networks can outperform humans on the task of boundary detection, as measured on the standard Berkeley Segmentation Dataset. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art, from an optimal dataset scale F-measure of 0.780 to 0.808 while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the higher level tasks of object proposal generation and semantic segmentation for both tasks our detector yields clear improvements over state-of-the-art systems.

Question: How does deep learning improve boundary detection?


Methods for ordinal peer grading. Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.

Question: How to infer student grades from ordinal peer feedback?


Locked-in syndrome : a challenge for embodied cognitive science. Embodied approaches in cognitive science hold that the body is crucial for cognition. What this claim amounts to, however, still remains unclear. This paper contributes to its clarification by confronting three ways of understanding embodiment—the sensorimotor approach, extended cognition and enactivism—with Lockedin syndrome (LIS). LIS is a case of severe global paralysis in which patients are unable to move and yet largely remain cognitively intact. We propose that LIS poses a challenge to embodied approaches to cognition requiring them to make explicit the notion of embodiment they defend and its role for cognition. We argue that the sensorimotor and the extended functionalist approaches either fall short of accounting for cognition in LIS from an embodied perspective or do it too broadly by relegating the body only to a historical role. Enactivism conceives of the body as autonomous system and of cognition as sense-making. From this perspective embodiment is not equated with bodily movement but with forms of agency that do not disappear with body paralysis. Enactivism offers a clarifying perspective on embodiment and thus currently appears to be the framework in embodied cognition best suited to address the challenge posed by LIS.

Question: What is the role of the body in cognition?


Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance. Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task.

Question: How to reduce the granularity of the wordnet sense inventory?


Marker-based tracking with unmanned aerial vehicles. With the availability of low-cost micro aerial vehicles (MAVs), unmanned aerial vehicles (UAVs) quickly gain popularity and application potential. This requires techniques that can be understood by non-experts and flexibly applied for rapid prototyping. Visual tracking is an essential task with many applications, such as autonomous navigation and scene acquisition. While marker-less methods emerge, marker-based methods still have major advantages including simplicity, robustness, accuracy and performance. In practice, however, multi-marker setups introduce complexity and calibration efforts that can void the advantages. This work proposes a solution for practical, robust and easy-to-use marker-based tracking with independent compound targets. We introduce two novel target designs and describe pose estimation, noise removal and geometric transformations. The concepts are implemented in a tracking library for the Parrot AR. Drone 2.0. We explain its video access and camera calibration, and provide a first set of intrinsic parameters, jointly estimated from 14 units with high accuracy and low variance. The library is applied in a one-day contest on automatic visual navigation of UAVs, where students without technical background and programming skills achieved learning by experience and rapid development. This shows the effectiveness of combining capability with simplicity, and provides a case study on robotics in interdisciplinary education.

Question: How to track a UAV with a camera?


Efficient parallel translating embedding for knowledge graphs. Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

Question: What is the time complexity of knowledge graph embedding?


A new 6-DOF parallel robot with simple kinematic model. This paper presents a novel six-legged parallel robot, the kinematic model of which is simpler than that of the simplest six-axis serial robot. The new robot is the 6-DOF extension of the Cartesian parallel robot. It consists of three pairs of base-mounted prismatic actuators, the directions in each pair parallel to one of the axes of a Cartesian coordinate system. In each of the six legs, there are also two passive revolute joints, the axes of which are parallel to the direction of the prismatic joint. Finally, each leg is attached to the mobile platform via a spherical joint. The direct kinematics of the novel parallel robot can be solved easily by partitioning the orientation and the position of the mobile platform. There are eight distinct solutions, which can be found directly by solving a linear system and alternating the signs of three radicals. This parallel robot has a large workspace and is suitable for machining or rapid prototyping, as detailed in this paper.

Question: What is the direct kinematics of the novel parallel robot?


Credit scoring using support vector machines with direct search for parameters selection. Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters' setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.

Question: How to optimize the parameters of a credit scoring model?


Making the Best of Mifare Classic Update. What would you do if you would be instructed to make a secure application built on the Mifare Classic? Arguably, due to the vulnerabilities shown in [5] and hinted at on [6], this is rather difficult and it may be easier to use to another chip. This document explores what the best is you can get, if the only option is Mifare Classic. We propose countermeasures against state restoration and against cloning. The effectiveness of these countermeasures depends on the absense of other vulnerabilities of

Question: What is the best option for a secure application?


Chapter 1 Feature Engineering for Twitter-based Applications. 1.

Question: What is feature engineering?


Wideband and Low Sidelobe Slot Antenna Fed by Series-Fed Printed Array. A combination of slots and series-fed patch antenna array is introduced leading to an array antenna with wide bandwidth, low sidelobe level (SLL), and high front-to-back ratio (F/B). Three structures are analyzed. The first, the reference structure, is the conventional series-fed microstrip antenna array that operates at 16.26 GHz with a bandwidth of 3%, SLL of -24 dB, and F/B of 23 dB. The second is similar to the reference structure, but a large slot that covers the patch array is removed from the ground plane. This results in a wide operating bandwidth of over 72% for , with a 3 dB gain bandwidth of over 15.9%. The SLL of this structure is improved to more than at 16.26 GHz, and stable SLL of over 15.5-16.4 GHz band is also exhibited. This structure has a bi-directional radiation pattern. To make the pattern uni-directional and increase the F/B, a third structure is analyzed. In this structure, rather than one single large slot, an array of slots is used and a reflector is placed above the series-fed patch array. This increases the F/B to 40 dB. The simulated and measured results are presented and discussed.

