A New Framework for Computer Science and Engineering. T raditionally, computing studies has two partitions—science and engineering—that are separated by a line roughly at the computer architecture level. Above the line is computer science and below is computer engineering. Each of these two disciplines then breaks into subspecialties, such as the decomposition of computer science into theory, systems, and AI. Such an approach succeeds in dividing the field into affinity groups, but it tends to isolate its respective parts and the systems that come from them. This isolation, in turn, makes it difficult to incorporate either an integrative systems-oriented perspective or the interdisciplinary work that is rapidly becoming critical to computing studies' future.1 A more effective organization for computer science and engineering (CS&E)—and one that helps point the way toward its promising future—requires an intrinsically interdisciplinary framework that combines academic and systems-oriented computing perspectives. My colleagues and I have been developing such a framework, which is already changing key segments of CS&E at the University of Southern California (USC). The framework reaggregates computer science and computer engineering and then repartitions the resulting single field into analysis and synthesis components.2-4 Analysis, the more academic of the two, focuses on the nature of computing and the relationships between computer science and other major scientific domains. The synthesis component builds up computer engineering as a more pragmatic systems-oriented activity through a hierarchy of related layers, each of which covers important interdisciplinary issues. The framework is based on the notion that science is foremost about dissecting and understanding, and engineering is mostly about envisioning and building. True, scientists also envision and build—integrated models, for example—but such activities are directed mostly at dissecting and understanding som

Question: What is the difference between science and engineering?


Strengths and Weaknesses of Quantum Computing. Recently a great deal of attention has focused on quantum computation following a sequence of results [4, 16, 15] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor's result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class NP ∩ co–NP cannot be solved on a quantum Turing machine in time o(2n/3). The former bound is tight since recent work of Grover [13] shows how to accept the class NP relative to any oracle on a quantum computer in time O(2n/2). To appear in SIAM Journal on Computing (special issue on quantum computing) ∗ IBM T. J. Watson Research Laboratory, Yorktown Heights, New York, NY 10598, USA. email: bennetc@watson.ibm.com. † 1 Microsoft Way, Redmond, WA 98052 – 6399, USA. email: ethanb@microsoft.com. ‡ Supported in part by Canada's nserc and Québec's fcar. §Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. ¶ Supported by NSF Grant No. CCR-9310214. Computer Science Division, University of California, Berkeley, CA 94720, USA. email: vazirani@cs.berkeley.edu.

Question: What is the difference between quantum and classical computing?


Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum. Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.

Question: How to measure the health of a helicopter drive shaft?


Novel air blowing control for balancing a unicycle robot. This paper presents the implementation of a novel control method of using air for balancing the roll angle of a unicycle robot. The unicycle robot is designed and built. The roll angle of the unicycle robot is controlled by blowing air while the pitch angle is also controlled by DC motors. Successful balancing performances are demonstrated.

Question: How to control the roll angle of a unicycle robot?


Topographical and temporal diversity of the human skin microbiome.. Human skin is a large, heterogeneous organ that protects the body from pathogens while sustaining microorganisms that influence human health and disease. Our analysis of 16S ribosomal RNA gene sequences obtained from 20 distinct skin sites of healthy humans revealed that physiologically comparable sites harbor similar bacterial communities. The complexity and stability of the microbial community are dependent on the specific characteristics of the skin site. This topographical and temporal survey provides a baseline for studies that examine the role of bacterial communities in disease states and the microbial interdependencies required to maintain healthy skin.

Question: What is the topographical and temporal diversity of the human skin microbiome?


Radar interferometry offers new insights into threats to the Angkor site. The conservation of World Heritage is critical to the cultural and social sustainability of regions and nations. Risk monitoring and preventive diagnosis of threats to heritage sites in any given ecosystem are a complex and challenging task. Taking advantage of the performance of Earth Observation technologies, we measured the impacts of hitherto imperceptible and poorly understood factors of groundwater and temperature variations on the monuments in the Angkor World Heritage site (400 km2). We developed a two-scale synthetic aperture radar interferometry (InSAR) approach. We describe spatial-temporal displacements (at millimeter-level accuracy), as measured by high-resolution TerraSAR/TanDEM-X satellite images, to provide a new solution to resolve the current controversy surrounding the potential structural collapse of monuments in Angkor. Multidisciplinary analysis in conjunction with a deterioration kinetics model offers new insights into the causes that trigger the potential decline of Angkor monuments. Our results show that pumping groundwater for residential and touristic establishments did not threaten the sustainability of monuments during 2011 to 2013; however, seasonal variations of the groundwater table and the thermodynamics of stone materials are factors that could trigger and/or aggravate the deterioration of monuments. These factors amplify known impacts of chemical weathering and biological alteration of temple materials. The InSAR solution reported in this study could have implications for monitoring and sustainable conservation of monuments in World Heritage sites elsewhere.

Question: What is the difference between a World Heritage site and a World Heritage site?


Finding Similar Files in a Large File System. We present a tool, called sif, for finding all similar files in a large file system. Files are considered similar if they have significant number of common pieces, even if they are very different otherwise. For example, one file may be contained, possibly with some changes, in another file, or a file may be a reorganization of another file. The running time for finding all groups of similar files, even for as little as 25% similarity, is on the order of 500MB to 1GB an hour. The amount of similarity and several other customized parameters can be determined by the user at a post-processing stage, which is very fast. Sif can also be used to very quickly identify all similar files to a query file using a preprocessed index. Application of sif can be found in file management, information collecting (to remove duplicates), program reuse, file synchronization, data compression, and maybe even plagiarism detection.

Question: How to find similar files in a large file system?


Trajectory Outlier Detection: A Partition-and-Detect Framework. Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.

Question: How to detect outliers in trajectory data?


