Forward-viewing CMUT arrays for medical imaging. This paper reports the design and testing of forward-viewing annular arrays fabricated using capacitive micromachined ultrasonic transducer (CMUT) technology. Recent research studies have shown that CMUTs have broad frequency bandwidth and high-transduction efficiency. One- and two-dimensional CMUT arrays of various sizes already have been fabricated, and their viability for medical imaging applications has been demonstrated. We fabricated 64-element, forward-viewing annular arrays using the standard CMUT fabrication process and carried out experiments to measure the operating frequency, bandwidth, and transmit/receive efficiency of the array elements. The annular array elements, designed for imaging applications in the 20 MHz range, had a resonance frequency of 13.5 MHz in air. The immersion pulse-echo data collected from a plane reflector showed that the devices operate in the 5-26 MHz range with a fractional bandwidth of 135%. The output pressure at the surface of the transducer was measured to be 24 kPa/V. These values translate into a dynamic range of 131.5 dB for 1-V excitation in 1-Hz bandwidth with a commercial low noise receiving circuitry. The designed, forward-viewing annular CMUT array is suitable for mounting on the front surface of a cylindrical catheter probe and can provide Doppler information for measurement of blood flow and guiding information for navigation through blood vessels in intravascular ultrasound imaging.

Question: What is the difference between a forward-viewing and a backward-viewing CMUT array?


SiGe BiCMOS and eWLB packaging technologies for automotive radar solutions. In this paper the evolution of silicon based automotive radar for the 76-81 GHz range is described. Starting with SiGe bare-die chips in 2009, today packaged MMICs are available for low-cost radar applications. Future SiGe BiCMOS technology will enable highly integrated single chip radars with superior performance at lower power consumption. This will pave the way for automotive radar safety for everyone and will be an important step towards autonomous driving.

Question: What is the evolution of silicon based automotive radar for the 76-81 GHz range?


ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection. As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicateobject triplet, e.g. 〈person-ride-horse〉. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the stateof-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.

Question: What is the visual relationship detection?


An automated system to detect and recognize vehicle license plates of Bangladesh. Bangladesh is a country in South Asia that uses Retro Reflective license plates. The plate has two lines with words, letters, and digits. An automated system to detect and recognize these plates is presented in this paper. The system is divided into four parts: plate detection, extraction, character segmentation and recognition. At first, the input image is enhanced using CLAHE and a matched filter specially designed for license plates with two lines is applied. Then tilt correction using Radon transformation, binarization and cleaning are performed. For character segmentation, mean intensity based horizontal and vertical projection is used. In recognition, we have used two different Convolutional Neural Network (CNN) to classify digits and letters. Tesseract OCR is used for district names. We have developed a dataset of over 400 images of different vehicles (e.g., private car, bus, truck etc.) taken at different times of day (including nights). The plates in this dataset are in different angles, including blurry, worn-out and muddy ones. On this dataset, the proposed system achieved a success rate of 96.8% in detection, 89.5% in extraction, 98.6% in segmentation and 98.0% in character recognition.

Question: What is the name of the system?


Understanding Human Mobility from Twitter. Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers' movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

Question: What is the impact of different modes of travel on human mobility?


The Case for VM-Based Cloudlets in Mobile Computing. Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement.

Question: What is the case for VM-based cloudlets in mobile computing?


A 6LoWPAN Accelerator for Internet of Things Endpoint Devices. The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.

Question: What is the IPv6 over low power wireless personal area networks (6LoWPAN) accelerator?


Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate. A patient's estimated glomerular filtration rate (eGFR) can provide important information about disease progression and kidney function. Traditionally, an eGFR time series is interpreted by a human expert labelling it as stable or unstable. While this approach works for individual patients, the time consuming nature of it precludes the quick evaluation of risk in large numbers of patients. However, automating this process poses significant challenges as eGFR measurements are usually recorded at irregular intervals and the series of measurements differs in length between patients. Here we present a two-tier system to automatically classify an eGFR trend. First, we model the time series using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed size vector of fifty time-dependent observations. Second, we classify the resampled eGFR time series using a K-NN/SVM classifier, and evaluate its performance via 5-fold cross validation. Using this approach we achieved an F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst themselves.

Question: How to classify irregularly sampled time series with unequal lengths?


A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment. With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.

Question: What is the difference between a parallel random forest algorithm and a parallel random forest algorithm?


Deep Learning and Wavelets for High-Frequency Price Forecasting. This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 oneminute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN's Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.

Question: What is the Dow Jones Industrial Average Index?


A Framework for Detecting MAC and IP Spoofing Attacks with Network Characteristics. This paper presents a spoofing attack detection framework based on physical network characteristics (e.g., received signal strength indicator round trip time and link quality indicator) that cannot easily be mimicked by artificial means. Unlike most previous studies that are sensitive to changes in network conditions, we propose a spoofing attack detection method, which is highly robust to the changes of network conditions over time. The proposed framework can monitor devices' physical network characteristics in real time and check if any significant changes have been made in the monitored measurements to effectively detect device spoofing attacks. To demonstrate the feasibility of the proposed framework, we analyzed how the RSSI values of packets were changed with varying physical distances in a real ZigBee (IEEE 802.15.4) network environment.

Question: How to detect spoofing attacks?


Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficultie

Question: How to improve the performance of the short separation regression method?


An electromechanical interpretation of electrowetting. Electrowetting on dielectric-coated electrodes involves two independently observable phenomena: (i) the well-known voltage dependence of the apparent contact angle and (ii) a central electromechanical force that can be exploited to move and manipulate small liquid volumes on a substrate. The electromechanical force does not depend upon field-mediated changes of the contact angle; it is operative even if the liquid surface is constrained. Forces on liquid volumes may be determined using capacitance or the Maxwell stress tensor with no reference made to liquid surface profiles. According to this interpretation, a nonlinear mechanism manifesting a voltage threshold is responsible for both contact angle saturation and the observed clamping of the electromechanical force.

Question: What is the electromechanical interpretation of electrowetting?


