The social impact of a systematic floor cleaner. Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.

Question: What is the social impact of a systematic floor cleaner?


Scalable knowledge harvesting with high precision and high recall. Harvesting relational facts from Web sources has received great attention for automatically constructing large knowledge bases. Stateof-the-art approaches combine pattern-based gathering of fact candidates with constraint-based reasoning. However, they still face major challenges regarding the trade-offs between precision, recall, and scalability. Techniques that scale well are susceptible to noisy patterns that degrade precision, while techniques that employ deep reasoning for high precision cannot cope with Web-scale data.  This paper presents a scalable system, called PROSPERA, for high-quality knowledge harvesting. We propose a new notion of ngram-itemsets for richer patterns, and use MaxSat-based constraint reasoning on both the quality of patterns and the validity of fact candidates.We compute pattern-occurrence statistics for two benefits: they serve to prune the hypotheses space and to derive informative weights of clauses for the reasoner. The paper shows how to incorporate these building blocks into a scalable architecture that can parallelize all phases on a Hadoop-based distributed platform. Our experiments with the ClueWeb09 corpus include comparisons to the recent ReadTheWeb experiment. We substantially outperform these prior results in terms of recall, with the same precision, while having low run-times.

Question: What is the difference between a scalable knowledge harvesting system and a scalable knowledge harvesting system?


Prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm. related to the prediction of the air temperature in the region of Meknes in Morocco. Depending on weather parameters, such as atmospheric pressure (Pr), humidity (H), visibility (Vis), wind speed (V), dew point (Tr) and the precipitation (P). The database used contains a history of meteorological parameters covering 3288 days, from 2004 to 2012. This multilayer perceptron type of neuron (MLP) was used to approximate the relationship between these parameters with minimum square error. For a better air temperature prediction, we developed a stochastic neural model. The mean square error (MSE) and the correlation coefficient (R) were used to evaluate the performance of the developed models. The study of these statistical indicators demonstrated that the prediction of the air temperature was powerful with the Levenberg-Marquard algorithm, with architecture [610-1], the Tansig function in the hidden layer and the Purelin function in the output layer.

Question: What is the prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm?


Complete 3D Scene Parsing from an RGBD Image. One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset (Silberman et al., in: Computer vision-ECCV, 2012, pp 746–760, 2012) with detailed 3D shapes.

Question: How to parse a 3D scene from an RGBD image?


The Media and Technology Usage and Attitudes Scale: An empirical investigation. Current approaches to measuring people's everyday usage of technology-based media and other computer-related activities have proved to be problematic as they use varied outcome measures, fail to measure behavior in a broad range of technology-related domains and do not take into account recently developed types of technology including smartphones. In the present study, a wide variety of items, covering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology and media usage, along with 18 additional items assessing attitudes toward technology, were administered to two independent samples of individuals, comprising 942 participants. Factor analyses were used to create 11 usage subscales representing smartphone usage, general social media usage, Internet searching, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships, phone calling, and watching television in addition to four attitude-based subscales: positive attitudes, negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales showed strong reliabilities and relationships between the subscales and pre-existing measures of daily media usage and Internet addiction were as predicted. Given the reliability and validity results, the new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media and technology involvement across a variety of types of research studies either as a single 60-item scale or any subset of the 15 subscales.

Question: What is the Media and Technology Usage and Attitudes Scale?


Longitudinal relations between children's exposure to TV violence and their aggressive and violent behavior in young adulthood: 1977-1992.. Although the relation between TV-violence viewing and aggression in childhood has been clearly demonstrated, only a few studies have examined this relation from childhood to adulthood, and these studies of children growing up in the 1960s reported significant relations only for boys. The current study examines the longitudinal relations between TV-violence viewing at ages 6 to 10 and adult aggressive behavior about 15 years later for a sample growing up in the 1970s and 1980s. Follow-up archival data (N = 450) and interview data (N = 329) reveal that childhood exposure to media violence predicts young adult aggressive behavior for both males and females. Identification with aggressive TV characters and perceived realism of TV violence also predict later aggression. These relations persist even when the effects of socioeconomic status, intellectual ability, and a variety of parenting factors are controlled.

Question: What is the relation between childhood exposure to TV violence and aggressive behavior in young adulthood?


An implementation of the 802.1AE MAC Security Standard for in-car networks. The continuous increase in complexity in automotive electronics has led to cars that include up to 80 Electronic Control Units (ECUs). As a consequence, in-car networks are currently up to their limit in terms of data load, flexibility and bandwidth. The Ethernet backbone is thus considered as the best performing solution. On the other hand, the growing interconnection of cars with the external world requires high security standards in order to prevent safety risks for car passengers. The IEEE 802.1AE MAC Security Standard (MACSec) solves the security issues of Ethernet networks by providing confidentiality, authenticity and integrity of data. This paper presents an efficient hardware implementation of the MACSec standard for the automotive world. The system was synthesized on a Stratix V FPGA and on a 28nm standard-cell CMOS technology. In terms of maximum throughput, the FPGA results in 1.1 Gbps while the standard-cell technology reaches 3.9 Gbps. The FPGA implementation occupies 4.5% of the Adaptive Logic Modules (ALMs) while the standard-cell one gives a 285 kgate size. The proposed architecture represents a suitable implementation for a low area and high-performance solution as usually required by in-car network controllers.

Question: What is the MACSec standard?


Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Several common systems satisfy some but not all of the VR definition above. Flight simulators provide vehicle tracking, not head tracking, and do not generally operate in binocular stereo. Omnimax theaters give a large angle of view [8], occasionally in stereo, but are not interactive. Head-tracked monitors [4][6] provide all but a large angle of view. Head-mounted displays (HMD) [7][13] and BOOMs [9] use motion of the actual display screens to achieve VR by our definition. Correct projection of the imagery on large screens can also create a VR experience, this being the subject of this paper. This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches the quality of workstation screens in terms of resolution, color, and flicker-free stereo. In addition, this format helps reduce the effect of common tracking and system latency errors. The off-axis perspective projection techniques we use are shown to be simple and straightforward. Our techniques for doing multi-screen stereo vision are enumerated, and design barriers, past and current, are described. Advantages and disadvantages of the projection paradigm are discussed, with an analysis of the effect of tracking noise and delay on the user. Successive refinement, a necessary tool for scientific visualization, is developed in the virtual reality context. The use of the CAVE as a one-to-many presentation device at SIGGRAPH '92 and Supercomputing '92 for computational science data is also mentioned. Previous work in the VR area dates back to Sutherland [12], who in 1965 wrote about the "Ultimate Display." Later in the decade at the University of Utah, Jim Clark developed a system that allowed wireframe graphics VR to be seen through a headmounted, BOOM-type

Question: What is the CAVE?