Why women use makeup: implication of psychological traits in makeup functions.. Makeup acts and stimulates three of our senses: touch (which encompasses all sensations from the body surface), smell (fragrance), and sight (the process of becoming and looking beautiful). The positive stimulation of these senses by makeup can induce sensory as well as psychological pleasure. In order to understand the relationship of women to their makeup, we interviewed different groups of women on their quality of life and makeup habits. Then, through four standard well-validated psychometric self-questionnaires, we examined the possible relation between the need to make up oneself and specific psychological features. Our first results clearly showed that makeup could support two opposite "up" functions, i.e., "camouflage" vs "seduction." Concerning their psychological profiles, results showed that women of the functional class "camouflage" are more anxious, defensive, and emotionally unstable compared to those of the functional class "seduction," who appear to be more sociable, assertive, and extroverted. Further analyses revealed a division of the two classes into subclasses of volunteers with opposed personality and psychological profiles. This new classification allowed us to define more precisely the relations existing within the subjective experience of women during the makeup process. In conclusion, our study revealed that beyond the simple application of colorful products on the face, makeup has two major functional implications depending on specific psychological profiles of women.

Question: What is the difference between the functional classes "seduction" and "camouflage"?


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Question: What is the difference between the publisher and the publisher's agent?


SVM based forest fire detection using static and dynamic features. A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

Question: What is the difference between static and dynamic features?


Cost-aware depth map estimation for Lytro camera. Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.

Question: How to estimate depth map for light field camera?


Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. It responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance; demand-driven supply chain services; and broad-based workforce involvement. ITenabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.

Question: What is smart manufacturing?


Helicopter induced propeller injuries.. Following routine maintenance a military helicopter was returned to service. Due to negligence some hoses had not been connected correctly. The wrongly connected hoses resulted in a malfunction, causing the rotor blades of the helicopter to smash through the windows of the helicopter cockpit. Both the pilot and the co-pilot were hit by the rotor blades and decapitated. Other passengers in the helicopter were injured. Due to vibrations the helicopter fell to the ground and its tail partly broke away (Figs. 1, 2). The bodies and body parts of the pilot and co-pilot were found on the ground outside the helicopter cockpit.

Question: What is the cause of the accident?


Using virtual data effects to stabilize pilot run neural network modeling. Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive, Artificial Neural Networks are widely utilized to extract management knowledge from acquired data for its non-linear properties; however, getting as large a number of training data as needed is the fundamental assumption. This is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize virtual sample generation techniques and the corresponding data effects to stabilize the prediction model. This research derives from using extreme value theory to estimate the domain range of a small data set, which is used for virtual sample production to fill the information gaps of sparse data. Further, for the virtual samples, a fuzzy-based data effect calculation system is developed to determine the comprehensive importance of each datum. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.

Question: How to use virtual data effects to stabilize pilot run neural network modeling.


FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining. This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

Question: What is the name of the dataset?


Jet lag: trends and coping strategies. The number of travellers undertaking long-distance flights has continued to increase. Such flights are associated with travel fatigue and jet lag, the symptoms of which are considered here, along with their similarities, differences, and causes. Difficulties with jet lag because of sleep loss and decreased performance are emphasised. Since jet lag is caused mainly by inappropriate timing of the body clock in the new time zone, the pertinent properties of the body clock are outlined, with a description of how the body clock can be adjusted. The methods, both pharmacological and behavioural, that have been used to alleviate the negative results of time-zone transitions, are reviewed. The results form the rationale for advice to travellers flying in different directions and crossing several time zones. Finally, there is an account of the main problems that remain unresolved.

Question: What is the difference between jet lag and travel fatigue?


C-Store: A Column-oriented DBMS. This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

Question: What is a column-oriented DBMS?


A primer on spatial modeling and analysis in wireless networks. The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.

Question: What is the objective of this article?


Embree: a kernel framework for efficient CPU ray tracing. We describe Embree, an open source ray tracing framework for x86 CPUs. Embree is explicitly designed to achieve high performance in professional rendering environments in which complex geometry and incoherent ray distributions are common. Embree consists of a set of low-level kernels that maximize utilization of modern CPU architectures, and an API which enables these kernels to be used in existing renderers with minimal programmer effort. In this paper, we describe the design goals and software architecture of Embree, and show that for secondary rays in particular, the performance of Embree is competitive with (and often higher than) existing state-of-the-art methods on CPUs and GPUs.

Question: What is a kernel framework for efficient CPU ray tracing?


SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents. We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.

Question: What is a recurrent neural network?


A MINI UNMANNED AERIAL VEHICLE ( UAV ) : SYSTEM OVERVIEW AND IMAGE ACQUISITION. In the last years UAV (Unmanned Aerial Vehicle)-systems became relevant for applications in precision farming and in infrastructure maintenance, like road maintenance and dam surveillance. This paper gives an overview about UAV (Unmanned Aerial Vehicle) systems and their application for photogrammetric recording and documentation of cultural heritage. First the historical development of UAV systems and the definition of UAV-helicopters will be given. The advantages of a photogrammetric system on-board a model helicopter will be briefly discussed and compared to standard aerial and terrestrial photogrammetry. UAVs are mostly low cost systems and flexible and therefore a suitable alternative solution compared to other mobile mapping systems. A mini UAV-system was used for photogrammetric image data acquisition near Palpa in Peru. A settlement from the 13 century AD, which was presumably used as a mine, was flown with a model helicopter. Based on the image data, an accurate 3D-model will be generated in the future. With an orthophoto and a DEM derived from aerial images in a scale of 1:7 000, a flight planning was build up. The determined flying positions were implemented in the flight control system. Thus, the helicopter is able to fly to predefined pathpoints automatically. Tests in Switzerland and the flights in Pinchango Alto showed that using the built-in GPS/INSand stabilization units of the flight control system, predefined positions could be reached exactly to acquire the images. The predicted strip crossings and flying height were kept accurately in the autonomous flying mode.