Compact Duplexing for a 680-GHz Radar Using a Waveguide Orthomode Transducer. A compact 680-GHz waveguide orthomode transducer (OMT) and circular horn combination has been designed, tested, and characterized in a radar transceiver's duplexer. The duplexing capability is implemented by a hybrid waveguide quasi optical solution, combining a linear polarization OMT and an external grating polarizer. Isolation between the OMT's orthogonal ports' flanges was measured with a vector network analyzer to exceed 33 dB over a >10% bandwidth between 630 and 710 GHz. Calibrated Y-factor measurements using a mixer attached to the OMT ports reveal losses through the transmit and receive paths that sum to an average of 4.7 dB of two-way loss over 660-690 GHz. This is consistent with radar sensitivity measurements comparing the new OMT/horn with a quasi-optical wire grid beam splitter. Moreover, the radar performance assessment validates the OMT as a suitable compact substitute of the wire grid for the JPL's short-range 680-GHz imaging radar.

Question: What is a waveguide orthomode transducer?


Detecting and discriminating behavioural anomalies. This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects' behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed behaviour is then modelled using a cascade of Dynamic Bayesian Networks (CasDBNs). In contrast to existing standalone models, the proposed behaviour decomposition and cascade modelling offers distinct advantage in simplicity for complex behaviour modelling. Importantly, the decomposition and cascade structure map naturally to the structure of complex behaviour, allowing for a more effective detection of subtle anomalies in surveillance videos. Comparative experiments using both indoor and outdoor data are carried out to demonstrate that, in addition to the novel capability of discriminating different types of anomalies, the proposed framework outperforms existing methods in detecting durational anomalies in complex behaviours and subtle anomalies that are difficult to detect when objects are viewed in isolation. & 2010 Elsevier Ltd. All rights reserved.

Question: How to detect and discriminate behavioural anomalies?


Examining the effects of mass media campaign exposure and interpersonal discussions on youth's drug use: the mediating role of visiting pro-drug websites.. To extend past research on interpersonal communication and campaign effects, we hypothesized that anti-drug mass media campaign message exposure indirectly affects visiting anti- and pro-drug websites through targeted parent-child and friend-to-friend communication against drugs, as well as through having drug-related discussions during organized group activities. Second, we posited that engaging in anti-drug interpersonal communication indirectly affects adolescents' drug use through two intervening variables: visiting anti-drug websites and visiting pro-drug websites. Using self-reported longitudinal data from 2,749 youth, we found that as youth reported higher levels of anti-drug mass media campaign message exposure, they were more likely to talk to friends about the bad consequences of drugs, how to avoid drugs, and anti-drug ads. In turn, however, they were more likely to visit pro-drug websites, and subsequently, to smoke cigarettes.

Question: How does mass media campaign exposure affect youth's drug use?


The thoracolumbar fascia: anatomy, function and clinical considerations.. In this overview, new and existent material on the organization and composition of the thoracolumbar fascia (TLF) will be evaluated in respect to its anatomy, innervation biomechanics and clinical relevance. The integration of the passive connective tissues of the TLF and active muscular structures surrounding this structure are discussed, and the relevance of their mutual interactions in relation to low back and pelvic pain reviewed. The TLF is a girdling structure consisting of several aponeurotic and fascial layers that separates the paraspinal muscles from the muscles of the posterior abdominal wall. The superficial lamina of the posterior layer of the TLF (PLF) is dominated by the aponeuroses of the latissimus dorsi and the serratus posterior inferior. The deeper lamina of the PLF forms an encapsulating retinacular sheath around the paraspinal muscles. The middle layer of the TLF (MLF) appears to derive from an intermuscular septum that developmentally separates the epaxial from the hypaxial musculature. This septum forms during the fifth and sixth weeks of gestation. The paraspinal retinacular sheath (PRS) is in a key position to act as a 'hydraulic amplifier', assisting the paraspinal muscles in supporting the lumbosacral spine. This sheath forms a lumbar interfascial triangle (LIFT) with the MLF and PLF. Along the lateral border of the PRS, a raphe forms where the sheath meets the aponeurosis of the transversus abdominis. This lateral raphe is a thickened complex of dense connective tissue marked by the presence of the LIFT, and represents the junction of the hypaxial myofascial compartment (the abdominal muscles) with the paraspinal sheath of the epaxial muscles. The lateral raphe is in a position to distribute tension from the surrounding hypaxial and extremity muscles into the layers of the TLF. At the base of the lumbar spine all of the layers of the TLF fuse together into a thick

Question: What is the thoracolumbar fascia?


A Survey of Challenges and Applications of Wireless Body Area Network (WBAN) and Role of a Virtual Doctor Server in Existing Architecture. Wireless body area network has gained much interest and became emerging technology at health service facilities due to its wide range of utility and vital role to improve the human health. In this research paper, we are conducting a comprehensive survey of wireless Body Area Network (WBAN) and also introducing a virtual doctor server (VDS) in existing WBAN architecture. Existing architecture of WBAN consists of: wireless sensor, wireless actuator node , wireless central unit and wireless Personal Device (PD). Personal Digital Assistant (PDA) or smart phone can be used as PD. Based on the existing architecture mentioned above, we propose a design concept for a virtual doctor server (VDS) to support various patient health care services. VDS will keep the historical data about the patient, generate the daily tips and advices for him, call the doctor or emergency squad if required and can provide first aid assistance instructions on patient or any of his close relative's PDA's.

Question: What is a virtual doctor server?


Performance Comparison between Five NoSQL Databases. Recently NoSQL databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of NoSQL databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five NoSQL clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool – YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to NoSQL developers and users.

Question: What is the performance of five NoSQL databases?


Support vector machines for spam categorization. We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.

Question: What is the difference between SVM and decision trees?


What Is Coefficient Alpha ? An Examination of Theory and Applications. Psychological research involving scale construction has been hindered considerably by a widespread lack of understanding of coefficient alpha and reliability theory in general. A discussion of the assumptions and meaning of coefficient alpha is presented. This discussion is followed by a demonstration of the effects of test length and dimensionality on alpha by calculating the statistic for hypothetical tests with varying numbers of items, numbers of orthogonal dimensions, and average item intercorrelations. Recommendations for the proper use of coefficient alpha are offered.

Question: What is coefficient alpha?


