A 100 watts L-band power amplifier. In this paper, a 1100–1350 MHz power amplifier is introduced. For the realization of the RF power amplifier an LDMOS transistor is used. The amplifier delivers 100W power to a 50 ohm load with 50% efficiency. The transducer power gain of the amplifier is has 16dB at 100W output power. A mixed lumped element and transmission line matching circuit is used to implement matching network. The simulations are performed by the nonlinear model of the transistor, which is provided by the manufacturer. Finally the results of simulations are compared with the measurements.

Question: What is the power amplifier?


A Real Time Audio Fingerprinting System for Advertisement Tracking and Reporting in FM Radio. In this paper we present a system designed to detect, identify and track commercial segments transmitted by a radio station. The program is entirely written in Visual C++ and uses state of the art audio fingerprinting technologies to achieve a great performance, being able to operate several times faster than real time while keeping a moderate computational load.

Question: What is a real time audio fingerprinting system?


Knowledge sharing in organisational contexts: a motivation-based perspective. Purpose – Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing. Design/methodology/approach – The analysis builds on a three-category taxonomy of motivation, adding ''hedonic'' motivation to the traditional dichotomy of ''extrinsic'' and ''intrinsic'' motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge-intensive organisations: professional bureaucracy and operating adhocracy. Findings – Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdependent teamwork is vital, it may be overcome through normative alignment reinforced by intensive socialisation. Extrinsic motivators that align with hedonic motivation may also reinforce the propensity for knowledge sharing. In both organisational types, financial extrinsic incentives do not appear to be relevant on their own, and may ''crowd out'' other motivators. Research limitations/implications – The cases reported were chosen from the existing literature and, although many were not designed specifically to address motivational issues, suggestive conclusions are drawn. Most of the cases were drawn from organisations rooted in the Anglo-American context and thus care would be needed in generalising the findings to organisations in other contexts. Originality/value – The paper represents the first attempt to apply a three-category taxonomy of motivation to examine knowledge-shar

Question: What is the difference between knowledge sharing and knowledge transfer?


Analysis of Semipermeable Containment Sleeve Technology for High-Speed Permanent Magnet Machines. This paper presents an alternative permanent magnet rotor containment technology. The use of a semipermeable material in a laminated structure is shown to result in a significant improvement in magnetic loading for a given thickness of containment compared to the use of magnetically inert materials such as carbon fiber or Inconel, while minimizing eddy current losses in the containment layer. An analytical model is presented for determining the air-gap field delivered from a permanent magnet array with a semipermeable containment. The analysis is validated through finite element analysis. The fabrication of a prototype machine is detailed and the results presented show good agreement with the analysis. The validated modeling process is used to assess the potential of the new technology.

Question: What is the difference between a permanent magnet and a magnet?


Dual-Polarized Broadband Array Antenna With BOR-Elements, Mechanical Design and Measurements. A dual polarized broadband phased array antenna designed for the frequency range 6-18 GHz, a 45deg conical grating lobe free scan volume and equipped with BOR-elements developed by Saab is presented. The aim with this array element is to bring about a dual polarized broadband array antenna that is easy to assemble, disassemble and connect to active microwave modules. Disassembling may be important for maintenance and upgrade reasons. Mechanical design and electromagnetic performance in the form of active reflection coefficient, calculated from measured mutual coupling coefficients, and measured active gain element pattern for a central and an edge element is presented. Edge effects in the array, which may be severe in small broadband arrays, are considered in this paper

Question: What is a dual polarized broadband phased array antenna?


An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition. Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.

Question: What is the incremental framework for video-based traffic sign detection, tracking, and recognition?


An information-centric energy infrastructure: The Berkeley view. We describe an approach for how to design an essentially more scalable, flexible and resilient electric power infrastructure – one that encourages efficient use, integrates local generation, and manages demand through omnipresent awareness of energy availability and use over time. We are inspired by how the Internet has revolutionized communications infrastructure, by pushing intelligence to the edges while hiding the diversity of underlying technologies through well-defined interfaces. Any end device is a traffic source or sink and intelligent endpoints adapt their traffic to what the infrastructure can

Question: What is the Berkeley view?


Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition. Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.

Question: What is the state of the art in fine-grained car recognition?


Question: What is the average effect size of small-group learning on undergraduates in science, mathematics, engineering, and technology?


Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion.. We used positron emission tomography to study neural mechanisms underlying intensely pleasant emotional responses to music. Cerebral blood flow changes were measured in response to subject-selected music that elicited the highly pleasurable experience of "shivers-down-the-spine" or "chills." Subjective reports of chills were accompanied by changes in heart rate, electromyogram, and respiration. As intensity of these chills increased, cerebral blood flow increases and decreases were observed in brain regions thought to be involved in reward/motivation, emotion, and arousal, including ventral striatum, midbrain, amygdala, orbitofrontal cortex, and ventral medial prefrontal cortex. These brain structures are known to be active in response to other euphoria-inducing stimuli, such as food, sex, and drugs of abuse. This finding links music with biologically relevant, survival-related stimuli via their common recruitment of brain circuitry involved in pleasure and reward.

Question: What is the difference between pleasure and reward?


Dissecting Android Malware: Characterization and Evolution. The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.

Question: What is the best case detection rate of mobile anti-virus software?


The INTERSPEECH 2017 Computational Paralinguistics Challenge: Addressee, Cold & Snoring. The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from 'healthy' speech; and in the Snoring subchallenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audiowords for the first time in the challenge series.

Question: What is the cold sub-challenge?


Single-structure 3-axis lorentz force magnetometer with sub-30 nT/√HZ resolution. This work demonstrates a 3-axis Lorentz force magnetometer for electronic compass purposes. The magnetometer measures magnetic flux in 3 axes using a single structure. With 1 mW power consumption, the sensor achieves sub-30 nT/√Hz resolution in each of the 3 axes. Compared to the 3-axis Hall sensors currently used in smartphones, the 3-axis magnetometer shown here has the advantages of 10× lower noise floor and the ability to be co-fabricated with MEMS inertial sensors.