Question: What is a UAV?


The cognitive control of emotion. The capacity to control emotion is important for human adaptation. Questions about the neural bases of emotion regulation have recently taken on new importance, as functional imaging studies in humans have permitted direct investigation of control strategies that draw upon higher cognitive processes difficult to study in nonhumans. Such studies have examined (1) controlling attention to, and (2) cognitively changing the meaning of, emotionally evocative stimuli. These two forms of emotion regulation depend upon interactions between prefrontal and cingulate control systems and cortical and subcortical emotion-generative systems. Taken together, the results suggest a functional architecture for the cognitive control of emotion that dovetails with findings from other human and nonhuman research on emotion.

Question: What is the function of the prefrontal cortex?


The self to other model of empathy: Providing a new framework for understanding empathy impairments in psychopathy, autism, and alexithymia. Despite increasing empirical and theoretical work on empathy, particularly on the content of empathic representations, there is a relative lack of consensus regarding the information processing necessary for empathy to occur. Here we attempt to delineate a mechanistic cognitive model of empathy in order to provide a framework within which neuroimaging work on empathy can be located, and which may be used in order to understand various disorders characterised by atypical levels of empathy. To this end data from individuals with psychopathy, autism, and alexithymia inform the model, and the model is used to provide a unifying framework for any empathy impairments seen in these disorders. The model adopts a developmental framework and tries to address the four difficult questions of empathy: How do we know what another is feeling? What is the role of theory of mind in empathy? How does the state of another cause a corresponding state in the self? How do we represent another's emotion once emotional contagion has taken place?

Question: What is the role of theory of mind in empathy?


2 Conditional Saliency 2 . 1 Lossy Coding. By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency.

Question: What is the conditional saliency?


Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks. In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.

Question: What is a wafer bin map?


Ontologies in E-Learning: Review of the Literature. We have witnessed a great interest in ontologies as an emerging technology within the Semantic Web. Its foundation that was laid in the last decade paved the way for the development of ontologies and systems that use ontologies in various domains, including the E-Learning domain. In this article, we survey key contributions related to the development and use of ontologies in the domain of E-Learning systems. We provide a framework for classification of the literature that is useful for the community of practice in categorizing work in this field and for determining possible research lines. We also discuss future trends in this area.

Question: What is the ontology in e-learning?


Number : AUTCON-D16-00058 R 1 Title : BIM for Infrastructure : An Overall Review and Constructor Perspective. 6 The subject of Building Information Modelling (BIM) has become a central topic to the improvement of the AECOO 7 (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the 8 concept is being expanded into domains it was not originally conceived to address. Transitioning BIM into the 9 domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. 10 Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its 11 use from the constructor perspective to review and analyse the current industry positioning and research state of 12 the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. From 13 the analysis, the related research gaps were identified regarding information integration, alignment of BIM 14 processes to constructor business processes & the effective governance and value of information. From this a 15 unique research strategy utilising a framework for information governance coupled with a graph based distributed 16 data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects. 17

Question: What is the current state of BIM for infrastructure?


Challenges and Solutions for End-to-End Time-Sensitive Optical Networking. Time sensitive applications rely on deterministic low latency, highly reactive, and trustable networks, as opposed to today's best effort internet. We review existing solutions and propose in this paper Deterministic Dynamic Network as a solution for end-to-end time-sensitive optical networking.

Question: What is the deterministic dynamic network?


Ghost hunters: ambient light and hand gesture utilization in mobile augmented reality games. This paper presents the current status of work-in-progress in developing Ghost Hunters that is targeted to explore the possibilities of incorporating gesture control and ambient lighting in a mobile augmented reality game played with smart glasses. We present the design and implementation of the current prototype, together with a small feasibility evaluation of battery consumption and gesture control.

Question: What is the current status of work-in-progress in developing Ghost Hunters?


Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects. We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield better returns than the buy-and-hold strategy when disregarding transaction costs both in terms of number of stocks with profitable trades as well as overall returns. Moreover, our findings show that two-way and three-way combinations, i.e., complex trading rules, are important to "beat" the buy-and-hold strategy.

Question: What is the performance of complex trading rules in equity price direction prediction?


A language modeling framework for resource selection and results merging. Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.

Question: What is a language modeling framework for resource selection and results merging?


Explaining the Poor Performance of Consumption-Based Asset Pricing Models. The poor performance of consumption-based asset pricing models relative to traditional portfoliobased asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models. John Y. Campbell John H. Cochrane Department of Economics Graduate School of Business Harvard University University of Chicago Littauer Center 213 1101 E. 58th. St. Cambridge, MA 02138 Chicago, IL 60637 and NBER and NBER john_campbell@harvard.edu john.cochrane@gsb.uchicago.edu

Question: What is the difference between consumption-based asset pricing models and traditional portfoliobased asset pricing models?


An Ultra-Wideband Horizontally Polarized Omnidirectional Circular Connected Vivaldi Antenna Array. An ultra-wideband horizontally polarized omnidirectional antenna is proposed. The connected Vivaldi antenna array mechanism is exploited to achieve the ultra-wide bandwidths of both the impedance and the radiation patterns simultaneously. Sixteen Vivaldi antenna elements are connected with each other and distributed along a circular substrate to form the proposed antenna. The designed antenna exhibits a relative impedance bandwidth of 159.97% (1.28–11.51 GHz) for <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert <-10$ </tex-math></inline-formula> dB. Meanwhile, uniform omnidirectional radiation patterns with a gain variation of less than 1.5 dB have been obtained within the frequency band of 1.25–7.6 GHz (142.73%).

Question: What is the name of the antenna?