Securing Bitcoin wallets via a new DSA / ECDSA threshold signature scheme. The Bitcoin ecosystem has suffered frequent thefts and losses affecting both businesses and individuals. Due to the irreversibility, automation, and pseudonymity of transactions, Bitcoin currently lacks support for the sophisticated internal control systems deployed by modern businesses to deter fraud. To address this problem, we present the first threshold signature scheme compatible with Bitcoin's ECDSA signatures and show how distributed Bitcoin wallets can be built using this primitive. For businesses, we show how our distributed wallets can be used to systematically eliminate single points of failure at every stage of the flow of bitcoins through the system. For individuals, we design, implement, and evaluate a two-factor secure Bitcoin wallet.

Question: How to secure bitcoin wallets?


Examining The Adoption Of Human Resource Information System In The Context Of Saudi Arabia. The aim of this paper is to examine the factors influencing the adoption of the human resource information system (HRIS) in the Saudi context. A few researches have looked at the adoption of HRIS, but none of these studies have examined the adoption of such a system from the users' perspective in general and Saudi Arabia (SA) in particular. This study has developed a model that integrates service quality with perceived usefulness and perceived ease of use to investigate the factors affecting the users' adoption of HRIS. The model was empirically tested using the structure equation modelling by employing data collected from a survey of HRIS users. The findings and discussion presented in this study will help the Saudi organisations to understand the current status of their HRIS to improve it and benefit from it. This study provides guidelines for its implementation for theory and practices, limitations and future directions.

Question: What is the aim of this paper?


Kinematics and dynamics analysis of a 2-DOF spherical parallel robot. In this paper, the kinematic and dynamic analysis of a 2-degree-of-freedom spherical parallel manipulator is presented. The mobility and motion pattern of the manipulator are analyzed using screw theory, which resulted in obtaining the direct and inverse Jacobian matrices. Position, velocity and acceleration relations between different parts of the manipulator and the actuated joint angles are obtained, which are prerequisite for dynamic analysis. Since the manipulator, belongs to a class of parallel mechanism known as over-constrained mechanism, thus to obtain a dynamical model, a modification should be applied in its kinematic arrangement by preserving the performed motion pattern. Finally, accuracy of the dynamical model is verified by comparing the results obtained from the formulated model with results obtained from a SimMechanics model of the under study manipulator.

Question: What is a 2-DOF spherical parallel manipulator?


Current review of prepubertal vaginal bleeding.. PURPOSE OF REVIEW Prepubertal vaginal bleeding raises many concerns and evaluation and diagnosis may prove difficult for many providers. We aim to provide a comprehensive review and recent updates for those practitioners who care for these patients.   RECENT FINDINGS Prompt management in the case of prepubertal vaginal bleeding is indicated, especially to rule out malignancy or abuse. If a child is reluctant to undergo examination, or if the extent of injury or source of bleeding cannot be determined, examination under anesthesia and vaginoscopy is recommended. Use of vaginoscopy allows for clear visualization of the vagina and cervix without distorting hymenal anatomy, as well as diagnosis and removal of a foreign body and evaluation of mucosal damage caused. In the case of sexual abuse, providers specifically trained in pediatrics need to be present, and safety of the patient should always be ensured.   SUMMARY Careful history taking and targeted examination may lead to diagnosis in the case of prepubertal vaginal bleeding. However, in more difficult cases, practitioners should not hesitate to examine a patient in the operating room using general anesthesia to elicit the cause. Although sexual abuse and malignancy are always on the differential, most causes of bleeding are benign and easily treated.

Question: What is prepubertal vaginal bleeding?


Modeling Credit Risk for SMEs : Evidence from the US Market. Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

Question: What is the difference between a generic corporate model and a model for SMEs?


A Blockchain-based Security-Oriented Framework for Cloud Federation. Cloud federations have been formed to share the services, prompt and support cooperation, as well as interoperability among their already deployed cloud systems. However, the creation and management of the cloud federations lead to various security issues such as confidentially, integrity and availability of the data. Despite the access control policies in place, an attacker may compromise the communication channel processing the access requests and the decisions between the access control systems and the members(users) and vice-versa. In cloud federation, the rating of the services offered by different cloud members becomes integral to providing the users with the best quality services. Hence, we propose an innovative blockchainbased framework that on the one hand permits secure communication between the members of the federation and the access control systems, while on the other hand provides the quality services to the members by considering the service constraints imposed by them.

Question: What is a blockchain-based security-oriented framework for cloud federation?


A codebook-free and annotation-free approach for fine-grained image categorization. Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation, often resulting in loss of subtle image information that are critical for fine-grained classification. Another way to address this problem is to introduce human annotations of object attributes or key points, a tedious process that is also difficult to generalize to new tasks. In this work, we propose a codebook-free and annotation-free approach for fine-grained image categorization. Instead of using vector-quantized codewords, we obtain an image representation by running a high throughput template matching process using a large number of randomly generated image templates. We then propose a novel bagging-based algorithm to build a final classifier by aggregating a set of discriminative yet largely uncorrelated classifiers. Experimental results show that our method outperforms state-of-the-art classification approaches on the Caltech-UCSD Birds dataset.

Question: What is a codebook-free and annotation-free approach for fine-grained image categorization?