Inside help: An integrative review of champions in healthcare-related implementation. Background/aims The idea that champions are crucial to effective healthcare-related implementation has gained broad acceptance; yet the champion construct has been hampered by inconsistent use across the published literature. This integrative review sought to establish the current state of the literature on champions in healthcare settings and bring greater clarity to this important construct.   Methods This integrative review was limited to research articles in peer-reviewed, English-language journals published from 1980 to 2016. Searches were conducted on the online MEDLINE database via OVID and PubMed using the keyword "champion." Several additional terms often describe champions and were also included as keywords: implementation leader, opinion leader, facilitator, and change agent. Bibliographies of full-text articles that met inclusion criteria were reviewed for additional references not yet identified via the main strategy of conducting keyword searches in MEDLINE. A five-member team abstracted all full-text articles meeting inclusion criteria.   Results The final dataset for the integrative review consisted of 199 unique articles. Use of the term champion varied widely across the articles with respect to topic, specific job positions, or broader organizational roles. The most common method for operationalizing champion for purposes of analysis was the use of a dichotomous variable designating champion presence or absence. Four studies randomly allocated of the presence or absence of champions.   Conclusions The number of published champion-related articles has markedly increased: more articles were published during the last two years of this review (i.e. 2015-2016) than during its first 30 years (i.e. 1980-2009).The number of champion-related articles has continued to increase sharply since the year 2000. Individual studies consistently found that champions were important p

Question: What is the difference between a champion and an opinion leader?


New Hash Functions and Their Use in Authentication and Set Equality. In this paper we exhibit several new classes of hash functions with certain desirable properties, and introduce two novel applications for hashing which make use of these functions. One class contains a small number of functions, yet is almost universal,. If the functions hash n-bit long names into m-bit indices, then specifying a member of the class requires only O((m + log, log,(n)) . log,(n)) bits as compared to O(n) bits for earlier techniques. For long names, this is about a factor of m larger than the lower bound of m + log, n -log, m bits. An application of this class is a provably secure authentication technique for sending messages over insecure lines. A second class of functions satisfies a much stronger property than universal,. We present the application of testing sets for equality. The authentication technique allows the receiver to be certain that a message is genuine. An "enemy"-even one with infinite computer resources-cannot forge or modify a message without detection. The set equality technique allows operations including "add member to set," "delete member from set" and "test two sets for equality" to be performed in expected constant time and with less than a specified probability of error.

Question: What is the difference between a hash function and a hash table?


Learning Policies for Partially Observable Environments: Scaling Up. Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for nding optimal behavior do not appear to scale well and have been unable to nd satisfactory policies for problems with more than a dozen states. After a brief review of pomdp's, this paper discusses several simple solution methods and shows that all are capable of nding near-optimal policies for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We nd that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains.

Question: What is a partially observable Markov decision process?


wradlib – An Open Source Library for Weather Radar Data Processing. Observation data from weather radars is deemed to have great potential in hydrology and meteorology for forecasting of severe weather or floods in small and urban catchments by providing high resolution measurements of precipitation. With the time series from operational weather radar networks attaining lengths suitable for the long term calibration of hydrological models, the interest in using this data is growing. There are, however, several challenges impeding a widespread use of weather radar data. The first being a multitude of different file formats for data storage and exchange. Although the OPERA project [1] has taken steps towards harmonizing the data exchange inside the European radar network [2], different dialects still exist in addition to a large variety of legacy formats. The second barrier is what we would like to describe as the product dilemma. A great number of potential applications also implies a great number of different and often competing requirements as to the quality of the data. As an example, while one user might be willing to accept more false alarms in a clutter detection filter, e.g. to avoid erroneous data assimilation results, another might want a more conservative product in order not to miss the small scale convection that leads to a hundred year flood in a small head catchment. A single product from a radar operator, even if created with the best methods currently available, will never be able to accommodate all these needs simultaneously. Thus the product will either be a compromise or it will accommodate one user more than the other. Often the processing chain needs to be in a specific order, where a change of a certain processing step is impossible without affecting the results of all following steps. Sometimes some post-processing of the product might be possible, but if not, the user's only choice is to either take the product as is or leave it. If a user sho

Question: What is the difference between a radar and a weather radar?


Language processing and learning models for community question answering in Arabic. In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA —from segmentation to constituency parsing— built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.

Question: What is the best language processing toolkit for Arabic?


A Roadmap for Natural Language Processing Research in Information Systems. Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between human and computers. Although many NLP studies have been published, none have comprehensively reviewed or synthesized tasks most commonly addressed in NLP research. We conduct a thorough review of IS literature to assess the current state of NLP research, and identify 12 prototypical tasks that are widely researched. Our analysis of 238 articles in Information Systems (IS) journals between 2004 and 2015 shows an increasing trend in NLP research, especially since 2011. Based on our analysis, we propose a roadmap for NLP research, and detail how it may be useful to guide future NLP research in IS. In addition, we employ Association Rules (AR) mining for data analysis to investigate co-occurrence of prototypical tasks and discuss insights from the findings.

Question: What is the roadmap for natural language processing research in information systems?


Real-time Accurate Object Detection using Multiple Resolutions. We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.

Question: How to detect objects in images?


SIFT-based local spectrogram image descriptor: a novel feature for robust music identification. Music identification via audio fingerprinting has been an active research field in recent years. In the real-world environment, music queries are often deformed by various interferences which typically include signal distortions and time-frequency misalignments caused by time stretching, pitch shifting, etc. Therefore, robustness plays a crucial role in music identification technique. In this paper, we propose to use scale invariant feature transform (SIFT) local descriptors computed from a spectrogram image as sub-fingerprints for music identification. Experiments show that these sub-fingerprints exhibit strong robustness against serious time stretching and pitch shifting simultaneously. In addition, a locality sensitive hashing (LSH)-based nearest sub-fingerprint retrieval method and a matching determination mechanism are applied for robust sub-fingerprint matching, which makes the identification efficient and precise. Finally, as an auxiliary function, we demonstrate that by comparing the time-frequency locations of corresponding SIFT keypoints, the factor of time stretching and pitch shifting that music queries might have experienced can be accurately estimated.

Question: What is the time-frequency location of a SIFT keypoint?


Autonomous Flight for Detection, Localization, and Tracking of Moving Targets With a Small Quadrotor. In this letter, we address the autonomous flight of a small quadrotor, enabling tracking of a moving object. The 15-cm diameter, 250-g robot relies only on onboard sensors (a single camera and an inertial measurement unit) and computers, and can detect, localize, and track moving objects. Our key contributions include the relative pose estimate of a spherical target as well as the planning algorithm, which considers the dynamics of the underactuated robot, the actuator limitations, and the field of view constraints. We show simulation and experimental results to demonstrate feasibility and performance, as well as robustness to abrupt variations in target motion.