A review on methods and classifiers in lip reading. The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes, further development of the system should be an active cycle. Ongoing research into improvement of the lip reading system performance by way of suitable choices of feature extraction and classifiers is essential to track the developing trend in both fields of technology as well as in pattern recognition. This paper discusses a short review on the existing methods and classifiers which have been used in previous work in the field of lip reading.

Question: What is lip reading?


Dynamic Adaptive Streaming over HTTP – Design Principles and Standards. In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and some forward-looking considerations are provided.

Question: What is the difference between the 3GPP and MPEG DASH specifications?


Mobile technology and the digitization of healthcare.. The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

Question: What is the current state of mHealth?


Multi-label convolutional neural network based pedestrian attribute classification. Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database.

Question: What is a multi-label convolutional neural network?


Indirect Image Registration with Large Diffeomorphic Deformations. The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends so zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.

Question: What is the indirect image registration?


Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection. Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality in

Question: How can we reduce the impact of land-based threats on marine ecosystems?


Project Lachesis: Parsing and Modeling Location Histories. A datatype with increasing importance in GIS is what we call the location history–a record of an entity's location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.

Question: What is the difference between a stay and a destination?


Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles. Summarization is the way towards lessening the content of a text file to make it brief that holds all the critical purposes in the content of original text file. In the process of extractive summarization, one extracts only those sentences which are the most relevant sentences in the text document and that conveys the moral of the content. The extractive summarization techniques usually revolve around the idea of discovering most relevant and frequent keywords and then extract the sentences based on those keywords. Manual extraction or explanation of relevant keywords are a dreary procedure overflowing with errors including loads of manual exertion and time. In this paper, we proposed a hybrid approach to extract keyword automatically for multi-document text summarization in enewspaper articles. The performance of the proposed approach is compared with three additional keyword extraction techniques namely, term frequency-inverse document frequency (TF-IDF), term frequency-adaptive inverse document frequency (TF-AIDF), and a number of false alarm (NFA) for automatic keyword extraction and summarization in e-newspapers articles for better analysis. Finally, we showed that our proposed techniques had been outperformed over other techniques for automatic keyword extraction and summarization.

Question: How to extract keywords automatically for multi-document text summarization in enewspaper articles?


MATLAB based code for 3 D joint inversion of Magnetotelluric and Direct Current resistivity imaging data. 24 th EM Induction Workshop, Helsingør, Denmark, August 12-19, 2018 1 /4 MATLAB based code for 3D joint inversion of Magnetotelluric and Direct Current resistivity imaging data M. Israil 1 , A. Singh 1 Anita Devi 1 , and Pravin K.Gupta 1 1 Indian Institute of Technology, Roorkee, 247667, India, mohammad.israil@gmail.com

Question: How to invert 3D data?


A CCPP algorithm based on the standard map for the mobile robot. This paper introduces a new integrated algorithm to achieve the complete coverage path planning (CCPP) task for the mobile robot in a given obstacles-included terrain. The algorithm combines the cellular decomposition approach and the chaotic Standard map together to design the coverage procedure. The cellular decomposition approach decompose the target region into several rectangular feasible sub-regions. Then the chaotic Standard map in the full mapping state produces the complete coverage trajectories inside the feasible sub-regions, and the connection trajectories between two adjacent feasible sub-regions. Compared with the general cellular decomposition method, the proposed integrated algorithm needs no designated start point and goal point to link two adjacent sub-regions. The planned trajectories demonstrate a good distribution characteristics with regard to completeness and evenness. No obstacles-avoidance method and boundaries detection are needed in the coverage procedure.

Question: What is the difference between the standard map and the cellular decomposition approach?


Child Education Through Animation: An Experimental Study. Teachers have tried to teach their students by introducing text books along with verbal instructions in traditional education system. However, teaching and learning methods could be changed for developing Information and Communication Technology (ICT). It's time to adapt students with interactive learning system so that they can improve their learning, catching, and memorizing capabilities. It is indispensable to create high quality and realistic leaning environment for students. Visual learning can be easier to understand and deal with their learning. We developed visual learning materials (an overview of solar system) in the form of video for students of primary level using different multimedia application tools. The objective of this paper is to examine the impact of student's abilities to acquire new knowledge or skills through visual learning materials and blended leaning that is integration of visual learning materials with teacher's instructions. We visited a primary school in Dhaka city for this study and conducted teaching with three different groups of students (i) teacher taught students by traditional system on same materials and marked level of student's ability to adapt by a set of questions (ii) another group was taught with only visual learning material and assessment was done with 15 questionnaires, (iii) the third group was taught with the video of solar system combined with teacher's instructions and assessed with the same questionnaires. This integration of visual materials (solar system) with verbal instructions is a blended approach of learning. The interactive blended approach greatly promoted students ability of acquisition of knowledge and skills. Students response and perception were very positive towards the blended technique than the other two methods. This interactive blending leaning system may be an appropriate method especially for school children.

Question: What is the impact of visual learning materials on learning?


Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver. The automation of the overtaking maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. This operation involves two vehicles (the overtaking and the overtaken) cooperatively driving, as well as the surveillance of any other vehicles that are involved in the maneuver. This operation consists of two lane changes-one from the right to the left lane of the road, and the other is to return to the right lane after passing. Lane-change maneuvers have been used to move into or out of a circulation lane or platoon; however, overtaking operations have not received much coverage in the literature. In this paper, we present an overtaking system for autonomous vehicles equipped with path-tracking and lane-change capabilities. The system uses fuzzy controllers that mimic human behavior and reactions during overtaking maneuvers. The system is based on the information that is supplied by a high-precision Global Positioning System and a wireless network environment. It is able to drive an automated vehicle and overtake a second vehicle that is driving in the same lane of the road.

Question: What is the difference between a lane change and an overtaking maneuver?


A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range. In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC  converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.

Question: What is the ultrawide dc-link voltage range?