Question: What is the difference between a slot antenna and a patch antenna?


Hidden semi-Markov models. Article history: Received 14 April 2009 Available online 17 November 2009

Question: What is a hidden semi-Markov model?


A wearable computer system with augmented reality to support terrestrial navigation. To date augmented realities are typically operated in only a small defined area, in the order of a large room. This paper reports on our investigation into expanding augmented realities to outdoor environments. The project entails providing visual navigation aids to users. A wearable computer system with a see-through display, digital compass, and a differential GPS are used to provide visual cues while performing a standard orienteering task. This paper reports the outcomes of a set of trials using an off the shelf wearable computer, equipped with a custom built navigation software package, "map-in-the-hat".

Question: What is a wearable computer?


SQLEM: Fast Clustering in SQL using the EM Algorithm. Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.

Question: How to implement EM in SQL?


A Type-Based Compiler for Standard ML. Compile-time type information should be valuable in efficient compilation of statically typed functional languages such as Standard ML. But how should type-directed compilation work in real compilers, and how much performance gain will type-based optimizations yield? In order to support more efficient data representations and gain more experience about type-directed compilation, we have implemented a new type-based middle end and back end for the Standard ML of New Jersey compiler. We describe the basic design of the new compiler, identify a number of practical issues, and then compare the performance of our new compiler with the old non-type-based compiler. Our measurement shows that a combination of several simple type-based optimizations reduces heap allocation by 36%; and improves the already-efficient code generated by the old non-type-based compiler by about 19% on a DECstation 500.

Question: What is a type-based compiler?


Learning as search optimization: approximate large margin methods for structured prediction. Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.

Question: What is the approximate large margin methods for structured prediction?


Contrast-Enhanced Black and White Images. This paper investigates contrast enhancement as an approach to tone reduction, aiming to convert a photograph to black and white. Using a filter-based approach to strengthen contrast, we avoid making a hard decision about how to assign tones to segmented regions. Our method is inspired by sticks filtering, used to enhance medical images but not previously used in non-photorealistic rendering. We amplify contrast of pixels along the direction of greatest local difference from the mean, strengthening even weak features if they are most prominent. A final thresholding step converts the contrast-enhanced image to black and white. Local smoothing and contrast enhancement balances abstraction and structure preservation; the main advantage of our method is its faithful depiction of image detail. Our method can create a set of effects: line drawing, hatching, and black and white, all having superior details to previous black and white methods.

Question: How to convert a photograph to black and white?


Emotion recognition with multimodal features and temporal models. This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.

Question: What is the 2017 Emotion Recognition in the Wild (EmotiW) Challenge?


Decentralized Reactive Power Sharing and Frequency Restoration in Islanded Microgrid. P-f and Q-V droop methods are the most common decentralized control methods in islanded microgrid. Although with the P-f droop method, an accurate active power sharing can be achieved among distributed energy resources (DERs), by Q-V droop, the reactive power sharing among DERs often deteriorates due to its highly dependence on the power line impedances and the local load. Variation of frequency and voltage by load changes is another challenge in droop control method. In this paper, a new autonomous control method is proposed to share reactive power among DERs accurately and restore frequency of a microgrid. The proposed method does not require any communication link and so maintains reliability and simplicity of the network. The synchronizing among DERs is obtained by the load change detection, which is accomplished by wavelet transform. The method operation principle is explained and analyzed. Simulation results are presented to validate the effectiveness of the proposed method.

Question: What is the difference between the P-f and Q-V droop methods?


Intersection-priority based optimal RSU allocation for VANET. Roadside Unit (RSU) is an essential unit in a vehicular ad-hoc network (VANET) for collecting and analyzing traffic data given from smart vehicles. Furthermore, RSUs can take part in controlling traffic flow for vehicle's secure driving by broadcasting locally analyzed data, forwarding some important messages, and communicating with other RSUs, and soon. In order to maximize the availability of RSUs in the VANET, RSUs need to be fully distributed over an entire area. Thus, RSUs can make the best use of all traffic data gathered from every intersection. In this paper, we provide intersection-priority based RSU placement methods to find the optimal number and positions of RSUs for the full distribution providing with a maximal connectivity between RSUs while minimizing RSU setup costs. We propose three optimal algorithms: greedy, dynamic and hybrid algorithms. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu/Jongrogu, YongsanGu, and GangnamGu in Seoul, each of which has a characteristic road style different than each other. We analyze how our algorithms work in such different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.