Biofeedback-based behavioral treatment for chronic tinnitus: results of a randomized controlled trial.. Many tinnitus sufferers believe that their tinnitus has an organic basis and thus seek medical rather than psychological treatments. Tinnitus has been found to be associated with negative appraisal, dysfunctional attention shift, and heightened psychophysiological arousal, so cognitive-behavioral interventions and biofeedback are commonly suggested as treatments. This study developed and investigated the efficacy of a biofeedback-based cognitive-behavioral treatment for tinnitus. In total, 130 tinnitus patients were randomly assigned to an intervention or a wait-list control group. Treatment consisted of 12 sessions of a biofeedback-based behavioral intervention over a 3-month period. Patients in the wait-list group participated in the treatment after the intervention group had completed the treatment. Results showed clear improvements regarding tinnitus annoyance, diary ratings of loudness, and feelings of controllability. Furthermore, changes in coping cognitions as well as changes in depressive symptoms were found. Improvements were maintained over a 6-month follow-up period in which medium-to-large effect sizes were observed. The treatment developed and investigated in this study is well accepted and leads to clear and stable improvements. Through demonstration of psychophysiological interrelationships, the treatment enables patients to change their somatic illness perceptions to a more psychosomatic point of view.

Question: What is the difference between a biofeedback-based behavioral treatment and a cognitive-behavioral treatment?


Automated Test Data Generation Using an Iterative Relaxation Method. An important problem that arises in path oriented testing is the generation of test data that causes a program to follow a given path. In this paper, we present a novel program execution based approach using an iterative relaxation method to address the above problem. In this method, test data generation is initiated with an arbitrarily chosen input from a given domain. This input is then iteratively refined to obtain an input on which all the branch predicates on the given path evaluate to the desired outcome. In each iteration the program statements relevant to the evaluation of each branch predicate on the path are executed, and a set of linear constraints is derived. The constraints are then solved to obtain the increments for the input. These increments are added to the current input to obtain the input for the next iteration. The relaxation technique used in deriving the constraints provides feedback on the amount by which each input variable should be adjusted for the branches on the path to evaluate to the desired outcome.When the branch conditions on a path are linear functions of input variables, our technique either finds a solution for such paths in one iteration or it guarantees that the path is infeasible. In contrast, existing execution based approaches may require an unacceptably large number of iterations for relatively long paths because they consider only one input variable and one branch predicate at a time and use backtracking. When the branch conditions on a path are nonlinear functions of input variables, though it may take more then one iteration to derive a desired input, the set of constraints to be solved in each iteration is linear and is solved using Gaussian elimination. This makes our technique practical and suitable for automation.

Question: What is the difference between a path oriented test and a branch oriented test?


Continuous control with deep reinforcement learning. We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies "end-to-end": directly from raw pixel inputs.

Question: What is the continuous control with deep reinforcement learning?


Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers. Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.

Question: How to predict the performance of a distributed deep learning system?


Intellectual Capital (IC) Measurement in the Mass Media Context. Mass media is the key in°uencer of public opinion. The in°uence is not only limited to political and social, but also relates to organisational and economical reputation and brands. Within public opinion, organisations must manage how they are represented competitively within mass media so that they can develop their brand strategically to grow and compete in the current global knowledge economy. This is where the link to Intellectual Capital (IC) Measurement is signi ̄cant. IC, as the sum of all an organisation's intangible assets drives a company's presence and value within the media, albeit related to human, structural or relational capital attributes. The measurement, therefore, of IC in the mass media context is invaluable to understand how a company is placed strategically and competitively in the external space, and how this links to internal activities, goals and outcomes. This paper is an attempt to address some of the issues related to IC measurements in the mass media context by suggesting a framework that provides a multidisciplinary and holistic approach to the understanding and contextualising of the organisation's presence in the public space.

Question: What is the difference between intangible assets and intellectual capital?


Warpage Tuning Study for Multi-chip Last Fan Out Wafer Level Package. In recent years, the IoT popularity pushes the package development of 3C products into a more functional and thinner target. For high I/O density and low cost considered package, the promising Fan-out Wafer Level Packaging (FOWLP) provides a solution to match OSAT existing capability, besides, the chip last process in FOWLP can further enhance the total yield by selectable known-good dies (KGDs). However, under processing, the large portion of molding compound induces high warpage to challenge fabrication limitation. The additional leveling process is usually applied to lower the warpage that caused by the mismatch of coefficient of thermal expansion and Young's modulus from carriers, dies, and molding compound. This process results in the increase of package cost and even induce internal damages that affect device reliability. In order to avoid leveling process and improve warpage trend, in this paper, we simulated several models with different design of molding compound and dies, and then developed a multi-chip last FOWLP test vehicle by package dimension of 12x15 mm2 with 8x9 and 4x9 mm2 multiple dies, respectively. The test vehicle performed three redistribution layers (RDLs) including one fine pitch RDL of line width/line spacing 2um/2um, which is also the advantage of multi-chip last FOWLP, and also exhibited ball on trace structure for another low cost option. For the wafer warpage discussion, the results showed that tuning the thickness of molding compound can improve warpage trend, especially in the application of high modulus carrier, which improved wafer warpage within 1mm, for package warpage discussion, the thinner die can lower the warpage of package. Through well warpage controlling, the multi-chip last FOWLP package with ball on trace design was successfully presented in this paper, and also passed the package level reliability of TCB 1000 cycles, HTSL 1000 hrs, and uHAST 96 hrs, a

Question: What is the ball on trace structure?


Model-based Q-learning for humanoid robots. This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning — one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.

Question: What is reinforcement learning?


Performance analysis of two open source intrusion detection systems. Several studies have been conducted where authors compared the performance of open source Intrusion detection systems, namely Snort and Suricata. However, most studies were limited to either security indicators or performance measurements under the same operating system. The objective of this study is to give a comprehensive analysis of both products in terms of several security related and performance related indicators. In addition, we tested the products under two different operating systems. Several experiments were run to evaluate the effects of open source intrusion detection and prevention systems Snort and Suricata, operating systems Windows, Linux and various attack types on system resource usage, dropped packets rate and ability to detect intrusions. The results show that Suricata has a higher CPU and RAM utilization than Snort in all cases on both operating systems, but lower percentage of dropped packets when evaluated during five of six simulated attacks. Both products had the same number of correctly identified intrusions. The results show that Linux-based solutions consume more system resources, but Windows-based systems had a higher rate of dropped packets. This indicates that these two intrusion detection and prevention systems should be run on Linux. However, both systems are inappropriate for high volumes of traffic in single-server setting.