Data Driven Ontology Evaluation. The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the 'fit' between an ontology and a domain of knowledge. We consider a number of methods for measuring this 'fit' and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Question: What is the best way to evaluate ontologies?


Design and analysis of an origami continuum manipulation module with torsional strength. This paper presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, low-volume deployment, light weight, and inherently safe human interaction and collaboration. Each module has a mass of around 110 g and integrates the actuation, sensing, and control sub-systems necessary for operation. The origami structure has 7.311 Nm/rad (0.128 Nm/degree) torsional stiffness while being capable of bending in two directions and changing arc-length down to a fully collapsed state. A maximum contraction of 35 mm and bending angle of 35.5 degrees were achieved with 45 mm arc length. The module is capable of passively supporting a 1-kg mass at its tip, or 4 additional serially connected modules, bending approximately 6 degrees in the worst case. We also show that we can actively compensate for external moments by pre-compressing or pre-bending the module. We utilize an inverse kinematic control scheme and use it for both open and closed loop control following a circular trajectory. Our results indicate that the module motion follows the desired trajectory with an RMS error of 0.681 mm in the horizontal (x-y) plane and 0.373 mm in the z-axis with closed-loop control. We also assembled two origami modules in series and drove them independently, demonstrating the proof-of-concept of a modular origami continuum manipulator.

Question: How does the module work?


Dual Quaternion Variational Integrator for Rigid Body Dynamic Simulation. We introduce a symplectic dual quaternion variational integrator(DQVI) for simulating single rigid body motion in all six degrees of freedom. Dual quaternion is used to represent rigid body kinematics and one-step Lie group variational integrator is used to conserve the geometric structure, energy and momentum of the system during the simulation. The combination of these two becomes the first Lie group variational integrator for rigid body simulation without decoupling translations and rotations. Newton-Raphson method is used to solve the recursive dynamic equation. This method is suitable for real-time rigid body simulations with high precision under large time step. DQVI respects the symplectic structure of the system with excellent long-term conservation of geometry structure, momentum and energy. It also allows the reference point and 6-by-6 inertia matrix to be arbitrarily defined, which is very convenient for a variety of engineering problems.

Question: What is a symplectic structure?


A 3D Face Model for Pose and Illumination Invariant Face Recognition. Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.

Question: What is a generative 3D face model?


Using k-Poselets for Detecting People and Localizing Their Keypoints. A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.

Question: What is a poselet?


Testing the Interactivity Model: Communication Processes, Partner Assessments, and the Quality of Collaborative Work. EE K. BuRGOON IS Professor of Communication and Director of the Center for the Management of Information at the University of Arizona. She is the author or coauthor of seven books and nearly two hundred articles and chapters on topics related to interpersonal and nonverbal communication, deception and credibility, mass media, and new communication technologies. Her current research focuses on interactivity, adaptation, and attunement in communication.

Question: What is the interactivity model?


LQR, double-PID and pole placement stabilization and tracking control of single link inverted pendulum. This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation. The nonlinear model linearization was presented based on Taylor series approximation. LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system. Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared. The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection. Thus, the performance of LQR is more reliable and satisfactory. Finally, future work suggestions were made.

Question: What is the difference between LQR and double-PID?


Reducing the dimensionality of data with neural networks.. High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

Question: How to initialize the weights of a neural network?


Non-Foster impedance matching of electrically small antennas. When the size of an antenna is electrically small, the antenna is neither efficient nor a good radiator because most of the input power is stored in the reactive near-field region and little power is radiated in the far-field region. As demonstrated in [1]- [2], the radiation quality factor of small antennas is definitely high. In other words, the input impedance of small antennas is considerably reactive. To reduce the radiation quality factor in the whole or partial frequency range of interest, it is important to increase the radiation resistance and/or reduce the reactance of the antenna. Hence, it is necessary to modify the antenna to reduce the reactance of the antenna and/or add impedance matching networks (MNs) to maximize the transfer of power from a resistive source to the highly reactive antenna.

Question: How to reduce the radiation quality factor of an electrically small antenna?


DRAW: A Recurrent Neural Network For Image Generation. This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

Question: What is the state of the art for generative models on MNIST?


Developing a Feasible and Maintainable Ontology for Automatic Landscape Design. In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of socalled sustainable landscapes that allow maximum use of the environment, natural resources and promote sustainable restoration of ecosystems. For such purposes, a designer needs a complete database with existing and suitable plants, but no designing tool has one. Therefore it is presented the structure and the development of on ontology suitable for storing and managing all information and knowledge about plants. The advantage is that the format of the ontology allows the storage of any plant species (e.g. live or fossil) and automated reasoning. Ontology is a formal conceptualization of a particular knowledge about the world, through the explicit representation of basic concepts, relations, and inference rules about themselves. Therefore the ontology may be used by a design tool for helping the designer and choosing the best options for a sustainable landscape. Keywords—environment; landscapes; ontology; ontology-based simulation; sustainable landscapes

Question: What is the advantage of ontology?


Improving retention and graduate recruitment through immersive research experiences for undergraduates. Research experiences for undergraduates are considered an effective means for increasing student retention and encouraging undergraduate students to continue on to graduate school. However, managing a cohort of undergraduate researchers, with varying skill levels, can be daunting for faculty advisors. We have developed a program to engage students in research and outreach in visualization, virtual reality, networked robotics, and interactive games. Our program immerses students into the life of a lab, employing a situated learning approach that includes tiered mentoring and collaboration to enable students at all levels to contribute to research. Students work in research teams comprised of other undergraduates, graduate students and faculty, and participate in professional development and social gatherings within the larger cohort. Results from our first two years indicate this approach is manageable and effective for increasing students' ability and desire to conduct research.

Question: How do you manage a cohort of undergraduate researchers?