Question: How to detect, localize, and track a moving object?


PIXHAWK: A system for autonomous flight using onboard computer vision. We provide a novel hardware and software system for micro air vehicles (MAV) that allows high-speed, low-latency onboard image processing. It uses up to four cameras in parallel on a miniature rotary wing platform. The MAV navigates based on onboard processed computer vision in GPS-denied in- and outdoor environments. It can process in parallel images and inertial measurement information from multiple cameras for multiple purposes (localization, pattern recognition, obstacle avoidance) by distributing the images on a central, low-latency image hub. Furthermore the system can utilize low-bandwith radio links for communication and is designed and optimized to scale to swarm use. Experimental results show successful flight with a range of onboard computer vision algorithms, including localization, obstacle avoidance and pattern recognition.

Question: What is a micro air vehicle?


Building balanced scorecard with SWOT analysis , and implementing " Sun Tzu ' s The Art of Business Management Strategies " on QFD methodology. Article Summary) Conjoining the SWOT matrix with the balanced scorecard (BSC) makes a systematic and holistic strategic management system. The SWOT matrix clearly identifies the critical success factors that can be implemented into the identification of the different aspects toward the balanced scorecard. It is, therefore, a more structural approach in setting up the foundation of the balanced scorecard; instead of simply identifying the key performance indicators (KPI) via gut feeling or by brainstorming. The next step of the whole process is to make use of the quality function deployment (QFD) methodology with the balanced scorecard (BSC) attributes identified as the Whats on the vertical axis, and the major strategies of The Art of Business Management Sun Tzu's as the horizontal Hows axis. The relationships are then studied in the body of the QFD matrix. Consideration is then given as to how the model presented can be customised to allow companies using this approach to develop and implement their corporate business strategic plan. Full Text (5087 words) Copyright MCB UP Limited (MCB) 2000 S.F. Lee: Hong Kong Institute of Vocational Education, Vocational Training Council, HongKong, China Andrew Sai On Ko: International Management Centre, Oxford Brookes, UK 1. Overview Almost every successful manager understands the importance of offering quality service to gain a distinctive advantage over the competition. After all, gaining an advantage is the key to success and even for the sake of survival. However, many of the so-called organizational strategies that companies depend on are not endurable. Why? These strategies may not be well structured and cannot stand the test of time. Nevertheless, powerful organizational tactics cannot easily be imitated; things like a patented algorithm, exclusive right to a special resource, and

Question: What is the difference between a strategy and a tactic?


Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. Abstruct-Most applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volumeldimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst. In this paper, we describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signal-to-noise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels.

Question: How to classify a hyperspectral image?


Go-ICP: Solving 3D Registration Efficiently and Globally Optimally. Registration is a fundamental task in computer vision. The Iterative Closest Point (ICP) algorithm is one of the widely-used methods for solving the registration problem. Based on local iteration, ICP is however well-known to suffer from local minima. Its performance critically relies on the quality of initialization, and only local optimality is guaranteed. This paper provides the very first globally optimal solution to Euclidean registration of two 3D point sets or two 3D surfaces under the L2 error. Our method is built upon ICP, but combines it with a branch-and-bound (BnB) scheme which searches the 3D motion space SE(3) efficiently. By exploiting the special structure of the underlying geometry, we derive novel upper and lower bounds for the ICP error function. The integration of local ICP and global BnB enables the new method to run efficiently in practice, and its optimality is exactly guaranteed. We also discuss extensions, addressing the issue of outlier robustness.

Question: How to solve the registration problem?


Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity. The fingerprinting location method is commonly used in WLAN indoor positioning system. Device diversity (DD) which leads to Received Signal Strength (RSS) value difference between the users' device and the reference device is becoming an increasingly important factor impacting the positioning accuracy. Thus, the device diversity is a key problem gained more and more attention in fingerprinting location system recently, which introduces many uncertainties to the positioning result. Traditionally, the Euclidean distance is widely adopted in fingerprinting method. However, when encountering with RSS value difference caused by device diversity, the localization performance is degraded significantly. Due to this problem, our paper proposes a method employing cosine similarity instead of the Euclidean distance to improve the positioning accuracy about 13.15% higher within 2 meters when device diversity exists in the positioning. The experiment results show that the proposed method presents a good performance without the expenses of computation caused by calibration method which is employed in many previous works.

Question: What is the fingerprinting location method?


Boosting Named Entity Recognition with Neural Character Embeddings. Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking. In this work we propose a language-independent NER system that uses automatically learned features only. Our approach is based on the CharWNN deep neural network, which uses word-level and character-level representations (embeddings) to perform sequential classification. We perform an extensive number of experiments using two annotated corpora in two different languages: HAREM I corpus, which contains texts in Portuguese; and the SPA CoNLL2002 corpus, which contains texts in Spanish. Our experimental results shade light on the contribution of neural character embeddings for NER. Moreover, we demonstrate that the same neural network which has been successfully applied to POS tagging can also achieve state-of-the-art results for language-independet NER, using the same hyperparameters, and without any handcrafted features. For the HAREM I corpus, CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score for the total scenario (ten NE classes), and by 7.2 points in the F1 for the selective scenario (five NE classes).

Question: How does the neural character embeddings help in boosting named entity recognition?


Real-Time or Near Real-Time Persisting Daily Healthcare Data Into HDFS and ElasticSearch Index Inside a Big Data Platform. Mayo Clinic (MC) healthcare generates a large number of HL7 V2 messages—0.7–1.1 million on weekends and 1.7–2.2 million on business days at present. With multiple RDBMS-based systems, such a large volume of HL7 messages still cannot be real-time or near-real-time stored, analyzed, and retrieved for enterprise-level clinic and nonclinic usage. To determine if Big Data technology coupled with ElasticSearch technology can satisfy MC daily healthcare needs for HL7 message processing, a BigData platform was developed to contain two identical Hadoop clusters (TDH1.3.2 version)—each containing an ElasticSearch cluster and instances of a storm topology—MayoTopology for processing HL7 messages on MC ESB queues into an ElasticSearch index and the HDFS. The implemented BigData platform can process 62 ± 4 million HL7 messages per day while the ElasticSearch index can provide ultrafast free-text searching at a speed level of 0.2-s per query on an index containing a dataset of 25 million HL7-derived-JSON-documents. The results suggest that the implemented BigData platform exceeds MC enterprise-level patient-care needs.