Question: What is the difference between Snort and Suricata?


A new paradigm of human gait analysis with Kinect. Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naïve Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.

Question: What is the difference between Kinect and Kinect?


Monte Carlo Filtering Using Kernel Embedding of Distributions. Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.

Question: What is a Monte Carlo filtering algorithm?


Lesion border detection in dermoscopy images. BACKGROUND Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.   METHODS In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects.   CONCLUSION Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.

Question: What is the difference between a border and a lesion?


Source Code Metrics for Programmable Logic Controller (PLC) Ladder Diagram (LD) Visual Programming Language. The IEC 611131-3, an open international standard for Programmable Logic Controllers (PLC) defines several domain specific languages for industrial and process automation. Domain specific languages have several features, programming constructs and notations which are different than general purpose languages and hence the traditional source code metrics for general purpose programming languages cannot be directly applied to domain specific languages for writing control programs in the area of industrial automation. We propose source code level metrics to measure size, vocabulary, cognitive complexity and testing complexity of a visual Programmable Logic Controller (PLC) programming language. We present metrics for Ladder Diagram (LD) programming language which is one of the five languages defined in the IEC 61131-3 standard. The proposed metric is based on factors like number of distinct operators and operands, total number of operators and operands, number of rungs, weights of different basic control structures, structure of the program and control flow. We apply Weyukur's property to validate the metrics and evaluate the number of properties satisfied by the proposed metric.

Question: What is the size of the source code for a PLC ladder diagram?


Tractable Queries for Lightweight Description Logics. It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.

Question: What is the size of the canadian military?


Fixed-Rate Compressed Floating-Point Arrays. Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4d values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.

Question: What is a fixed-rate compressed floating-point array?


Simulation in software engineering training. Simulation is frequently used for training in many application areas like aviation and economics, but not in software engineering. We present the SESAM project which focuses on software engineering education using simulation. In the SESAM project a simulator was developed. Using this simulator, a student can take the role of a software project manager. The simulated software project can be finished within a couple of hours because it is simulated in "quick-motion" mode. In this paper, the background and goals of the SESAM project are presented. A new simulation model, the so called QA model, is introduced. The model behavior is demonstrated by investigating and comparing different strategies for software development. The results of experiments based on the QA model are reported. Finally, conclusions are drawn from the experiments and future work is outlined.

Question: What is the background and goals of the SESAM project?


A review on plant disease detection using image processing. India is the agriculture based country, since it contributes 7.68 percent of total global agricultural output. In India, agricultural sector contributes about seventeen percentage of total Indian gross domestic product (GDP). Effective growth and improved yield of plants are necessary for increment of farmer's profit and economy of India. For this purpose farmers need domain experts for manual monitoring of plants. But manual monitoring will not give satisfactory result all the time. Moreover, domain experts are not available at all regions and are expensive as farmers have to pay fees including travelling charges. Hence, it requires developing an efficient smart farming technique which will help for better yield and growth with less human efforts. In this paper, we provide a review on methods developed by various researchers for detection of diseases in plants, in the field of image processing. It includes research in disease detection of plants such as apple, grapes, pepper, pomegranate, tomato etc.

Question: What is the agriculture based country?


Learning patterns for discovering domain-oriented opinion words. Sentiment analysis is a challenging task that attracted increasing interest during the last years. The availability of online data along with the business interest to keep up with consumer feedback generates a constant demand for online analysis of user-generated content. A key role to this task plays the utilization of domain-specific lexicons of opinion words that enables algorithms to classify short snippets of text into sentiment classes (positive, negative). This process is known as dictionary-based sentiment analysis. The related work tends to solve this lexicon identification problem by either exploiting a corpus and a thesaurus or by manually defining a set of patterns that will extract opinion words. In this work, we propose an unsupervised approach for discovering patterns that will extract domain-specific dictionary. Our approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics. The outcome is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task. Experiments on user reviews coming from a diverse set of products demonstrate the utility of the proposed method. An implementation of the proposed approach in an easy to use application for extracting opinion words from any domain and evaluate their quality is also presented.

Question: What is the difference between a thesaurus and a dictionary?


Grounded Theory and the Constant Comparative Method : Valid Research Strategies for Educators. University of Wisconsin-Whitewater. USA ___________________________________________________________________________ Grounded theory was developed by Glaser and Strauss who believed that theory could emerge through qualitative data analysis. In grounded theory, the researcher uses multiple stages of collecting, refining, and categorizing the data. The researcher uses the strategies of the making constant comparisons and applying theoretical sampling to obtain a theory grounded in the data. The justification of this paper is to provide discussion on the validity of grounded theory and the constant comparative method as effective research strategies for educators. The qualitative design of grounded theory will be the focus of this paper, along with a discussion of the constant comparative method, issues related to trustworthiness, and limitations inherent in grounded theory methodology __________________________________________________________________________________________

Question: What is the constant comparative method?


Dose the Use of Big Data Analytics Guarantee a High Firm Performance? An Empirical Study Bases on the Dynamic Capabilities Theory. For the sake of enriching relevant research field, this study focuses on two main questions: (1) What is the effect of DBA usage on firm performance (including market performance and operational performance)? and (2) What factors that drive organizations to use big data analytics (BDA) are key drivers, based on dynamic capabilities theory? And we also identified the moderating effect of environmental dynamism between BDA use and firm performance. Furthermore, we introduce the need pull/technology push framework to identify and theorize paths via which factors influence the use of BDA. Purely from the need pull/technology push theory perspective, we propose that perceived usefulness of BDA should belong to need pull factor. Thus, we select perceived usefulness and satisfaction level with existing IT technology as need pull factors, BDA use of competitors and technology compatibility as technology push factors for big data analytics (BDA) use, and examines if these factors have a positive effect on big data analytics (BDA).

Question: What is the effect of big data analytics on firm performance?