Handbook of Natural Language Processing. Handbook of Natural Language Processing and Machine Translation · Learning Natural Language Processing With Python and NLTK p.1 Tokenizing words and By far, the most popular toolkit or API to do natural language processing... Nitin Indurkhya, Fred J. Damerau, Handbook of Natural Language Processing, Second Edition. Chapman & Hall/CRC Machine Learning & Pattern Recognition. Arabic Natural Language Processing, Machine Translation Handbook of Natural Language Processing and Machine Translation, 164-175, 2011. 2011.

Question: What is the most popular toolkit or API to do natural language processing?


Block Chain Technologies & The Semantic Web : A Framework for Symbiotic Development. The concept of peer-to-peer applications is not new, nor is the concept of distributed hash tables. What emerged in 2008 with the publication of the Bitcoin white paper was an incentive structure that unified these two software paradigms with a set of economic stimuli to motivate the creation of a dedicated computing network orders of magnitude more powerful than the world's fastest supercomputers. The purpose of which is the maintenance of a massive distributed database known as the Bitcoin "block chain". Apart from the digital currency it enables, block chain technology is a fascinating new computing paradigm with broad implications for the future development of the World Wide Web, and by extension, the further growth of Linked Data and the Semantic Web. This work is divided into two main sections, we first demonstrate how block chain technologies can contribute toward the realization of a more robust Semantic Web, and subsequently we provide a framework wherein the Semantic Web is utilized to ameliorate block chain technology itself.

Question: What is the purpose of the block chain?


An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis.

Question: What is a neural network?


Parallel Computer Architecture: A Hardware/Software Approach. enter the circuit several times, possibly via different input processors. More elaborate combinatorial considerations than for standard VLSI circuits are needed to show how communication complexity can be used to get lower bounds on multilective VLSI computations. Near the end of the book, Hromkovic presents relations between communication complexity and some complexity measures of sequential computations. The core section introduces a uniform model of one-way communication protocols and shows that the corresponding uniform one-way communication complexity is strongly related to the size of deterministic finite automata. Then he relates communication complexity to the time and space complexity of Turing machines. Finally, he shows how communication complexity can be used to obtain lower bounds on the size and the depth of decisions nees and branching programs.

Question: What is the size of a deterministic finite automata?


Insider Threat Security Reference Architecture. The Insider Threat Security Reference Architecture, ITSRA, provides an enterprise wide solution to insider threat. The architecture consists of four security layers - Business, Information, Data, and Application. Organizations should deploy and enforce controls at each layer in order to address insider attacks. Each layer does not function in isolation or independently of other layers. Rather, the correlation of indicators and application of controls across all four layers forms the crux of this approach. Based on empirical data consisting of over 600 cases of insider crimes, insider attacks proved successful in inflicting damage when an organization failed to implement adequate controls in any of three security principles - authorized access, acceptable use, and continuous monitoring. The ITSRA draws from existing best practices and standards as well as from analysis of these cases to provide actionable guidance for organizations to improve their posture against the insider threat.

Question: What is the insider threat security reference architecture?


Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis. Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics [1] and sensor array processing [2]), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states [7]). In many applications, the possibly huge tensors can be approximated well by compact multilinear models or decompositions. Tensor decompositions are more versatile tools than the linear models resulting from traditional matrix approaches. Compared to matrices, tensors have at least one extra dimension. The number of elements in a tensor increases exponentially with the number of dimensions, and so do the computational and memory requirements. The exponential dependency (and the problems that are caused by it) is called the curse of dimensionality. The curse limits the order of the tensors that can be handled. Even for a modest order, tensor problems are often large scale. Large tensors can be handled, and the curse can be alleviated or even removed by using a decomposition that represents the tensor instead of using the tensor itself. However, most decomposition algorithms require full tensors, which renders these algorithms infeasible for many data sets. If a tensor can be represented by a decomposition, this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.

Question: What is the curse of dimensionality?


Event Extraction with Complex Event Classification Using Rich Features. Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.

Question: What is the main contribution of the present paper?


An approach to interactive deep reinforcement learning for serious games. Serious games receive increasing interest in the area of e-learning. Their development, however, is often still a demanding, specialized and arduous process, especially when regarding reasonable non-player character behaviour. Reinforcement learning and, since recently, also deep reinforcement learning have proven to automatically generate successful AI behaviour to a certain degree. These methods are computationally expensive and hardly scalable to various complex serious game scenarios. For this reason, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts' knowledge to guide the learning process. Thereby, we aim to create a synergistic combination of experts and emergent cognitive systems. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a framework.

Question: What is a serious game?


Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System. The perceptual computer (Per-C) is an architecture that makes subjective judgments by computing with words (CWWs). This paper applies the Per-C to hierarchical decision making, which means decision making based on comparing the performance of competing alternatives, where each alternative is first evaluated based on hierarchical criteria and subcriteria, and then, these alternatives are compared to arrive at either a single winner or a subset of winners. What can make this challenging is that the inputs to the subcriteria and criteria can be numbers, intervals, type-1 fuzzy sets, or even words modeled by interval type-2 fuzzy sets. Novel weighted averages are proposed in this paper as a CWW engine in the Per-C to aggregate these diverse inputs. A missile-evaluation problem is used to illustrate it. The main advantages of our approaches are that diverse inputs can be aggregated, and uncertainties associated with these inputs can be preserved and are propagated into the final evaluation.

Question: What is a missile-evaluation problem?


Learning user tastes: a first step to generating healthy meal plans. Poor nutrition is fast becoming one of the major causes of ill-health and death in the western world. It is caused by a variety of factors including lack of nutritional understanding leading to poor choices being made when selecting which dishes to cook and eat. We wish to build systems which can recommend nutritious meal plans to users, however a crucial pre-requisite is to be able to recommend dishes that people will like. In this work we investigate key factors contributing to how recipes are rated by analysing the results of a longterm study (n=123 users) in order to understand how best to approach the recommendation problem. In doing so we identify a number of important contextual factors which can influence the choice of rating and suggest how these might be exploited to build more accurate recipe recommender systems. We see this as a crucial first step in a healthy meal recommender. We conclude by summarising our thoughts on how we will combine recommended recipes into meal plans based on nutritional guidelines.