Question: What is a 3-axis Lorentz force magnetometer?


The State of Public Infrastructure-as-a-Service Cloud Security. The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of "best-practices," while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.

Question: What are the security mechanisms offered by the cloud services?


CREDIT CARD FRAUD DETECTION BASED ON BEHAVIOR MINING. NIMISHA PHILIP, SHERLY K.K Department of Computer Science & Engineering, Toc H Institute of Science & Technology email:nimishavellapally@gmail.com Department of Information Technology, Toc H Institute of Science & Technology, Ernakulam, Kerala, India shrly_shilu@yahoo.com Abstract — Globalization and increased use of the Internet for online shopping has resulted in a considerable proliferation of credit card transactions throughout the world.Higher acceptability and convenience of credit cards for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by adversaries. Most of the well known algorithms for fraud detection are based on supervised training Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing FDS try to capture behavioral patterns as rules which are static .This becomes ineffective when cardholder develops new patterns of behavior Here, we propose a unsupervised method to dynamically profile behavior pattern of customer Then the incoming transactions are compared against the user profile to indicate the anomalies, based on which the corresponding warnings are outputted. A FP tree based pattern matching algorithm is used to evaluate how unusual the new transactions are.

Question: How to detect credit card fraud?


Box Pleating is Hard. In their seminal 1996 paper, Bern and Hayes initiated investigation into the computational complexity of origami [3]. They proved that it is NP-hard to determine whether a given general crease pattern can be folded flat, when the creases have or have not been assigned crease directions (mountain fold or valley fold). Since that time, there has been considerable work in analyzing the computational complexity of other origami related problems. For example, Arkin et al. [1] proved that deciding foldability is hard even for simple folds, while Demaine et al. [4] proved that optimal circle packing for origami design is also hard. At the end of their paper, Bern and Hayes pose some interesting open questions to further their work. While most of them have been investigated since, two in particular (problems 2 and 3) have remained untouched until now. First, while the gadgets used in their hardness proof for unassigned crease patterns are relatively straightforward, their gadgets for assigned crease patterns are considerably more convoluted, and quite difficult to check. In particular, we found an error in their crossover gadget where signals are not guaranteed to transmit dependably for wires that do not cross orthogonally, which is required in their construction. Is there a simpler way to achieve a correct result (i.e. "without tabs")? Second, their reductions construct creases at a variety of unconstrained angles. Is deciding flat foldability easy under more restrictive inputs? For example box pleating, folding only along creases aligned at multiples of 45◦ to each other, is a subset of particular interest in transformational robotics and selfassembly, with a universality result constructing arbitrary polycubes using box pleating [2]. In this paper we prove deciding flat foldability of box pleated crease patterns to be NP-hard in both the unassigned and assigned cases, using relatively simple gadgets containing no more than 20 layers at any point. A

Question: What is the complexity of deciding flat foldability of box pleated crease patterns?


Interactive multiple anisotropic scattering in clouds. We propose an algorithm for the real time realistic simulation of multiple anisotropic scattering of light in a volume. Contrary to previous real-time methods we account for all kinds of light paths through the medium and preserve their anisotropic behavior.  Our approach consists of estimating the energy transport from the illuminated cloud surface to the rendered cloud pixel for each separate order of multiple scattering. We represent the distribution of light paths reaching a given viewed cloud pixel with the mean and standard deviation of their entry points on the lit surface, which we call the collector area. At rendering time for each pixel we determine the collector area on the lit cloud surface for different sets of scattering orders, then we infer the associated light transport. The fast computation of the collector area and light transport is possible thanks to a preliminary analysis of multiple scattering in plane-parallel slabs and does not require slicing or marching through the volume.  Rendering is done efficiently in a shader on the GPU, relying on a cloud surface mesh augmented with a Hypertexture to enrich the shape and silhouette. We demonstrate our model with the interactive rendering of detailed animated cumulus and cloudy sky at 2--10 frames per second.

Question: How to simulate multiple scattering in clouds?


Guidance for the evaluation and treatment of hereditary and acquired thrombophilia. Thrombophilias are hereditary and/or acquired conditions that predispose patients to thrombosis. Testing for thrombophilia is commonly performed in patients with venous thrombosis and their relatives; however such testing usually does not provide information that impacts management and may result in harm. This manuscript, initiated by the Anticoagulation Forum, provides clinical guidance for thrombophilia testing in five clinical situations: following 1) provoked venous thromboembolism, 2) unprovoked venous thromboembolism; 3) in relatives of patients with thrombosis, 4) in female relatives of patients with thrombosis considering estrogen use; and 5) in female relatives of patients with thrombosis who are considering pregnancy. Additionally, guidance is provided regarding the timing of thrombophilia testing. The role of thrombophilia testing in arterial thrombosis and for evaluation of recurrent pregnancy loss is not addressed. Statements are based on existing guidelines and consensus expert opinion where guidelines are lacking. We recommend that thrombophilia testing not be performed in most situations. When performed, it should be used in a highly selective manner, and only in circumstances where the information obtained will influence a decision important to the patient, and outweigh the potential risks of testing. Testing should not be performed during acute thrombosis or during the initial (3-month) period of anticoagulation.

Question: What is the role of thrombophilia testing in arterial thrombosis?


Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research. With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

Question: What is the current trend in cloud-based big data management and analytics for scholarly resources?


Can Active Memory Replace Attention?. Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation. Recently, similar improvements have been obtained using alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling. So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.

Question: What is the difference between attention and active memory?


Lecture Notes on Network Information Theory. These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/.

Question: What is the book about?


A modified particle swarm optimizer. In this paper, we introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the signilicant and effective impact of this new parameter on the particle swarm optimizer.

Question: What is a particle swarm optimizer?


A Literature Review and Classification of Recommender Systems on Academic Journals. Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid‐1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "

Question: What is the difference between a recommender system and a recommendation system?