Bifurcation Analysis of a Mathematical Model for Malaria Transmission. We present an ordinary differential equation mathematical model for the spread of malaria in human and mosquito populations. Susceptible humans can be infected when they are bitten by an infectious mosquito. They then progress through the exposed, infectious, and recovered classes, before reentering the susceptible class. Susceptible mosquitoes can become infected when they bite infectious or recovered humans, and once infected they move through the exposed and infectious classes. Both species follow a logistic population model, with humans having immigration and disease-induced death. We define a reproductive number, R0, for the number of secondary cases that one infected individual will cause through the duration of the infectious period. We find that the disease-free equilibrium is locally asymptotically stable when R0 < 1 and unstable when R0 > 1. We prove the existence of at least one endemic equilibrium point for all R0 > 1. In the absence of disease-induced death, we prove that the transcritical bifurcation at R0 = 1 is supercritical (forward). Numerical simulations show that for larger values of the disease-induced death rate, a subcritical (backward) bifurcation is possible at R0 = 1.

Question: What is a transcritical bifurcation?


A Machine Learning Approach for Product Matching and Categorization Use case : Enriching Product Ads with Semantic Structured Data. Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.

Question: What is the product matching and categorization approach?


mTOR regulation of autophagy.. Nutrient starvation induces autophagy in eukaryotic cells through inhibition of TOR (target of rapamycin), an evolutionarily-conserved protein kinase. TOR, as a central regulator of cell growth, plays a key role at the interface of the pathways that coordinately regulate the balance between cell growth and autophagy in response to nutritional status, growth factor and stress signals. Although TOR has been known as a key regulator of autophagy for more than a decade, the underlying regulatory mechanisms have not been clearly understood. This review discusses the recent advances in understanding of the mechanism by which TOR regulates autophagy with focus on mammalian TOR (mTOR) and its regulation of the autophagy machinery.

Question: What is the role of mTOR in autophagy?


Multiresolution Models for Object Detection. Most current approaches to recognition aim to be scaleinvariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from those for recognizing a 3 pixel tall object. We argue that for sensors with finite resolution, one should instead use scale-variant, or multiresolution representations that adapt in complexity to the size of a putative detection window. We describe a multiresolution model that acts as a deformable part-based model when scoring large instances and a rigid template with scoring small instances. We also examine the interplay of resolution and context, and demonstrate that context is most helpful for detecting low-resolution instances when local models are limited in discriminative power. We demonstrate impressive results on the Caltech Pedestrian benchmark, which contains object instances at a wide range of scales. Whereas recent state-of-theart methods demonstrate missed detection rates of 86%-37% at 1 falsepositive-per-image, our multiresolution model reduces the rate to 29%.

Question: What is a multiresolution model?


Fuzzy & Datamining based Disease Prediction Using K-NN Algorithm. Disease diagnosis is one of the most important applications of such system as it is one of the leading causes of deaths all over the world. Almost all system predicting disease use inputs from complex tests conducted in labs and none of the system predicts disease based on the risk factors such as tobacco smoking, alcohol intake, age, family history, diabetes, hypertension, high cholesterol, physical inactivity, obesity. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour (KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of disease specially heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.

Question: What is the accuracy of the system?


A ZVS Grid-Connected Three-Phase Inverter. A six-switch three-phase inverter is widely used in a high-power grid-connected system. However, the antiparallel diodes in the topology operate in the hard-switching state under the traditional control method causing severe switch loss and high electromagnetic interference problems. In order to solve the problem, this paper proposes a topology of the traditional six-switch three-phase inverter but with an additional switch and gave a new space vector modulation (SVM) scheme. In this way, the inverter can realize zero-voltage switching (ZVS) operation in all switching devices and suppress the reverse recovery current in all antiparallel diodes very well. And all the switches can operate at a fixed frequency with the new SVM scheme and have the same voltage stress as the dc-link voltage. In grid-connected application, the inverter can achieve ZVS in all the switches under the load with unity power factor or less. The aforementioned theory is verified in a 30-kW inverter prototype.

Question: What is a ZVS grid-connected inverter?


A Review of Substrate Integrated Waveguide End-Fire Antennas. Substrate integrated waveguide (SIW) is a planar waveguide structure, which offers the advantages of low profile, ease of fabrication, low insertion loss, and compatibility with other planar circuits. SIW end-fire antennas have drawn broad interests due to the potential applications in aircraft, missile, and radar systems. However, this planar structure suffers from narrow bandwidth due to severe impedance mismatch at the radiating aperture. Meanwhile, the narrow radiating aperture of SIW end-fire antennas also deteriorates the radiation performance. This paper presents a detailed review upon the most recent research efforts concerning the improvement of antenna performances. They are discussed and classified into three different categories from the aspect of polarization properties: horizontally polarized, vertically polarized, and circularly polarized SIW end-fire antennas. Some practical difficulties for the development of SIW end-fire antennas are pointed out and effective approaches are also provided. A wide variety of antenna examples are presented with respect to theoretical and experimental results.

Question: What is the difference between horizontally polarized, vertically polarized, and circularly polarized SIW end-fire antennas?


Question: What is HOG-EBGM?


Modeling narrative discourse. Modeling Narrative Discourse

Question: What is modeling narrative discourse?


A general framework for benchmarking firewall optimization techniques. Firewalls are among the most pervasive network security mechanisms, deployed extensively from the borders of networks to end systems. The complexity of modern firewall policies has raised the computational requirements for firewall implementations, potentially limiting the throughput of networks. Administrators currently rely on ad hoc solutions to firewall optimization. To address this problem, a few automatic firewall optimization techniques have been proposed, but there has been no general approach to evaluate the optimality of these techniques. In this paper we present a general framework for rule-based firewall optimization. We give a precise formulation of firewall optimization as an integer programming problem and show that our framework produces optimal reordered rule sets that are semantically equivalent to the original rule set. Our framework considers the complex interactions among the rules in firewall configurations and relies on a novel partitioning of the packet space defined by the rules themselves. For validation, we employ this framework on real firewall rule sets for a quantitative evaluation of existing heuristic approaches. Our results indicate that the framework is general and faithfully captures performance benefits of firewall optimization heuristics.

Question: What is a firewall optimization framework?