Question: How to find the optimal number and positions of RSUs for the full distribution providing with a maximal connectivity between RSUs while minimizing RSU setup costs?


The nature of cyberbullying, and strategies for prevention. 0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.05.024 ⇑ Corresponding author. Address: Unit for School an University of London, UK. Tel.: +44 (0)207 919 7870; E-mail address: r.slonje@gmail.com (R. Slonje). Cyberbullying has been identified as an important problem amongst youth in the last decade. This paper reviews some recent findings and discusses general concepts within the area. The review covers definitional issues such as repetition and power imbalance, types of cyberbullying, age and gender differences, overlap with traditional bullying and sequence of events, differences between cyberbullying and traditional bullying, motives for and impact of cyber victimization, coping strategies, and prevention/intervention possibilities. These issues will be illustrated by reference to recent and current literature, and also by in-depth interviews with nine Swedish students aged 13–15 years, who had some first-hand experience of one or more cyberbullying episodes. We conclude by discussing the evidence for different coping, intervention and prevention strategies. 2012 Elsevier Ltd. All rights reserved.

Question: What is cyberbullying?


Linearizability: A Correctness Condition for Concurrent Objects. A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent objects using known techniques from the sequential domain. Linearizability provides the illusion that each operation applied by concurrent processes takes effect instantaneously at some point between its invocation and its response, implying that the meaning of a concurrent object's operations can be given by pre- and post-conditions. This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.

Question: What is linearizability?


Facial Expression Recognition via a Boosted Deep Belief Network. A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.

Question: What is a deep belief network?


Multi-scale Internet traffic forecasting using neural networks and time series methods. This article presents three methods to forecast accurately the amount of traffic in TCP/IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using realworld data from two large Internet service providers. In addition, different time scales (five minutes, one hour and one day) and distinct forecasting lookaheads were analyzed. The experiments with the neural ensemble achieved the best results for five minutes and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.

Question: What is the best method to forecast traffic?


TANDEM-bottleneck feature combination using hierarchical Deep Neural Networks. To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone recordings of the Cantonese IARPA Babel corpus using both standard MFCCs and Gabor as input features.

Question: What is the difference between TANDEM and bottleneck DNN?


Evaluating the Effectiveness of Serious Games for Cultural Awareness: The Icura User Study. There is an increasing awareness about the potential of serious games for education and training in many disciplines. However, research still witnesses a lack of methodologies, guidelines and best practices on how to develop effective serious games and how to integrate them in the actual learning and training processes. This process of integration heavily depends on providing and spreading evidence of the effectiveness of serious games This paper reports a user study to evaluate the effectiveness of Icura, a serious game about Japanese culture and etiquette. The evaluation methodology extends the set of instruments used in previous studies by evaluating the effects of the game on raising awareness, by avoiding the selective attention bias and by assessing the medium-term retention. . With this research we aim to provide a handy toolkit for evaluating the effectiveness a serious games for cultural awareness and heritage.

Question: How to evaluate the effectiveness of a serious game for cultural awareness?


Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization. Communication is now a standard tool in the central bank's monetary policy toolkit. Theoretically, communication provides the central bank an opportunity to guide public expectations, and it has been shown empirically that central bank communication can lead to financial market fluctuations. However, there has been little research into which dimensions or topics of information are most important in causing these fluctuations. We develop a semi-automatic methodology that summarizes the FOMC statements into its main themes, automatically selects the best model based on coherency, and assesses whether there is a significant impact of these themes on the shape of the U.S Treasury yield curve using topic modeling methods from the machine learning literature. Our findings suggest that the FOMC statements can be decomposed into three topics: (i) information related to the economic conditions and the mandates, (ii) information related to monetary policy tools and intermediate targets, and (iii) information related to financial markets and the financial crisis. We find that statements are most influential during the financial crisis and the effects are mostly present in the curvature of the yield curve through information related to the financial theme.

Question: What is the central bank communication?


Program Synthesis Using Natural Language. Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks.  We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.

Question: How to create a program synthesizer?


Deep Convolutional Neural Networks for Fire Detection in Images. Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balanced benchmark dataset provided in this paper. We therefore propose to use even deeper Convolutional Neural Networks for fire detection in images, and enhancing these with fine tuning based on a fully connected layer. We use two pretrained state-of-the-art Deep CNNs, VGG16 and Resnet50, to develop our fire detection system. The Deep CNNs are tested on our imbalanced dataset, which we have assembled to replicate real world scenarios. It includes images that are particularly difficult to classify and that are deliberately unbalanced by including significantly more non-fire images than fire images. The dataset has been made available online. Our results show that adding fully connected layers for fine tuning indeed does increase accuracy, however, this also increases training time. Overall, we found that our deeper CNNs give good performance on a more challenging dataset, with Resnet50 slightly outperforming VGG16. These results may thus lead to more successful fire detection systems in practice.

Question: How deep is deep?


3D distance fields: a survey of techniques and applications. A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?.

Question: What is the complexity and runtime of such methods?


Feature Mining for Localised Crowd Counting. This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.

Question: How to count people in a crowd?