3-Dimensional Analysis on the GIDL Current of Body-tied Triple Gate FinFET. Triple gate FinFET is emerging as a promising candidate for the future CMOS device structures because of its immunity to short-channel effect. However, the suppression of GIDL is a significant challenge for its application. In this paper, we discuss the characteristics of GIDL on FinFET and extensively analyze the influence of the device technology on GIDL. The analysis is expected to give guidelines to the future development of triple gate FinFET

Question: What is the GIDL current of body-tied triple gate FinFET?


Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.

Question: What is the difference between machine learning and artificial intelligence?


Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices. Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. In this paper, we propose two methods for reducing the localization error in indoor non-line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can also be used on computationally restricted devices.

Question: How to improve indoor localization using convolutional neural networks?


Anomaly Detection using Autoencoders in High Performance Computing Systems. Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

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


Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey. Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

Question: What is a wireless sensor network?


An Ultra Low Energy 12-bit Rate-Resolution Scalable SAR ADC for Wireless Sensor Nodes. A resolution-rate scalable ADC for micro-sensor networks is described. Based on the successive approximation register (SAR) architecture, this ADC has two resolution modes: 12 bit and 8 bit, and its sampling rate is scalable, at a constant figure-of-merit, from 0-100 kS/s and 0-200 kS/s, respectively. At the highest performance point (i.e., 12 bit, 100 kS/s), the entire ADC (including digital, analog, and reference power) consumes 25 muW from a 1-V supply. The ADC's CMRR is enhanced by common-mode independent sampling and passive auto-zero reference generation. The efficiency of the comparator is improved by an analog offset calibrating latch, and the preamplifier settling time is relaxed by self-timing the bit-decisions. Prototyped in a 0.18-mum, 5M2P CMOS process, the ADC, at 12 bit, 100 kS/s, achieves a Nyquist SNDR of 65 dB (10.55 ENOB) and an SFDR of 71 dB. Its INL and DNL are 0.68 LSB and 0.66 LSB, respectively

Question: What is the resolution of the ADC?


Reaching Agreement in the Presence of Faults. The problem addressed here concerns a set of isolated processors, some unknown subset of which may be faulty, that communicate only by means of two-party messages. Each nonfaulty processor has a private value of information that must be communicated to each other nonfaulty processor. Nonfaulty processors always communicate honestly, whereas faulty processors may lie. The problem is to devise an algorithm in which processors communicate their own values and relay values received from others that allows each nonfaulty processor to infer a value for each other processor. The value inferred for a nonfaulty processor must be that processor's private value, and the value inferred for a faulty one must be consistent with the corresponding value inferred by each other nonfaulty processor. It is shown that the problem is solvable for, and only for, n ≥ 3m + 1, where m is the number of faulty processors and n is the total number. It is also shown that if faulty processors can refuse to pass on information but cannot falsely relay information, the problem is solvable for arbitrary n ≥ m ≥ 0. This weaker assumption can be approximated in practice using cryptographic methods.

Question: What is the smallest number of processors that can be used to solve this problem?


Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric. We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.

Question: What is the minimum string distance?


Java-based home automation system. This paper presents the design and implementation of a Java-based automation system that can monitor and control home appliances via the World Wide Web. The design is based on a stand alone embedded system board integrated into a PC-based server at home. The home appliances are connected to the input/output ports of the embedded system board and their status are passed to the server. The monitoring and control software engine is based on the combination of JavaServer pages, JavaBeans, and interactive C. The home appliances can be monitored and controlled locally via the embedded system board, or remotely through a Web browser from anywhere in the world provided that an Internet access is available. The system is scalable and allows multi-vendor appliances to be added with no major changes to its core. Password protection is used to block unauthorized users from accessing the appliances at home. If the Internet connection is down or the server is not up, the embedded system board still can control and operate the appliances locally.

Question: What is a Java-based home automation system?


Hardware Covert Attacks and Countermeasures. Computing platforms deployed in many critical infrastructures, such as smart grid, financial systems, governmental organizations etc., are subjected to security attacks and potentially devastating consequences. Computing platforms often get attacked 'physically' by an intruder accessing stored information, studying the internal structure of the hardware or injecting a fault. Even if the attackers fail to gain sensitive information stored in hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Hardware attacks could be covert or overt, based on the awareness of the intended system. This work classifies existing hardware attacks. Focusing mainly on covert attacks, they are quantified using a proposed schema. Different countermeasure techniques are proposed to prevent such attacks.

Question: What is the difference between covert and overt attacks?


Part Detector Discovery in Deep Convolutional Neural Networks. Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB2002011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_ discovery and https://github.com/cvjena/PartDetectorDisovery.

Question: How to detect parts in images?


Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor. To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains the vehicle's control system should have settings for individual terrain surfaces. A first step in such a terrain-dependent control system is classification of the surface upon which the AGV is traversing. This paper considers vision-based terrain surface classification for the path directly in front of the vehicle (≪ 1 m). Most visionbased terrain classification has focused on terrain traversability and not on terrain surface classification. The few approaches to classifying traversable terrain surfaces, with the exception of the use of infrared cameras to classify mud, have relied on stand-alone cameras that are designed for daytime use and are not expected to perform well in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a laser in conjunction with a camera, and hence can work at night. Also, unlike most previous results, the classification here does not rely on color since color changes with illumination and weather, and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain surface classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.

Question: How does a laser stripe-based structured light sensor work?


HermitCore: A Unikernel for Extreme Scale Computing. We expect that the size and the complexity of future supercomputers will increase on their path to exascale systems and beyond. Therefore, system software has to adapt to the complexity of these systems for a simplification of the development of scalable applications. In this paper, we present a unikernel operating system design for HPC. It extends the multi-kernel approach while providing better programmability and scalability for hierarchical systems, such as HLRS' Hazel Hen, which base on multiple cluster-on-a-chip processors. We prove the scalability of the design via micro benchmarks by taking the example of HermitCore---our prototype implementation of the new design.

Question: What is a unikernel?


Novel Video Prediction for Large-scale Scene using Optical Flow. Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.

Question: How to make predictions of future frames in complex urban scenes?