Customer churn analysis : Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer's status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer's partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer's status change, or both; therefore, a customer's status change explains the relationship between churn determinants and the probability of churn. r 2006 Elsevier Ltd. All rights reserved.

Question: What is the difference between churn and defection?


Risk Assessment Uncertainties in Cybersecurity Investments. When undertaking cybersecurity risk assessments, it is important to be able to assign numeric values to metrics to compute the final expected loss that represents the risk that an organization is exposed to due to cyber threats. Even if risk assessment is motivated by real-world observations and data, there is always a high chance of assigning inaccurate values due to different uncertainties involved (e.g., evolving threat landscape, human errors) and the natural difficulty of quantifying risk. Existing models empower organizations to compute optimal cybersecurity strategies given their financial constraints, i.e., available cybersecurity budget. Further, a general game-theoretic model with uncertain payoffs (probability-distribution-valued payoffs) shows that such uncertainty can be incorporated in the game-theoretic model by allowing payoffs to be random. This paper extends previous work in the field to tackle uncertainties in risk assessment that affect cybersecurity investments. The findings from simulated examples indicate that although uncertainties in cybersecurity risk assessment lead, on average, to different cybersecurity strategies, they do not play a significant role in the final expected loss of the organization when utilising a game-theoretic model and methodology to derive these strategies. The model determines robust defending strategies even when knowledge regarding risk assessment values is not accurate. As a result, it is possible to show that the cybersecurity investments' tool is capable of providing effective decision support.

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


The''echo state''approach to analysing and training recurrent neural networks. The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen

Question: What is the echo state approach to analysing and training recurrent neural networks?


Virtual Reality and Education. Virtual Reality (VR), a new computer technology, has incredible potential in the education field. The reason for this assertion is that education is a field that requires the students to understand complex data, particularly in the study of science (see "Why is science hard to learn?" by Millar) and VR makes that task easier. VR presents information in a 3-dimensional form with the participant viewing the world from inside the world (an immersed viewpoint) with the ability to interact with the information or world. People can enter a VR world that is already created or build their own. VR's style of presentation mimics the ways that we, as humans, have learned to interact with our physical world. The requirement of learning abstract concepts (such as written language or jargon of a particular field) in order to understand data is dramatically reduced. For example, a chemistry world can show participants how electrons hover around a nucleus depending on the amount of energy involved. The abstractness of the subject would not be eliminated, but metaphors could be employed that were based solidly in reality. For instance, in the electron world the color of the world could represent the amount of energy in the system with red representing more energy than blue. For the students, they would be able to relate to hot things looking red and cold things looking blue without the concept of "joules increasing" getting in the way.

Question: What is virtual reality?


Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement. Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ($>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.

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


Gal4 in the Drosophila female germline. The modular Gal4 system has proven to be an extremely useful tool for conditional gene expression in Drosophila. One limitation has been the inability of the system to work in the female germline. A modified Gal4 system that works throughout oogenesis is presented here. To achieve germline expression, it was critical to change the basal promoter and 3'-UTR in the Gal4-responsive expression vector (generating UASp). Basal promoters and heterologous 3'-UTRs are often considered neutral, but as shown here, can endow qualitative tissue-specificity to a chimeric transcript. The modified Gal4 system was used to investigate the role of the Drosophila FGF homologue branchless, ligand for the FGF receptor breathless, in border cell migration. FGF signaling guides tracheal cell migration in the embryo. However, misexpression of branchless in the ovary had no effect on border cell migration. Thus border cells and tracheal cells appear to be guided differently.

Question: How does the modified Gal4 system work?


Cancer genome landscapes.. Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

Question: What are the 12 signaling pathways that regulate three core cellular processes?


Robot dynamics and control. This chapter presents an introduction to the dynamics and control of robot manipulators. We derive the equations of motion for a general open-chain manipulator and, using the structure present in the dynamics , construct control laws for asymptotic tracking of a desired trajectory. In deriving the dynamics, we will make explicit use of twists for representing the kinematics of the manipulator and explore the role that the kinematics play in the equations of motion. We assume some familiarity with dynamics and control of physical systems.

Question: What is the dynamics of a robot?


Millibot Trains for Enhanced Mobility. The objective of this work is to enhance the mobility of small mobile robots by enabling them to link into a train configuration capable of crossing relatively large obstacles. In particular, we are building on Millibots, semiautonomous, tracked mobile sensing/communication platforms at the 5-cm scale previously developed at Carnegie Mellon University. TheMillibot Train concept provides couplers that allow the Millibot modules to engage/disengage under computer control and joint actuators that allow lifting of one module by another and control of the whole train shape in two dimensions. A manually configurable train prototype demonstrated the ability to climb standard stairs and vertical steps nearly half the train length. A fully functional module with powered joints has been developed and several have been built and tested. Construction of a set of six modules is well underway and will allow testing of the complete train in the near future. This paper focuses on the development, design, and construction of the electromechanical hardware for the Millibot Train.

Question: How do you build a train?


On the privacy of anonymized networks. The proliferation of online social networks, and the concomitant accumulation of user data, give rise to hotly debated issues of privacy, security, and control. One specific challenge is the sharing or public release of anonymized data without accidentally leaking personally identifiable information (PII). Unfortunately, it is often difficult to ascertain that sophisticated statistical techniques, potentially employing additional external data sources, are unable to break anonymity. In this paper, we consider an instance of this problem, where the object of interest is the structure of a social network, i.e., a graph describing users and their links. Recent work demonstrates that anonymizing node identities may not be sufficient to keep the network private: the availability of node and link data from another domain, which is correlated with the anonymized network, has been used to re-identify the anonymized nodes. This paper is about conditions under which such a de-anonymization process is possible.  We attempt to shed light on the following question: can we assume that a sufficiently sparse network is inherently anonymous, in the sense that even with unlimited computational power, de-anonymization is impossible? Our approach is to introduce a random graph model for a version of the de-anonymization problem, which is parameterized by the expected node degree and a similarity parameter that controls the correlation between two graphs over the same vertex set. We find simple conditions on these parameters delineating the boundary of privacy, and show that the mean node degree need only grow slightly faster than log n with network size n for nodes to be identifiable. Our results have policy implications for sharing of anonymized network information.