Question: How many messages can be processed per day?


Shunt active filter for harmonic and reactive power compensation using p-q theory. Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.

Question: What is the difference between a passive filter and an active filter?


Network Traffic Anomaly Detection. This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today's social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.

Question: What is a tutorial for network anomaly detection?


Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme. Ball and plate system is the extension of traditional ball and beam system. In this paper, a trajectory planning and tracking problem of ball and plate system is put forward to proof-test diverse control schemes. Firstly, we derive the mathematical model of the ball and plate system in detail. Then a hierarchical fuzzy control scheme is proposed to deal with this problem. Our scheme is composed of three levels. The lowest level is a TS type fuzzy tacking controller; the middle level is a fuzzy supervision controller that takes actions in extreme situations; and the top level is a fuzzy planning controller that determines the desired trajectory. In order to optimize the ball's moving trajectory, the genetic algorithm is introduced to adjust the output membership functions of the fuzzy planning controller. Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time. c © 2003 Elsevier B.V. All rights reserved.

Question: How to control a ball and plate system?


Turning the Waiting Room into a Classroom: Weekly Classes Using a Vegan or a Portion-Controlled Eating Plan Improve Diabetes Control in a Randomized Translational Study.. BACKGROUND In research settings, plant-based (vegan) eating plans improve diabetes management, typically reducing weight, glycemia, and low-density lipoprotein (LDL) cholesterol concentrations to a greater extent than has been shown with portion-controlled eating plans.   OBJECTIVE The study aimed to test whether similar benefits could be found using weekly nutrition classes in a typical endocrinology practice, hypothesizing that a vegan eating plan would improve glycemic control, weight, lipid concentrations, blood pressure, and renal function and would do so more effectively than a portion-controlled eating plan.   DESIGN In a 20-week trial, participants were randomly assigned to a low-fat vegan or portion-controlled eating plan.   PARTICIPANTS/SETTING Individuals with type 2 diabetes treated in a single endocrinology practice in Washington, DC, participated (45 starters, 40 completers).   INTERVENTION Participants attended weekly after-hours classes in the office waiting room. The vegan plan excluded animal products and added oils and favored low-glycemic index foods. The portion-controlled plan included energy intake limits for weight loss (typically a deficit of 500 calories/day) and provided guidance on portion sizes.   MAIN OUTCOME MEASURES Body weight, hemoglobin A1c (HbA1c), plasma lipids, urinary albumin, and blood pressure were measured.   STATISTICAL ANALYSES PERFORMED For normally distributed data, t tests were used; for skewed outcomes, rank-based approaches were implemented (Wilcoxon signed-rank test for within-group changes, Wilcoxon two-sample test for between-group comparisons, and exact Hodges-Lehmann estimation to estimate effect sizes).   RESULTS Although participants were in generally good metabolic control at baseline, body weight, HbA1c, and LDL cholesterol improved signific

Question: What is the difference between a vegan and a portion-controlled eating plan?


Healthcare via cell phones: a systematic review.. Regular care and informational support are helpful in improving disease-related health outcomes. Communication technologies can help in providing such care and support. The purpose of this study was to evaluate the empirical evidence related to the role of cell phones and text messaging interventions in improving health outcomes and processes of care. Scientific literature was searched to identify controlled studies evaluating cell phone voice and text message interventions to provide care and disease management support. Searches identified 25 studies that evaluated cell phone voice and text messaging interventions, with 20 randomized controlled trials and 5 controlled studies. Nineteen studies assessed outcomes of care and six assessed processes of care. Selected studies included 38,060 participants with 10,374 adults and 27,686 children. They covered 12 clinical areas and took place in 13 countries. Frequency of message delivery ranged from 5 times per day for diabetes and smoking cessation support to once a week for advice on how to overcome barriers and maintain regular physical activity. Significant improvements were noted in compliance with medicine taking, asthma symptoms, HbA1C, stress levels, smoking quit rates, and self-efficacy. Process improvements were reported in lower failed appointments, quicker diagnosis and treatment, and improved teaching and training. Cost per text message was provided by two studies. The findings that enhancing standard care with reminders, disease monitoring and management, and education through cell phone voice and short message service can help improve health outcomes and care processes have implications for both patients and providers.

Question: What is the cost of a text message?


Performance Analysis of Software-Defined Networking (SDN). Software-Defined Networking (SDN) approaches were introduced as early as the mid-1990s, but just recently became a well-established industry standard. Many network architectures and systems adopted SDN, and vendors are choosing SDN as an alternative to the fixed, predefined, and inflexible protocol stack. SDN offers flexible, dynamic, and programmable functionality of network systems, as well as many other advantages such as centralized control, reduced complexity, better user experience, and a dramatic decrease in network systems and equipment costs. However, SDN characterization and capabilities, as well as workload of the network traffic that the SDN-based systems handle, determine the level of these advantages. Moreover, the enabled flexibility of SDN-based systems comes with a performance penalty. The design and capabilities of the underlying SDN infrastructure influence the performance of common network tasks, compared to a dedicated solution. In this paper we analyze two issues: a) the impact of SDN on raw performance (in terms of throughput and latency) under various workloads, and b) whether there is an inherent performance penalty for a complex, more functional, SDN infrastructure. Our results indicate that SDN does have a performance penalty, however, it is not necessarily related to the complexity level of the underlying SDN infrastructure.

Question: What is the performance penalty of software defined networking?


Smart Cities' Data: Challenges and Opportunities for Semantic Technologies. How can we innovate smart systems for smart cities, to make data available homogeneously, inexpensively, and flexibly while supporting an array of applications that have yet to exist or be specified?

Question: How can we innovate smart systems for smart cities?


A general approach to sizing and power density equations for comparison of electrical machines. Whenever an electrical machine is meant to be fed by a power converter, the design should be approached as a system optimization, more than a simple machine sizing. A great variety of electrical machines is available to accomplish this goal, and the task of comparing the different options can be very difficult. A general purpose sizing equation, easily adjustable for every topology, that could take into account different waveforms and machine characteristics, would be a very desirable tool. In this paper, a general approach is presented to develop and to discuss such an equation. Sample applications of the sizing and power density equations are utilized to compare the induction machine and the doubly salient permanent magnet (DSPM) machine.