Image description with features that summarize. We present a new method for describing images for the purposes of matching and registration. We take the point of view that large, coherent regions in the image provide a concise and stable basis for image description. We develop a new algorithm for feature detection that operates on several projections (feature spaces) of the image using kernel-based optimization techniques to locate local extrema of a continuous scale-space of image regions. Descriptors of these image regions and their relative geometry then form the basis of an image description. The emphasis of the work is on features that summarize image content and are highly robust to viewpoint changes and occlusion yet remain discriminative for matching and registration. We present experimental results of these methods applied to the problem of image retrieval. We find that our method performs comparably to two published techniques: Blobworld and SIFT features. However, compared to these techniques two significant advantages of our method are its (1) stability under large changes in the images and (2) its representational efficiency. 2008 Elsevier Inc. All rights reserved.

Question: What is the difference between the methods?


Breast cancer intrinsic subtype classification, clinical use and future trends.. Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.

Question: What is the intrinsic subtype classification of breast cancer?


Causes and consequences of microRNA dysregulation in cancer. Over the past several years it has become clear that alterations in the expression of microRNA (miRNA) genes contribute to the pathogenesis of most — if not all — human malignancies. These alterations can be caused by various mechanisms, including deletions, amplifications or mutations involving miRNA loci, epigenetic silencing or the dysregulation of transcription factors that target specific miRNAs. Because malignant cells show dependence on the dysregulated expression of miRNA genes, which in turn control or are controlled by the dysregulation of multiple protein-coding oncogenes or tumour suppressor genes, these small RNAs provide important opportunities for the development of future miRNA-based therapies.

Question: What is the cause of microRNA dysregulation in cancer?


Fusion of AV features and external information sources for event detection in team sports video. The use of AV features alone is insufficient to induce high-level semantics. This article proposes a framework that utilizes both internal AV features and various types of external information sources for event detection in team sports video. Three schemes are also proposed to tackle the asynchronism between the fusion of AV and external information. The framework is extensible as it can provide increasing functionalities given more detailed external information and domain knowledge. By demonstrating its effectiveness on soccer and American football, we believe that with the availability of appropriate domain knowledge, the framework is applicable to other team sports.

Question: How to detect events in team sports video?


Sentiment Classification based on Latent Dirichlet Allocation. Opinion miningrefers to the use of natural language processing, text analysis and computational linguistics to identify and extract the subjective information. Opinion Mining has become an indispensible part of online reviews which is in the present scenario. In the field of information retrieval, a various kinds of probabilistic topic modeling techniques have been used to analyze contents present in a document. A topic model is a generative technique for document. All

Question: What is a topic model?


An Infrastructure Approach to Context-Aware Computing. The Context Toolkit (Dey, Salber, and Abowd 2001 [this special issue]) is only one of many possible architectures for supporting context-aware applications. In this essay, we look at the trade-offs involved with a service infrastructure approach to context-aware computing. We describe the advantages that a service infrastructure for contextawareness has over other approaches, outline some of the core technical challenges that must be addressed before such an infrastructure can be built, and point out promising research directions for overcoming these challenges.

Question: What is the trade-off between a service infrastructure and a context-aware application?


A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures. As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.

Question: What is the SPEC RG Cloud Group?


Emotion classification using minimal EEG channels and frequency bands. In this research we propose to use EEG signal to classify two emotions (i.e., positive and negative) elicited by pictures. With power spectrum features, the accuracy rate of SVM classifier is about 85.41%. Considering each pair of channels and different frequency bands, it shows that frontal pairs of channels give a better result than the other area and high frequency bands give a better result than low frequency bands. Furthermore, we can reduce number of pairs of channels from 7 to 5 with almost the same accuracy and can cut low frequency bands in order to save computation time. All of these are beneficial to the development of emotion classification system using minimal EEG channels in real-time.

Question: How to classify emotions using minimal EEG channels?


Rigor mortis in an unusual position: Forensic considerations. We report a case in which the dead body was found with rigor mortis in an unusual position. The dead body was lying on its back with limbs raised, defying gravity. Direction of the salivary stains on the face was also defying the gravity. We opined that the scene of occurrence of crime is unlikely to be the final place where the dead body was found. The clues were revealing a homicidal offence and an attempt to destroy the evidence. The forensic use of 'rigor mortis in an unusual position' is in furthering the investigations, and the scientific confirmation of two facts - the scene of death (occurrence) is different from the scene of disposal of dead body, and time gap between the two places.

Question: What is the forensic use of 'rigor mortis in an unusual position'?


Tiny ImageNet Visual Recognition Challenge. In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified activation unit type and dataset noise on its accuracy in Tiny-ImageNet Challenge settings. In order to make a thorough evaluation of the cause of the peformance improvement, we start with a basic 5 layer model with 5×5 convolutional receptive fields. We keep increasing network depth or reducing receptive field size, and continue applying modern techniques, such as PReLu and dropout, to the model. Our model achieves excellent performance even compared to state-of-the-art results, with 0.444 final error rate on the test set.

Question: What is the effect of convolutional network depth on accuracy?


Blockchain-based sharing services : What blockchain technology can contribute to smart cities. Background: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word "smart" and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. Methods: By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Results: Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. Conclusions: This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.

Question: What is a smart city?


Principles of spatial database analysis and design. This chapter covers the fundamentals of spatial database analysis and design, It begins by defining the most important concepts: 'spatial database', 'analysis', 'design', and 'model'; and continues with a presentation of the rationale supporting the use of formal methods for analysis and design. The basic elements and approaches of such methods are described, in addition to the processes used. Emphasis is placed on the particularities of spatial databases and the improvements needed for non-spatial methods and tools in order to enhance their efficiency. Finally, the chapter presents a set of tools, called CASE (computer-assisted software engineering), which are built to support the formal analysis and design methods.

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


Predicting retweet count using visual cues. Social media platforms allow rapid information diffusion, and serve as a source of information to many of the users. Particularly, in Twitter information provided by tweets diffuses over the users through retweets. Hence, being able to predict the retweet count of a given tweet is important for understanding and controlling information diffusion on Twitter. Since the length of a tweet is limited to 140 characters, extracting relevant features to predict the retweet count is a challenging task. However, visual features of images linked in tweets may provide predictive features. In this study, we focus on predicting the expected retweet count of a tweet by using visual cues of an image linked in that tweet in addition to content and structure-based features.