Question: How to anonymize a social network?


Energy Efficient Clustering Algorithms for Wireless Sensor Networks. Energy efficiency is a major concern in wireless sensor networks (WSNs). Many clustering algorithms have been proposed for such a purpose. This paper investigates the existing clustering algorithms. The algorithms have been classified and some representatives are described in each category. After analyzing the strengths and the weaknesses of each category, an important characteristic of WSNs is pointed out for further improvement of energy efficiency for WSNs.

Question: What is the energy efficiency of clustering algorithms?


Underwater image enhancement via dark channel prior and luminance adjustment. Underwater images are degraded mainly by light scattering and color distortion. Light scattering occurs because the light is reflected and deflected multiple times by the suspended particles in the water before reaching the camera. Color distortion is caused due to the absorption degrees vary according to the light wavelength. At last, the underwater images are dominated by a bluish tone. In this paper, we propose a novel underwater image enhancement approach based on dark channel prior and luminance adjustment. The dehazed underwater image obtained by dark channel prior characterizes with low brightness. Then, we utilize a novel luminance adjustment method to enhance image contrast. The enhanced image shows improved global contrast and better preserved image details and edges. Experimental results demonstrate the effectiveness of our method compared to the state-of-the-art methods.

Question: How to enhance underwater images?


Design and Implementation of Power Converters for Hybrid Wind-Solar Energy Conversion System with an Implementation of MPPT. This paper presents the design and implementation of power converters for wind conversion systems. The power converter can not only transfer the power from a wind generator, but also improve the stability and safety of the system. The proposed system consists of a Permanent magnet synchronous generator (PMSG); a DC/DC boosts converter, a bi-directional DC/DC converter and a full-bridge DC/AC inverter. The wind generator is the main power source of the system, and the battery is used for energy storage and power compensation to recover the natural irregularity of the wind power. In this paper presents a new system configuration of the front-end rectifier stage for a hybrid wind or photo voltaic energy system. The configuration allows the two sources to supply the load separately or simultaneously, depending on the availability of energy sources. The inherent nature of this cuk-scpic fused converter, additional input filters are not necessary to filter out high frequency harmonic content is determinant for the generator life span, heating issue and efficiency. The fused multi-input rectifier stage also allows maximum power from the wind and sun. When it is available an adaptive MPPT algorithm will be used for photo voltaic (PV) system. Operational analysis of the proposed system, will discoursed in this paper simulation results are given to highlight the merit of the proposed circuit Index terms —Wind generator, PV and Fuel Cells, Bi-directional DC/DC converter, full-bridge DC/AC inverter, MPPT.

Question: How to design a power converter for a hybrid wind-solar energy conversion system?


Mometasone furoate in the treatment of vulvar lichen sclerosus: could its formulation influence efficacy, tolerability and adherence to treatment?. PURPOSE To assess the effectiveness, tolerability, and convenience of the cream formulation of mometasone furoate 0.1% (MMF) in the treatment of active vulvar lichen sclerosus (VLS) and to compare the cream with the ointment formulation.   METHODS The following efficacy parameters were assessed in 27 VLS patients treated with MMF cream for 12 weeks (group A): (i) response rate, (ii) percentage of patients achieving an improvement from baseline of ≥75% in subjective and objective scores, and (iii) mean reduction in subjective and objective scores. These efficacy assessments, as well as those regarding safety and adherence, were compared with the assessments recorded among 37 VLS patients treated with MMF ointment (group B).   RESULTS 59.3% (group A) and 78.4% (group B) of patients were considered responders; 44.4% and 40.7% of patients in group A and 54.1% and 45.9% in group B achieved an improvement of at least 75% in subjective and objective scores, respectively. MMF ointment obtained a significantly higher improvement in symptom scores in comparison with the cream formulation.   CONCLUSIONS MMF in ointment formulation seems to be more effective in treating active VLS in comparison with MMF cream. Both formulations are well tolerated and there is no difference in patient adherence and satisfaction.

Question: What is the difference between the cream and ointment formulation of mometasone furoate?


Experiences of System-Level Model-Based GUI Testing of an Android Application. This paper presents experiences in model-based graphical user interface testing of Android applications. We present how model-based testing and test automation was implemented with Android, including how applications were modeled, how tests were designed and executed, and what kind of problems were found in the tested application during the whole process. The main focus is on a case study that was performed with an Android application, the BBC News Widget. Our goal is to present actual data on the experiences and to discuss if advantages can be gained using model-based testing when compared with traditional graphical user interface testing. Another contribution of this paper is a description of a keyword-based test automation tool that was implemented for the Android emulator during the case study. All the models and the tools created or used in this case study are available as open source.

Question: How to test an android application?


Probabilistic reasoning in intelligent systems - networks of plausible inference. Probabilistic methods to create the areas, of computational tools. But I needed to get canned, bayesian networks worked recently strongly. Recently I tossed this book was published. In intelligent systems is researchers in, ai operations research excellence award for graduate. Too concerned about how it i've been. Apparently daphne koller and learning structures evidential reasoning. Pearl is a language for i've. Despite its early publication date it, is not great give the best references.

Question: What is the best reference for probabilistic reasoning in intelligent systems?


A Survey on Survey of Migration of Legacy Systems. Legacy systems are mission critical complex systems which are hard to maintain owing to shortage of skill sets and monolithic code architecture with tightly coupled tiers, all of which are indication of obsoleteness of the system technology. Thus, they have to be migrated to the latest technology(ies). Migration is an offspring research in Software Engineering which is almost three decades old research and numerous publications have emerged in many topics in the migration domain with focus areas of code migration, architecture migration, case study on migration and effort estimation on migration. In addition, various survey works surveying different aspects of migration have also emerged. This paper provides a survey of these survey works in order to provide a consolidation of these survey works. As an outcome of the survey on survey of migration, a road map of migration evolution from the early stages to the recent works on migration comprising of significant milestones in migration research has been presented in this work.

Question: What is a legacy system?