Question: How to build a healthy meal recommender system?


Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review.. Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.

Question: What is the most common form of cancer among women worldwide?


Bus detection device for the blind using RFID application. This paper outlines a bus detection mechanism for the blind in travelling from one place to another. In order to get transportation independently, the blind use auditory touched clues like walking stick or white cane. The limitation of the walking stick is that a blind person must come into close proximity with their surroundings to determine the location of an obstacle. For that basis, various devices have been developed such as the Sonicguide, the Mowat sensor, the Laser cane and the Navbelt [4]. However, these device can only assist the blind at a pedestrian crossing. Therefore, the project is aims to develop a bus detection prototype using Radio Frequency Identification (RFID) for the blind. The paper covers brief idea about the blind and RFID system, review relevant papers and summary of current research. The review of RFID system compare between families of auto-ID technology, the basic principle of RFID, the type of RFID tagging and the antenna characteristic. The summary of current research discussed about the development of prototype, the database system, the output mechanism and integration between hardware and software. Database management will provided. The information such as bus route, final destination and bus number are also provided. This paper also describes the future work intended to be done.

Question: What is the difference between RFID and barcode?


A Resource-Based Model For E-Commerce In Developing Countries. Previous efforts in electronic commerce (e-commerce) research in developing countries shows that there is an acute lack of theoretical frameworks and empirical evidence to understand how developing country firms realize electronic commerce benefits amidst their national constraints. This paper sets out to develop a theoretically abstracted but contextually grounded model of how developing country firms can orient their resources and realize these benefits amidst their national constraints. A review of e-commerce and strategy management literature to develop a resource – based model for e-commerce benefits was undertaken. The process-based model provides an understanding of how to identify, integrate, and reconfigure resources to achieve electronic commerce benefits; provides propositions that serves as theoretical platforms for future empirically grounded research on electronic commerce in developing country contexts and brings organizations closer to identifying and categorizing the strategic value of resources and the role managerial capabilities and intangible resources play in sustaining e-commerce benefits. Finally, our findings provides organizations the strategic options to address resources which have lost their value or have become less valuable to their strategic orientation in e-commerce adoption thereby serving as a starting point of examining e-commerce in developing countries through the theoretical lens of information systems and strategic management.

Question: What is a resource-based model for e-commerce in developing countries?


Building Footprints Extraction from Oblique Imagery. Nowadays, multi-camera aerial platforms combining nadir and oblique cameras are experiencing a revival and several companies have proposed new image acquisition systems. Due to their various advantages, oblique imagery have found their place in numerous companies and civil applications. However, the automatic processing of such image blocks still remains a topic of research. Camera configuration indeed poses a challenge on the traditional photogrammetric pipeline used in commercial software but, on the other hand, gives the opportunity to exploit the additional information provided by the oblique views and allows a more reliable feature extraction. In particular, the information that can be provided in correspondence of building façades can open new possibilities for the building detection and footprint extraction. In this paper, a methodology for the automated extraction of building footprints from oblique imagery is presented. The extraction is performed using dense point clouds generated using an image matching algorithm. The developed methodology and the achieved results are described in detail showing the advantages and opportunities offered by oblique aerial systems for cartographic and mapping purposes.

Question: Building footprints extraction from oblique imagery


Hand Gesture Recognition with Leap Motion. The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multiclass SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.

Question: How to recognize hand gesture with Leap Motion?


High Accuracy Optical Flow Estimation Based on a Theory for Warping. We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise. In Proc. 8th European Conference on Computer Vision, Springer LNCS 3024, T. Pajdla and J. Matas (Eds.), vol. 4, pp. 25-36, Prague, Czech Republic, May 2004 c © Springer-Verlag Berlin Heidelberg 2004 Received The Longuet-Higgins Best Paper Award.

Question: What is the difference between the two methods?


Deformable Image Registration: A Survey. Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by di erent imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where the development of phenomenon is studied over time; and iii) population modeling towards studying normal anatomical variability. In this research report, we attempt to give an overview of the deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study in depth image registration methods, their main components are identi ed and then studied independently. The most recent techniques are presented in a principled fashion. Key-words: Deformable registration, bibliographical review ∗ Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale de Paris, Equipe GALEN, INRIA Saclay Étude Bibliographique sur le Recalage Déformable d'Images Résumé : Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des di érents types de modalités a n de faciliter le diagnostic et la plani cation du traitement; ii) les études longitudinales, oú le d'eveloppement d'un phénomène est etudié en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population. Dans ce rapport de recherche, nous essayons de donner un aperçu des di érentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particuliérement insisté sur les techniques appliquées aux images médicales. A n d'étudier en profondeur les méthodes du recalage d'images, leurs composants principales sont d'abord iden

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Finite Element Analysis Based Design of Mobile Robot for Removing Plug Oil Well. In order to develop the mobile robot for removing the plug oil well, the robot was designed based on the wheeltype and leg-type robot mechanism. A well functioning prototype has been manufactured. To demonstrate the validity and the benefit of the mobile robot, supporting mechanism and guiding rod were chosen to design based on the FEM. The mathematical model of the supporting mechanism is established and the mechanical property is analyzed using the FEM. The deformation and stress of some components of the supporting mechanism and the guiding rod is investigated. The results show that the supporting mechanism and the guiding rod have excellent performance with little displacement and small stress under working condition. The strength and rigidity of supporting mechanism and the guiding rod are good enough to ensure the reliability of the whole robot mechanism.