Question: How to predict the retweet count of a tweet?


An Algorithm for Differential File Comparison. The programdiff reports differences between tw o files, expressed as a minimal list of line changes to bring either file into agreement with the other . Diff has been engineered to mak e efficient use of time and space on typical inputs that arise in vetting v ersion-to-version changes in computer-maintained or computer-generated documents. T ime and space usage are observed to v ary about as the sum of the file lengths on real data, although they are known to vary as the product of the file lengths in the worst case. The central algorithm of diff solves the 'longest common subsequence problem' to find the lines that do not change between files. Practical ef ficiency is gained by attending only to certain critical 'candidate' matches between the files, the breaking of which would shorten the longest subsequence common to some pair of initial segments of the two files. Various techniques of hashing, presorting into equi valence classes, merging by binary search, and dynamic storage allocation are used to obtain good performance. [This document was scanned from Bell Laboratories Computing Science T e hnical Report #41, dated July 1976. Te xt was converted by OCR and hand-corrected (last changed June, 2012). Figures were reconstructed. Some OCR errors may remain, especially in tables and equations. Please report them to doug@cs.dartmouth.edu.] The programdiff creates a list of what lines of one file ha ve to be changed to bring it into agreement with a second file or vice versa. It is based on ideas from se veral sources[1,2,7,8]. As an example of its work, consider the two files, listed horizontally for brevity: a b c d e f g w a b x y z e It is easy to see that the first file can be made into the second by the follo wing prescription, in which an imaginary line 0 is understood at the beginning of each: append after line 0: w, change lines 3 through 4, which were: c d into: x y z, delete lines 6 through 7, which were: f g

Question: What is the longest common subsequence of the two files?


Image annotation via deep neural network. Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.

Question: What is the NUS-WIDE dataset?


Interactive Attention Networks for Aspect-Level Sentiment Classification. Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.

Question: What is the difference between aspect-level sentiment classification and sentiment classification?


Anaphoric Annotation in the ARRAU Corpus. Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.

Question: What is the anaphoric annotation in the arrau corpus?


Knowledge sharing in open source software communities: motivations and management. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Question: What is the difference between copyright and moral rights?


Hybrid analog-digital backscatter: A new approach for battery-free sensing. After comparing the properties of analog backscatter and digital backscatter, we propose that a combination of the two can provide a solution for high data rate battery free wireless sensing that is superior to either approach on its own. We present a hybrid analog-digital backscatter platform that uses digital backscatter for addressability and control but switches into analog backscatter mode for high data rate transmission of sensor data. Using hybrid backscatter, we report the first digitally addressable real-time battery free wireless microphone. We develop the hybrid backscatter platform by integrating an electret microphone and RF switch with a digital RFID platform (WISP). The hybrid WISP operates by default in digital mode, transmitting and receiving digital data using the EPC Gen 2 RFID protocol but switching into analog mode to backscatter audio sensor data when activated by Gen 2 READ command. The data is recovered using a USRP-based Software Defined RFID reader. We report an operating range of 7.4 meters for the analog backscatter microphone and 2.7 meters for hybrid microphone with 26.7 dBm output power USRP-based RFID reader.

Question: What is the difference between analog and digital backscatter?


Unicorn: A System for Searching the Social Graph. Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook's Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook's search offerings.

Question: What is the data model of unicorn?


Inverse Risk-Sensitive Reinforcement Learning. We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. We derive a risk-sensitive reinforcement learning algorithm with convergence guarantees that employs convex risk metrics and models of human decisionmaking deriving from behavioral economics. The risk-sensitive reinforcement learning algorithm provides the theoretical underpinning for a gradient-based inverse reinforcement learning algorithm that minimizes a loss function defined on observed behavior of a risk-sensitive agent. We demonstrate the performance of the proposed technique on two examples: (i) the canonical Grid World example and (ii) a Markov decision process modeling ride-sharing passengers' decisions given price changes. In the latter, we use pricing and travel time data from a ride-sharing company to construct the transition probabilities and rewards of the Markov decision process.

Question: How to minimize a loss function defined on observed behavior of a risk-sensitive agent?


Tag recommendations in social bookmarking systems. Collaborative tagging systems allow users to assign keywords—so called "tags"—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Question: What is tag recommendation?


A Nonlinear Controller Based on a Discrete Energy Function for an AC/DC Boost PFC Converter. AC/DC converter systems generally have two stages: an input power factor correction (PFC) boost ac/dc stage that converts input ac voltage to an intermediate dc voltage while reducing the input current harmonics injected to the grid, followed by a dc/dc converter that steps up or down the intermediate dc-bus voltage as required by the output load and provides high-frequency galvanic isolation. Since a low-frequency ripple (second harmonic of the input ac line frequency) exists in the output voltage of the PFC ac/dc boost converter due to the power ripple, the voltage loop in the conventional control system must have a very low bandwidth in order to avoid distortions in the input current waveform. This results in the conventional PFC controller having a slow dynamic response against load variations with adverse overshoots and undershoots. This paper presents a new control approach that is based on a novel discrete energy function minimization control law that allows the front-end ac/dc boost PFC converter to operate with faster dynamic response than the conventional controllers and simultaneously maintain near unity input power factor. Experimental results from a 3-kW ac/dc converter built for charging traction battery of a pure electric vehicle are presented in this paper to validate the proposed control method and its superiority over conventional controllers.

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


Pre-Prosthetic Orthodontic Implant for Management of Congenitally Unerupted Lateral Incisors – A Case Report. The maxillary lateral incisor is one of the most common congenitally missing teeth of the permanent dentition. With the advent of implants in the field of restorative dentistry, a stable and predictable fixed prosthetic replacement has become a reality, especially for young adult patients who suffer from congenital absence of teeth. The dual goals of establishment of functional stability as well as enhancement of esthetic outcomes are made achievable by the placement of implants. A multidisciplinary team approach involving the triad of orthodontist, periodontist and restorative dentist will ensure the successful completion of the integrated treatment approach in these patients. The present case report achieved successful implant based oral rehabilitation in a patient diagnosed with congenital absence of bilateral maxillary lateral incisors utilizing a preprosthetic orthodontic implant site preparation for the purpose of space gain.