Question: How to compare induction and permanent magnet machines?


Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies: A Longitudinal Test of an Evolutionary Model.. The National Longitudinal Study of Adolescent Health data were used to test predictions from life history theory. We hypothesized that (1) in young adulthood an emerging life history strategy would exist as a common factor underlying many life history traits (e.g., health, relationship stability, economic success), (2) both environmental harshness and unpredictability would account for unique variance in expression of adolescent and young adult life history strategies, and (3) adolescent life history traits would predict young adult life history strategy. These predictions were supported. The current findings suggest that the environmental parameters of harshness and unpredictability have concurrent effects on life history development in adolescence, as well as longitudinal effects into young adulthood. In addition, life history traits appear to be stable across developmental time from adolescence into young adulthood.

Question: What is the life history strategy of a young adult?


Odin: Team VictorTango's entry in the DARPA Urban Challenge. The DARPA Urban Challenge required robotic vehicles to travel more than 90 km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and roadblocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, global positioning system, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to

Question: What is the DARPA Urban Challenge?


A study of speaker adaptation for DNN-based speech synthesis. A major advantage of statistical parametric speech synthesis (SPSS) over unit-selection speech synthesis is its adaptability and controllability in changing speaker characteristics and speaking style. Recently, several studies using deep neural networks (DNNs) as acoustic models for SPSS have shown promising results. However, the adaptability of DNNs in SPSS has not been systematically studied. In this paper, we conduct an experimental analysis of speaker adaptation for DNN-based speech synthesis at different levels. In particular, we augment a low-dimensional speaker-specific vector with linguistic features as input to represent speaker identity, perform model adaptation to scale the hidden activation weights, and perform a feature space transformation at the output layer to modify generated acoustic features. We systematically analyse the performance of each individual adaptation technique and that of their combinations. Experimental results confirm the adaptability of the DNN, and listening tests demonstrate that the DNN can achieve significantly better adaptation performance than the hidden Markov model (HMM) baseline in terms of naturalness and speaker similarity.

Question: What is the speaker adaptation for DNN-based speech synthesis?


Alterations of the human gut microbiome in liver cirrhosis. Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.

Question: What is the difference between the gut microbiome in liver cirrhosis and the gut microbiome in healthy individuals?


Exact robot navigation using artificial potential functions. We present a new methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design. Complete information about the freespace and goal is encoded in the form of a special artificial potential function a navigation function that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. Since navigation functions exist for any robot and obstacle course, our methodology is completely general in principle. However, this paper is mainly concerned with certain constructive techniques for a particular class of motion planning problems. Specifically, we present a formula for navigation functions that guide a point-mass robot in a generalized sphere world. The simplest member of this family is a space obtained by puncturing a disc by an arbitrary number of smaller disjoint discs representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation that we show how to build. Our constructions exploit these coordinate transformations to adapt a navigation function on the model space to its more geometrically complicated (but topologically equivalent) instances. The formula that we present admits sphere-worlds of arbitrary dimension and is directly applicable to configuration spaces whose forbidden regions can be modeled by such generalized discs. We have implemented these navigation functions on planar scenarios, and simulation results are provided throughout the paper. Disciplines Robotics Comments Copyright 1992 IEEE. Reprinted from IEEE Transactions on Robotics and Automation, Volume 8, Issue 5, October 1992

Question: What is a generalized sphere world?


On Extracting Structured Knowledge from Unstructured Business Documents. Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-pastemethod, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.

Question: How to extract object instances from unstructured business documents?


Big Data For Development: Applications and Techniques. With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/ algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.

Question: What is the impact of big data on development?


Discovering interpretable geo-social communities for user behavior prediction. Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.

Question: What is the difference between a social community and a social network?


Body Part Detection for Human Pose Estimation and Tracking. Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.

Question: How to detect human body parts?


Automated detection of smuggled high-risk security threats using Deep Learning. The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "small metallic threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).

Question: What is the difference between a convolutional neural network and a bag of words?


Fine-to-Coarse Global Registration of RGB-D Scans. RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a fine-to-coarse global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods.

Question: How to register RGB-D images?


A Middleware for Fast and Flexible Sensor Network Deployment. A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface. The GSN implementation is available from http://globalsn.sourceforge.net/.

Question: What is the name of the middleware?


Rate control for communication networks: shadow prices, proportional fairness and stability. Rate Control for Communication Networks: Shadow Prices, Proportional Fairness and Stability Author(s): F. P. Kelly, A. K. Maulloo and D. K. H. Tan Source:  The Journal of the Operational Research Society, Vol. 49, No. 3 (Mar., 1998), pp. 237-252 Published by: on behalf of the Palgrave Macmillan Journals Operational Research Society Stable URL: http://www.jstor.org/stable/3010473 Accessed: 07-10-2015 21:10 UTC

Question: What is the shadow price?


Defining Digital Forensic Examination and Analysis Tool Using Abstraction Layers. This paper uses the theory of abstraction layers to describe the purpose and goals of digital forensic analysis tools. Using abstraction layers, we identify where tools can introduce errors and provide requirements that the tools must follow. Categories of forensic analysis types are also defined based on the abstraction layers. Abstraction layers are not a new concept, but their usage in digital forensic analysis is not well documented.

Question: What is the purpose of abstraction layers?


Human C-tactile afferents are tuned to the temperature of a skin-stroking caress.. Human C-tactile (CT) afferents respond vigorously to gentle skin stroking and have gained attention for their importance in social touch. Pharmacogenetic activation of the mouse CT equivalent has positively reinforcing, anxiolytic effects, suggesting a role in grooming and affiliative behavior. We recorded from single CT axons in human participants, using the technique of microneurography, and stimulated a unit's receptive field using a novel, computer-controlled moving probe, which stroked the skin of the forearm over five velocities (0.3, 1, 3, 10, and 30 cm s(-1)) at three temperatures (cool, 18 °C; neutral, 32 °C; warm, 42 °C). We show that CTs are unique among mechanoreceptive afferents: they discharged preferentially to slowly moving stimuli at a neutral (typical skin) temperature, rather than at the cooler or warmer stimulus temperatures. In contrast, myelinated hair mechanoreceptive afferents proportionally increased their firing frequency with stroking velocity and showed no temperature modulation. Furthermore, the CT firing frequency correlated with hedonic ratings to the same mechano-thermal stimulus only at the neutral stimulus temperature, where the stimuli were felt as pleasant at higher firing rates. We conclude that CT afferents are tuned to respond to tactile stimuli with the specific characteristics of a gentle caress delivered at typical skin temperature. This provides a peripheral mechanism for signaling pleasant skin-to-skin contact in humans, which promotes interpersonal touch and affiliative behavior.