Question: How to design a mobile robot for removing the plug oil well?


Review of Ontology Matching Approaches and Challenges. Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instancebased, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.

Question: What is ontology matching?


Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification. This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.

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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrowclearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.

Question: What is a narrowclearance peg-insertion task?


Research guidelines for the Delphi survey technique.. Consensus methods such as the Delphi survey technique are being employed to help enhance effective decision-making in health and social care. The Delphi survey is a group facilitation technique, which is an iterative multistage process, designed to transform opinion into group consensus. It is a flexible approach, that is used commonly within the health and social sciences, yet little guidance exists to help researchers undertake this method of data collection. This paper aims to provide an understanding of the preparation, action steps and difficulties that are inherent within the Delphi. Used systematically and rigorously, the Delphi can contribute significantly to broadening knowledge within the nursing profession. However, careful thought must be given before using the method; there are key issues surrounding problem identification, researcher skills and data presentation that must be addressed. The paper does not claim to be definitive; it purports to act as a guide for those researchers who wish to exploit the Delphi methodology.

Question: What is the delphi survey technique?


Agile Flight Control Techniques for a Fixed-Wing Aircraft by Frantisek Michal Sobolic. As unmanned aerial vehicles (UAVs) become more involved in challenging mission objectives, the need for agility controlled flight becomes more of a necessity. The ability to navigate through constrained environments as well as quickly maneuver to each mission target is essential. Currently, individual vehicles are developed with a particular mission objective, whether it be persistent surveillance or fly-by reconnaissance. Fixed-wing vehicles with a high thrust-to-weight ratio are capable of performing maneuvers such as take-off or perch style landing and switch between hover and conventional flight modes. Agile flight controllers enable a single vehicle to achieve multiple mission objectives. By utilizing the knowledge of the flight dynamics through all flight regimes, nonlinear controllers can be developed that control the aircraft in a single design. This thesis develops a full six-degree-of-freedom model for a fixed-wing propellerdriven aircraft along with methods of control through nonconventional flight regimes. In particular, these controllers focus on transitioning into and out of hover to level flight modes. This maneuver poses hardships for conventional linear control architectures because these flights involve regions of the post-stall regime, which is highly nonlinear due to separation of flow over the lifting surfaces. Using Lyapunov backstepping control stability theory as well as quaternion-based control methods, control strategies are developed that stabilize the aircraft through these flight regimes without the need to switch control schemes. The effectiveness of each control strategy is demonstrated in both simulation and flight experiments. Thesis Supervisor: Jonathan P. How Title: Professor

Question: How to control a fixed wing aircraft


Depression as a risk factor for coronary artery disease: evidence, mechanisms, and treatment.. OBJECTIVE The present paper reviews the evidence that depression is a risk factor for the development and progression of coronary artery disease (CAD).   METHODS MEDLINE searches and reviews of bibliographies were used to identify relevant articles. Articles were clustered by theme: depression as a risk factor, biobehavioral mechanisms, and treatment outcome studies.   RESULTS Depression confers a relative risk between 1.5 and 2.0 for the onset of CAD in healthy individuals, whereas depression in patients with existing CAD confers a relative risk between 1.5 and 2.5 for cardiac morbidity and mortality. A number of plausible biobehavioral mechanisms linking depression and CAD have been identified, including treatment adherence, lifestyle factors, traditional risk factors, alterations in autonomic nervous system (ANS) and hypothalamic pituitary adrenal (HPA) axis functioning, platelet activation, and inflammation.   CONCLUSION There is substantial evidence for a relationship between depression and adverse clinical outcomes. However, despite the availability of effective therapies for depression, there is a paucity of data to support the efficacy of these interventions to improve clinical outcomes for depressed CAD patients. Randomized clinical trials are needed to further evaluate the value of treating depression in CAD patients to improve survival and reduce morbidity.

Question: What is the evidence that depression is a risk factor for coronary artery disease?


Amplified Boomerang Attacks Against Reduced-Round MARS and Serpent. We introduce a new cryptanalytic technique based on Wagner's boomerang and inside-out attacks. We first describe this new attack in terms of the original boomerang attack, and then demonstrate its use on reduced-round variants of the MARS core and Serpent. Our attack breaks eleven rounds of the MARS core with 2 chosen plaintexts, 2 memory, and 2 partial decryptions. Our attack breaks eight rounds of Serpent with 2 chosen plaintexts, 2 memory, and 2 partial decryptions.

Question: How to break a reduced-round MARS core?


Improving Distributional Similarity with Lessons Learned from Word Embeddings. Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.

Question: What is the difference between neural network and traditional distributional models?


Analytical Design Methodology for Litz-Wired High-Frequency Power Transformers. In the last quarter of a century, high-frequency (HF) transformer design has been one of the major concerns to power electronics designers in order to increase converter power densities and efficiencies. Conventional design methodologies are based on iterative processes and rules of thumb founded more on expertise than on theoretical developments. This paper presents an analytical design methodology for litz-wired HF power transformers that provides a deep insight into the transformer design problem making it a powerful tool for converter designers. The most suitable models for the calculation of core and winding losses and the transformer thermal resistance are first selected and then validated with a 5-kW 50-kHz commercial transformer for a photovoltaic application. Based on these models, the design methodology is finally proposed, reducing the design issue to directly solve a five-variable nonlinear optimization problem. The methodology is illustrated with a detailed design in terms of magnetic material, core geometry, and primary and secondary litz-wire sizing. The optimal design achieves a 46.5% power density increase and a higher efficiency of 99.70% when compared with the commercial one.

Question: What is the design methodology for litz-wired high-frequency power transformers?


Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel. Most previously proposed statistical models for the indoor multipath channel include only time of arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full spare-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.

Question: How to model the time and angle of arrival characteristics of an indoor multipath channel?