Question: How to treat congenitally missing teeth?


A comparison of radial and axial flux structures in electrical machines. This paper explores the comparative advantages and disadvantages of the simplest form of axial flux geometry, the single sided version, compared to the most common form of radial flux geometry with an inside rotor, when considering permanent magnet synchronous machines. A detailed literature review of comparisons is presented. New material is presented highlighting the benefits of single sided axial flux geometry. The constraints and assumptions made when comparing possibilities are discussed in detail, including a study of the biases these can introduce. The basis of this comparison is founded on constant electromagnetic airgap shear stress, being the product of electric loading and magnetic loading, and indeed the constancy of both of those factors. The metric used for comparison is simply that of the masses of the active materials, steel, copper, and magnet material. Examples are presented, including a machine which recently went into production in quantity. A range of lesser issues which are relevant when one is making a choice, are presented and discussed.

Question: What is the difference between radial and axial flux?


Ultra-stretchable and skin-mountable strain sensors using carbon nanotubes-Ecoflex nanocomposites.. Super-stretchable, skin-mountable, and ultra-soft strain sensors are presented by using carbon nanotube percolation network-silicone rubber nanocomposite thin films. The applicability of the strain sensors as epidermal electronic systems, in which mechanical compliance like human skin and high stretchability (ϵ > 100%) are required, has been explored. The sensitivity of the strain sensors can be tuned by the number density of the carbon nanotube percolation network. The strain sensors show excellent hysteresis performance at different strain levels and rates with high linearity and small drift. We found that the carbon nanotube-silicone rubber based strain sensors possess super-stretchability and high reliability for strains as large as 500%. The nanocomposite thin films exhibit high robustness and excellent resistance-strain dependency for over ~1380% mechanical strain. Finally, we performed skin motion detection by mounting the strain sensors on different parts of the body. The maximum induced strain by the bending of the finger, wrist, and elbow was measured to be ~ 42%, 45% and 63%, respectively.

Question: What is the maximum strain of the strain sensors?


A Literature Survey of Benchmark Functions For Global Optimization Problems. Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.

Question: What is a benchmark function?


Non-orthogonal Multiple Access in a Downlink Multiuser Beamforming System. In this paper, we propose a non-orthogonal multiple access-based multiuser beamforming (NOMA-BF) system designed to enhance the sum capacity. In the proposed NOMA-BF system, a single BF vector is shared by two users, so that the number of supportable users can be increased. However, sharing a BF vector leads to interference from other beams as well as from the other user sharing the BF vector. Therefore, to reduce interference and improve the sum capacity, we additionally propose a clustering and power allocation algorithm. This clustering algorithm, which selects two users with high correlation and a large gain-difference between their channels, can reduce the interference from other beams and from the other user as well. Furthermore, power allocation ensures that each user's transmit power is allocated so as to maximize the sum capacity. Numerical results verify that the proposed NOMA-BF system improves the sum capacity, compared to the conventional multiuser BF system.

Question: How to reduce interference and improve the sum capacity?


Chernoff-Hoeffding Inequality and Applications. When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making "many simple estimates" of the full data set, and then judging them as a whole. Perhaps magically, these "many simple estimates" can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 6]. This document provides a simple form of this bound, and two examples of its use.

Question: How many simple estimates are needed to get a fixed accuracy?


Language games for autonomous robots. extraordinarily difficult and poses challenges for both integration and grounding. Here, I propose a unifying idea that meets both challenges: language games. A language game is a sequence of verbal interactions between two agents situated in a specific environment. Language games both integrate the various activities required for dialogue and ground unknown words or phrases in a specific context, which helps constrain possible meanings. Over the past five years, I have been working with a team to develop and test language games on progressively more sophisticated systems, from relatively simple camera-based systems to humanoid robots. The results of our work show that language games are a useful way to both understand and design human–robot interaction.

Question: What is a language game?


Iterative methods for sparse linear systems. Sometimes we need to solve the linear equation Ax = b for a very big and very sparse A. For example, the Poisson equation where only 5 entries of each row of the matrix A are non-zero. Standard methods such as inverting the matrix A (numerically unstable) or Guass elimination do not take advantage of the sparsity of A. In this lesson we will discuss two iterative methods suitable for sparse linear systems: Jacobi and Gauss-Seidel.

Question: What is a sparse matrix?


The Use Of Edmodo In Creating An Online Learning Community Of Practice For Learning To Teach Science. This study aimed to create an online community of practice by creating a virtual classroom in the Edmodo application and ascertain the opinions of pre-service primary teachers about the effects of Edmodo on their learning to teach science and availability of Edmodo. The research used a case study, which is one method of descriptive research. During the implementation process, pre-service primary teachers used Edmodo to share activities they had designed that centred on the scientific concepts taught in primary science education programmes. They also shared their diary entries that outlined their experiences and views after they had practised their activities in a real classroom. 58 pre-service primary teachers participated in the study. The author developed a questionnaire and it included one closed-ended and six open-ended questions; the questionnaire was used as the data collection tool. The pre-service primary teachers used Edmodo for 12 weeks. Descriptive and content analysis methods were used to analyse the data obtained from the study. The results obtained from the data analysis showed that pre-service primary teachers generally had positive views about the use of Edmodo in teacher education programmes. Most pre-service primary teachers stated that Edmodo provides the possibility of sharing knowledge, experiences and views. However, some pre-service teachers stated that Edmodo has some limitations; for example, the fact that it requires the user to have internet access. As a result, it can be said that Edmodo can be used to create an online community of practice in teacher education programmes.

Question: What is the use of edmodo in creating an online learning community of practice for learning to teach science?


The study on feature selection in customer churn prediction modeling. When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some telecom operator's marketing data to verify the efficiency of the feature selection framework.