Question: What is the temperature of a gentle caress?


A randomized trial of two different doses of a SHR-5 Rhodiola rosea extract versus placebo and control of capacity for mental work.. A randomized, double-blind, placebo-controlled, parallel-group clinical study with an extra non-treatment group was performed to measure the effect of a single dose of standardized SHR-5 Rhodiola rosea extract on capacity for mental work against a background of fatigue and stress. An additional objective was to investigate a possible difference between two doses, one dose being chosen as the standard mean dose in accordance with well-established medicinal use as a psychostimulant/adaptogen, the other dose being 50% higher. Some physiological parameters, e.g. pulse rate, systolic and diastolic blood pressure, were also measured. The study was carried out on a highly uniform population comprising 161 cadets aged from 19 to 21 years. All groups were found to have very similar initial data, with no significant difference with regard to any parameter. The study showed a pronounced antifatigue effect reflected in an antifatigue index defined as a ratio called AFI. The verum groups had AFI mean values of 1.0385 and 1.0195, 2 and 3 capsules respectively, whilst the figure for the placebo group was 0.9046. This was statistically highly significant (p < 0.001) for both doses (verum groups), whilst no significant difference between the two dosage groups was observed. There was a possible trend in favour of the lower dose in the psychometric tests. No such trend was found in the physiological tests.

Question: What is the difference between the two doses?


The Flipped Classroom : An Opportunity To Engage Millennial Students Through Active Learning Strategies. "Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.

Question: What is the flipped classroom?


Autophagy as a regulated pathway of cellular degradation.. Macroautophagy is a dynamic process involving the rearrangement of subcellular membranes to sequester cytoplasm and organelles for delivery to the lysosome or vacuole where the sequestered cargo is degraded and recycled. This process takes place in all eukaryotic cells. It is highly regulated through the action of various kinases, phosphatases, and guanosine triphosphatases (GTPases). The core protein machinery that is necessary to drive formation and consumption of intermediates in the macroautophagy pathway includes a ubiquitin-like protein conjugation system and a protein complex that directs membrane docking and fusion at the lysosome or vacuole. Macroautophagy plays an important role in developmental processes, human disease, and cellular response to nutrient deprivation.

Question: What is the role of macroautophagy in human disease?


Network diversity and economic development.. Social networks form the backbone of social and economic life. Until recently, however, data have not been available to study the social impact of a national network structure. To that end, we combined the most complete record of a national communication network with national census data on the socioeconomic well-being of communities. These data make possible a population-level investigation of the relation between the structure of social networks and access to socioeconomic opportunity. We find that the diversity of individuals' relationships is strongly correlated with the economic development of communities.

Question: How does the structure of social networks affect the economic development of communities?


Kullback-Leibler Divergence Constrained Distributionally Robust Optimization. In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be formulated as a one-layer convex minimization problem. We also consider DRO problems where the ambiguity is in the constraint. We show that ambiguous expectation-constrained programs may be reformulated as a one-layer convex optimization problem that takes the form of the Benstein approximation of Nemirovski and Shapiro (2006). We further consider distributionally robust probabilistic programs. We show that the optimal solution of a probability minimization problem is also optimal for the distributionally robust version of the same problem, and also show that the ambiguous chance-constrained programs (CCPs) may be reformulated as the original CCP with an adjusted confidence level. A number of examples and special cases are also discussed in the paper to show that the reformulated problems may take simple forms that can be solved easily. The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.

Question: What is the Kullback-Leibler divergence?


Dual-Resonance NFC Antenna System Based on NFC Chip Antenna. In this letter, to enhance the performance of the near-field communication (NFC) antenna, an antenna system that has dual resonance is proposed. The antenna is based on NFC chip antenna, and the dual resonance comes from the chip antenna itself, the eddy current on printed circuit board, and a nearby loop that has strong coupling with the chip antenna. The performance of the proposed antenna system is confirmed by measurement.

Question: What is the performance of the proposed antenna system?


Publicity Trends in Arrests of " Online Predators " How the National Juvenile Online Victimization (n‐jov) Study Was Conducted. about " online predators " * – sex of‐ fenders who use the Internet to meet juvenile victims – has raised considerable alarm about the extent to which Internet use may be put‐ ting children and adolescents at risk for sexual abuse and exploitation. Media stories and Internet safety messages have raised fears by describing violent offenders who use the Inter‐ net to prey on naïve children by tricking them into face‐to‐face meetings or tracking them down through information posted online. Law enforcement has mobilized on a number of fronts, setting up task forces to identify and prosecute online predators, developing under‐ cover operations, and urging social networking sites to protect young users. Unfortunately, however, reliable information on the scope and nature of the online predator problem remains scarce. Established criminal justice data collection systems do not gather detailed data on such crimes that could help inform public policy and education. To remedy this information vacuum, the Crimes against Children Research Center at the University of New Hampshire conducted two waves of a The N‐JOV Study collected information from a national sample of law en‐ forcement agencies about the prevalence of arrests for and characteristics of online sex crimes against minors during two 12 month periods: July 1, 2000 through June 30, 2001 (Wave 1) and calendar year 2006 (Wave 2). For both Waves, we used a two‐phase process of mail surveys followed by telephone interviews to collect data from a national sample of the same lo‐ cal, county, state, and federal law enforcement agencies. First, we sent the mail surveys to a national sample of more than 2,500 agencies. These sur‐ veys asked if agencies had made arrests for online sex crimes against minors during the respective one‐year timeframes. Then we conducted detailed telephone interviews with l

Question: How many arrests were made for online sex crimes against minors?