Question: What is the framework of feature selection for customer churn prediction?


The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits. Recognizing people by the way they walk – also known as gait recognition – has been studied extensively in the recent past. Recent gait recognition methods solely focus on data extracted from an RGB video stream. With this work, we provide a means for multimodal gait recognition, by introducing the freely available TUM Gait from Audio, Image and Depth (GAID) database. This database simultaneously contains RGB video, depth and audio. With 305 people in three variations, it is one of the largest to-date. To further investigate challenges of time variation, a subset of 32 people is recorded a second time. We define standardized experimental setups for both person identification and for the assessment of the soft biometrics age, gender, height, and shoe type. For all defined experiments, we present several baseline results on all available modalities. These effectively demonstrate multimodal fusion being beneficial to gait recognition.

Question: How large is the database?


Efficient and Effective Solutions for Video Classification.

Question: What is the difference between a video classification system and a video indexing system?


Crowd analysis using visual and non-visual sensors, a survey. This paper proposes a critical survey of crowd analysis techniques using visual and non-visual sensors. Automatic crowd understanding has a massive impact on several applications including surveillance and security, situation awareness, crowd management, public space design, intelligent and virtual environments. In case of emergency, it enables practical safety applications by identifying crowd situational context information. This survey identifies different approaches as well as relevant work on crowd analysis by means of visual and non-visual techniques. Multidisciplinary research groups are addressing crowd phenomenon and its dynamics ranging from social, and psychological aspects to computational perspectives. The possibility to use smartphones as sensing devices and fuse this information with video sensors data, allows to better describe crowd dynamics and behaviors. Eventually, challenges and further research opportunities with reference to crowd analysis are exposed.

Question: What is crowd analysis?


The implementation of a full EMV smartcard for a point-of-sale transaction. This paper examines the changes in the payment card environment as they relate to EMV (named after Europay, MasterCard and Visa). This research shows that if the combined dynamic data authentication (CDA) card variant of the EMV card is deployed in a full EMV environment, given the relevant known vulnerabilities and attacks against the EMV technology, the consequences of unauthorized disclosure of the cardholder data is of significantly reduced value to a criminal.

Question: What is the EMV card?


Content-based approach for Vietnamese spam SMS filtering. Short Message Service (SMS) spam is a serious problem in Vietnam because of the availability of very cheap prepaid SMS packages. There are some systems to detect and filter spam messages for English, most of which use machine learning techniques to analyze the content of messages and classify them. For Vietnamese, there is some research on spam email filtering but none focused on SMS. In this work, we propose the first system for filtering Vietnamese spam SMS. We first propose an appropriate preprocessing method since existing tools for Vietnamese preprocessing cannot give good accuracy on our dataset. We then experiment with vector representations and classifiers to find the best model for this problem. Our system achieves an accuracy of 94% when labelling spam messages while the misclassification rate of legitimate messages is relatively small, about only 0.4%. This is an encouraging result compared to that of English and can be served as a strong baseline for future development of Vietnamese SMS spam prevention systems.

Question: What is the first system for Vietnamese spam SMS filtering?


An Optimized Round Robin Scheduling Algorithm for CPU Scheduling. The main objective of this paper is to develop a new approach for round robin scheduling which help to improve the CPU efficiency in real time and time sharing operating system. There are many algorithms available for CPU scheduling. But we cannot implemented in real time operating system because of high context switch rates, large waiting time, large response time, large trn around time and less throughput. The proposed algorithm improves all the drawback of simple round robin architecture. The author have also given comparative analysis of proposed with simple round robin scheduling algorithm. Therefore, the author strongly feel that the proposed architecture solves all the problem encountered in simple round robin architecture by decreasing the performance parameters to desirable extent and thereby increasing the system throughput.

Question: What is round robin scheduling?


A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests. We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.

Question: What is a Bayesian network?


Optimal spatial filtering of single trial EEG during imagined hand movement.. The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.

Question: How to filter EEG signals?


Harnessing the power of FPGAs using altera's OpenCL compiler. In recent years, Field-Programmable Gate Arrays have become extremely powerful computational platforms that can efficiently solve many complex problems. The most modern FPGAs comprise effectively millions of programmable elements, signal processing elements and high-speed interfaces, all of which are necessary to deliver a complete solution. The power of FPGAs is unlocked via low-level programming languages such as VHDL and Verilog, which allow designers to explicitly specify the behavior of each programmable element. While these languages provide a means to create highly efficient logic circuits, they are akin to "assembly language" programming for modern processors. This is a serious limiting factor for both productivity and the adoption of FPGAs on a wider scale. In this talk, we use the OpenCL language to explore techniques that allow us to program FPGAs at a level of abstraction closer to traditional software-centric approaches. OpenCL is an industry standard parallel language based on 'C' that offers numerous advantages that enable designers to take full advantage of the capabilities offered by FPGAs, while providing a high-level design entry language that is familiar to a wide range of programmers.  To demonstrate the advantages a high-level programming language can offer, we demonstrate how to use Altera's OpenCL Compiler on a set of case studies. The first application is single-precision general-element matrix multiplication (SGEMM). It is an example of a highly-parallel algorithm for which an efficient circuit structures are well known. We show how this application can be implemented in OpenCL and how the high-level description can be optimized to generate the most efficient circuit in hardware. The second application is a Fast Fourier Transform (FFT), which is a classical FPGA benchmark that is known to have a good implementation on FPGAs. We show how we can implement the FFT algorithm, while e

Question: What is the difference between a FPGA and a CPU?


A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications. Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (tomultiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research,mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

Question: What is the difference between a particle swarm optimization algorithm and a genetic algorithm?


A Better Baseline for AVA. We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features – in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3% of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.

Question: What is the baseline for action localization on the AVA dataset?


Prediction, learning, and games. Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading prediction learning and games, you can take more advantages with limited budget.

Question: How to learn more?


Image super-resolution by estimating the enhancement weight of self example and external missing patches. Image super-resolution (SR) is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed, but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas.