Changes in women's choice of dress across the ovulatory cycle: naturalistic and laboratory task-based evidence.. The authors tested the prediction that women prefer clothing that is more revealing and sexy when fertility is highest within the ovulatory cycle. Eighty-eight women reported to the lab twice: once on a low-fertility day of the cycle and once on a high-fertility day (confirmed using hormone tests). In each session, participants posed for full-body photographs in the clothing they wore to the lab, and they drew illustrations to indicate an outfit they would wear to a social event that evening. Although each data source supported the prediction, the authors found the most dramatic changes in clothing choice in the illustrations. Ovulatory shifts in clothing choice were moderated by sociosexuality, attractiveness, relationship status, and relationship satisfaction. Sexually unrestricted women, for example, showed greater shifts in preference for revealing clothing worn to the laboratory near ovulation. The authors suggest that clothing preference shifts could reflect an increase in female-female competition near ovulation.

Question: What is the difference between a laboratory task and a naturalistic task?


Energy efficient wearable sensor node for IoT-based fall detection systems. Falls can cause serious traumas such as brain injuries and bone fractures, especially among elderly people. Fear of falling might reduce physical activities resulting in declining social interactions and eventually causing depression. To lessen the effects of a fall, timely delivery of medical treatment can play a vital role. In a similar scenario, an IoT-based wearable system can pave the most promising way to mitigate serious consequences of a fall while providing the convenience of usage. However, to deliver sufficient degree of monitoring and reliability, wearable devices working at the core of fall detection systems are required to work for a prolonged period of time. In this work, we focus on energy efficiency of a wearable sensor node in an Internet-of-Things (IoT) based fall detection system. We propose the design of a tiny, lightweight, flexible and energy efficient wearable device. We investigate different parameters (e.g. sampling rate, communication bus interface, transmission protocol, and transmission rate) impacting on energy consumption of the wearable device. In addition, we provide a comprehensive analysis of energy consumption of the wearable in different configurations and operating conditions. Furthermore, we provide hints (hardware and software) for system designers implementing the optimal wearable device for IoT-based fall detection systems in terms of energy efficiency and high quality of service. The results clearly indicate that the proposed sensor node is novel and energy efficient. In a critical condition, the wearable device can be used continuously for 76 h with a 1000 mAh li-ion battery.

Question: What is the size of the canadian military?


Design and implementation of 0.7 to 7 GHz broadband double-ridged horn antenna. In this paper, simulation and measurement results of 0.7-7 GHz double-ridged guide horn (DRGH) antenna with coaxial input feed section is presented. This antenna, due to the large frequency band required by standards, is appropriate to be used in electromagnetic compatibility (EMC) testing and antenna measurement as a transmitter. A step by step method for designing DRGH antenna is given. A suitable taper for the ridges in the horn is designed and its impedance variations along the horn are shown. In addition, a new structure for the electrical field probe in the feed section is introduced by which a shift-down of lower frequency to 0.7 GHz is achieved. A sensitivity analysis is done on the parameters of the proposed structure. Other parameters of the feed section are also investigated and optimized values are obtained. Finally, the proposed antenna has been fabricated and measurement results show good agreement with the simulation.

Question: How to design a double-ridged horn antenna?


Introducing 'Bones': a parallelizing source-to-source compiler based on algorithmic skeletons. Recent advances in multi-core and many-core processors requires programmers to exploit an increasing amount of parallelism from their applications. Data parallel languages such as CUDA and OpenCL make it possible to take advantage of such processors, but still require a large amount of effort from programmers.  A number of parallelizing source-to-source compilers have recently been developed to ease programming of multi-core and many-core processors. This work presents and evaluates a number of such tools, focused in particular on C-to-CUDA transformations targeting GPUs. We compare these tools both qualitatively and quantitatively to each other and identify their strengths and weaknesses.  In this paper, we address the weaknesses by presenting a new classification of algorithms. This classification is used in a new source-to-source compiler, which is based on the algorithmic skeletons technique. The compiler generates target code based on skeletons of parallel structures, which can be seen as parameterisable library implementations for a set of algorithm classes. We furthermore demonstrate that the presented compiler requires little modifications to the original sequential source code, generates readable code for further fine-tuning, and delivers superior performance compared to other tools for a set of 8 image processing kernels.

Question: What is a source-to-source compiler?


Corpora – the best possible solution for tracking rare phenomena in underresourced languages : clitics in Bosnian , Croatian and Serbian. Complex linguistic phenomena, such as Clitic Climbing in Bosnian, Croatian and Serbian, are often described intuitively, only from the perspective of the main tendency. In this paper, we argue that web corpora currently offer the best source of empirical material for studying Clitic Climbing in BCS. They thus allow the most accurate description of this phenomenon, as less frequent constructions can be tracked only in big, well-annotated data sources. We compare the properties of web corpora for BCS with traditional sources and give examples of studies on CC based on web corpora. Furthermore, we discuss problems related to web corpora and suggest some improvements for

Question: What is the best source of empirical material for studying clitics in Bosnian, Croatian and Serbian?


VMVM: unit test virtualization for Java. As software evolves and grows, its regression test suites tend to grow as well. When these test suites become too large, they can eventually reach a point where they become too length to regularly execute. Previous work in Test Suite Minimization has reduced the number of tests in such suites by attempting to identify those that are redundant (e.g. by a coverage metric). Our approach to ameliorating the runtime of these large test suites is complementary, instead focusing on reducing the overhead of running each test, an approach that we call Unit Test Virtualization. This Tool Demonstration presents our implementation of Unit Test Virtualization, VMVM (pronounced "vroom-vroom") and summarizes an evaluation of our implementation on 20 real-world Java applications, showing that it reduces test suite execution time by up to 97% (on average, 62%). A companion video to this demonstration is available online, at https://www.youtube.com/watch?v=sRpqF3rJERI.

Question: How does unit test virtualization work?


Recognizing Objects in Range Data Using Regional Point Descriptors. Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

Question: What is the name of the regional shape descriptor?


CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection. Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the CnnRnn Earthquake Detector (CRED), a detector based on deep neural networks. The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the timefrequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with a F-score of 99.95. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.

Question: What is a deep residual network?


Neural Map: Structured Memory for Deep Reinforcement Learning. A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work (Oh et al., 2016) has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training.

Question: What is a memory system?


Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.

Question: How to map user attributes to latent features of a matrix factorization model?