Question: How to estimate the enhancement weight of each patch?


Digital Clock and Data Recovery Circuits for Optical Links. Clock and Data Recovery (CDR) circuits perform the function of recovering clock and re-timing received data in optical links. These CDRs must be capable of tolerating large input jitter (high JTOL), filter input jitter (low JTRAN with no jitter peaking) and in burst-mode applications be capable of phase locking in a very short time. In this paper, we elucidate these design tradeoffs and present various CDR architectures that can overcome them. Specifically, D/PLL CDR architecture that achieves high JTOL, low JTRAN, and no jitter peaking is described. A new burst-mode CDR that can lock instantaneously while filtering input jitter is also discussed.

Question: What is the difference between a CDR and a PLL?


Multidisciplinary Instruction with the Natural Language Toolkit. The Natural Language Toolkit ( NLTK ) is widely used for teaching natural language processing to students majoring in linguistics or computer science. This paper describes the design ofNLTK , and reports on how it has been used effectively in classes that involve different mixes of linguistics and computer science students. We focus on three key issues: getting started with a course, delivering interactive demonstrations in the classroom, and organizing assignments and projects. In each case, we report on practical experience and make recommendations on how to useNLTK to maximum effect.

Question: How to use NLTK in a class?


Performance Counters to Rescue: A Machine Learning based safeguard against Micro-architectural Side-Channel-Attacks. Micro-architectural side-channel-attacks are presently daunting threats to most mathematically elegant encryption algorithms. Even though there exist various defense mechanisms, most of them come with the extra overhead of implementation. Recent studies have prevented some particular categories of these attacks but fail to address the detection of other classes. This paper presents a generic machine learning based multi-layer detection approach targeting these micro-architectural side-channel-attacks, without concentrating on a single category. The proposed approach work by profiling low-level hardware events using Linux perf event API and then by analyzing these data with some appropriate machine learning techniques. This paper also presents a novel approach, using time-series data, to correlate the execution trace of the adversary with the secret key of encryption for dealing with false-positives and unknown attacks. The experimental results and performance of the proposed approach suggest its superiority with high detection accuracy and low performance overhead.

Question: What is a micro-architectural side-channel-attack?


Learning to simplify: fully convolutional networks for rough sketch cleanup. In this paper, we present a novel technique to simplify sketch drawings based on learning a series of convolution operators. In contrast to existing approaches that require vector images as input, we allow the more general and challenging input of rough raster sketches such as those obtained from scanning pencil sketches. We convert the rough sketch into a simplified version which is then amendable for vectorization. This is all done in a fully automatic way without user intervention. Our model consists of a fully convolutional neural network which, unlike most existing convolutional neural networks, is able to process images of any dimensions and aspect ratio as input, and outputs a simplified sketch which has the same dimensions as the input image. In order to teach our model to simplify, we present a new dataset of pairs of rough and simplified sketch drawings. By leveraging convolution operators in combination with efficient use of our proposed dataset, we are able to train our sketch simplification model. Our approach naturally overcomes the limitations of existing methods, e.g., vector images as input and long computation time; and we show that meaningful simplifications can be obtained for many different test cases. Finally, we validate our results with a user study in which we greatly outperform similar approaches and establish the state of the art in sketch simplification of raster images.

Question: What is the state of the art in sketch simplification of raster images?


An On-Chip Temperature Sensor With a Self-Discharging Diode in 32-nm SOI CMOS. We report a 39 μm × 27 μm on-chip temperature sensor which uses the temperature-dependent reverse-bias leakage current of a lateral silicon on insulator (SOI) CMOS p-n diode to monitor the thermal profile of a 32-nm microprocessor core. In this sensor, the diode junction capacitance is first charged to a fixed voltage. Subsequently, the diode capacitance is allowed to self-discharge through its temperature-dependent reverse-bias current. Next, by using a time-to-digital-converter circuit, the discharge voltage is converted to a temperature-dependent time pulse, and finally, its width is measured by using a digital counter. This compact temperature sensor demonstrates a 3σ measurement inaccuracy of ±1.95°C across the 5°C-100°C temperature range while consuming only 100 μW from a single 1.65-V supply.

Question: How does a temperature sensor work?


Suicide prevention strategies: a systematic review.. CONTEXT In 2002, an estimated 877,000 lives were lost worldwide through suicide. Some developed nations have implemented national suicide prevention plans. Although these plans generally propose multiple interventions, their effectiveness is rarely evaluated.   OBJECTIVES To examine evidence for the effectiveness of specific suicide-preventive interventions and to make recommendations for future prevention programs and research.   DATA SOURCES AND STUDY SELECTION Relevant publications were identified via electronic searches of MEDLINE, the Cochrane Library, and PsychINFO databases using multiple search terms related to suicide prevention. Studies, published between 1966 and June 2005, included those that evaluated preventative interventions in major domains; education and awareness for the general public and for professionals; screening tools for at-risk individuals; treatment of psychiatric disorders; restricting access to lethal means; and responsible media reporting of suicide.   DATA EXTRACTION Data were extracted on primary outcomes of interest: suicidal behavior (completion, attempt, ideation), intermediary or secondary outcomes (treatment seeking, identification of at-risk individuals, antidepressant prescription/use rates, referrals), or both. Experts from 15 countries reviewed all studies. Included articles were those that reported on completed and attempted suicide and suicidal ideation; or, where applicable, intermediate outcomes, including help-seeking behavior, identification of at-risk individuals, entry into treatment, and antidepressant prescription rates. We included 3 major types of studies for which the research question was clearly defined: systematic reviews and meta-analyses (n = 10); quantitative studies, either randomized controlled trials (n = 18) or cohort studies (n = 24); and ecological, or population- based studies (n = 41). Heterogeneity of study populations and methodology did not pe

Question: What is the best way to prevent suicide?


Multilingual Models for Compositional Distributed Semantics. We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.

Question: What is the difference between the distributional hypothesis and joint-space embeddings?


Ontology based context modeling and reasoning using OWL. Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.

Question: What is the ontology based context modeling and reasoning using OWL?