UMAN WAYFINDING IN UNFAMILIAR BUILDINGS : A SIMULATION WITH A COGNIZING AGENT. Existing cognitively based computational models for wayfinding focus primarily on the exploration of mental representations rather than the information needs for wayfinding. It is important to consider information needs because people trying to find their ways in unfamiliar environments do not have a previously acquired mental representation but depend on external information. The fundamental tenet of this work is that all information must be presented to the wayfinder at each decision point as "knowledge in the world." Simulating people's wayfinding behavior in a cognitively plausible way requires the integration of structures for information perception and cognition in the underlying model. In this work we use a cognizing agent to simulate people's wayfinding processes in unfamiliar buildings. The agent-based model consists of two tiers: simulated states of the environment and simulated beliefs of the agent. The agent is modeled with state, an observation schema, a specific wayfinding strategy, and commonsense knowledge. The environment is modeled as a graph, where nodes represent decision points and edges represent lines of movement. The perceptual wayfinding model integrates the agent and its environment within a "Sense-Plan-Act" framework. It focuses on knowledge in the world to explain actions of the agent. The concepts of affordance and information are used to describe the kinds of knowledge the agent derives from the world by means of visual perception. Affordances are possibilities for action with reference to the agent. Information is necessary for the agent to decide which affordances to utilize. During the navigation process the agent accumulates beliefs about the environment by observing task-relevant affordances and information at decision points. The utilization of a "go-to" affordance leads the agent from one node to another where it is again provided with percepts. A succ

Question: How do you know what to do?


Smart Configuration of Smart Environments. One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to "program" their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.

Question: How can we configure smart environments?


Smart-cap for pedestrian safety. In the last few decades, the use of mobile phones have increased rapidly. Safety experts have considered dependency on mobile phones as a threat to the pedestrian safety. In this paper, a wearable smart-cap has been proposed to detect an obstacle in the path of a pedestrian mobile user. The vision of the proposed system is to provide a user-friendly, cost effective and a quick way to avoid pedestrian accidents due to mobile phones. The volunteers' feedback and testing results have been outstanding. The volunteers have reported that the proposed system is user-friendly and accurate.

Question: What is a smart-cap?


Context-Aware Bandits. In this paper, we present a simple and efficient Context-Aware Bandit (CAB) algorithm. With CAB we attempt to craft a bandit algorithm that can capture collaborative effects and that can be easily deployed in a real-world recommendation system, where the multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering technique augmenting exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We provide a theoretical analysis in the standard stochastic multi-armed bandits setting. We demonstrate the efficiency of our approach on production and real-world datasets, showing the scalability and, more importantly, the significantly increased prediction performance against several existing state-of-theart methods.

Question: What is the cold-start problem?


Linking received packet to the transmitter through physical-fingerprinting of controller area network. The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.

Question: How to link CAN packet to the transmitter?


Microcephaly and early-onset nephrotic syndrome —confusion in Galloway-Mowat syndrome. We report a 2-year-old girl with nephrotic syndrome, microcephaly, seizures and psychomotor retardation. Histological studies of a renal biopsy revealed focal glomerular sclerosis with mesangiolysis and capillary microaneurysms. Dysmorphic features were remarkable: abnormal-shaped skull, coarse hair, narrow forehead, large low-set ears, almond-shaped eyes, low nasal bridge, pinched nose, thin lips and micrognathia. Cases with this rare combination of microcephaly and early onset of nephrotic syndrome with various neurological abnormalities have been reported. However, clinical manifestations and histological findings showed a wide variation, and there is a lot of confusion in this syndrome. We therefore reviewed the previous reports and propose a new clasification of this syndrome.

Question: What is the name of this syndrome?


Boosting Algorithms for Parallel and Distributed Learning. The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less m

Question: What is the difference between boosting and bagging?


Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks. Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes 'normal' from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging fr

Question: How do early-life stress and maternal care affect the brain?


Benchmarking Deep Reinforcement Learning for Continuous Control. Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released open-source in order to facilitate experimental reproducibility and to encourage adoption by other researchers.

Question: What is a benchmark suite of continuous control tasks?


An Efficient Active Learning Framework for New Relation Types. Supervised training of models for semantic relation extraction has yielded good performance, but at substantial cost for the annotation of large training corpora. Active learning strategies can greatly reduce this annotation cost. We present an efficient active learning framework that starts from a better balance between positive and negative samples, and boosts training efficiency by interleaving self-training and co-testing. We also studied the reduction of annotation cost by enforcing argument type constraints. Experiments show a substantial speed-up by comparison to the previous state-of-the-art pure co-testing active learning framework. We obtain reasonable performance with only 150 labels for individual ACE 2004 relation

Question: What is the cost of annotating a corpus?


Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments. This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.

Question: What is the difference between discrete and continuous?


Estimating the class prior and posterior from noisy positives and unlabeled data. We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and parametric and nonparametric algorithms proposed here constitute an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.

Question: How to estimate the class prior and posterior from noisy positives and unlabeled data.


Detection of obstructive sleep apnea through ECG signal features. Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.

Question: How can we detect sleep apnea?


Automated detection of euro pallet loads by interpreting PMD camera depth images. In this study, a novel approach for the detection of parcel loading positions on a pallet is presented. This approach was realized as a substantial change in comparison with traditional system design of contour detection in de-palletizing processes. Complex 3D-vision systems, costly laser scanners or throughput decreasing local sensor solutions integrated in grippers are substituted by a lowcost photonic mixing device (PMD) camera. By combining PMD technology and a predetermined model of loading situations, stored during assembling the pallet, this approach can compensate for the drawbacks of each respective system. An essential part of the approach are computer-graphics methods specific to the given problem to both detect the deviation between the nominal and the actual loading position and if necessary an automated correction of the packaging scheme. From an economic point of view, this approach can decrease the costs of mandatory contour checking in automated de-palletizing processes.

Question: What is the difference between a pallet and a container?


Algorithms for Quantum Computation: Discrete Logarithms and Factoring. A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factol: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. (We thus give the first examples of quantum cryptanulysis.)

Question: What is a quantum computer?


Learning from Corrupted Binary Labels via Class-Probability Estimation. Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each label may be flipped with some constant probability (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corruption processes belonging to the mutually contaminated distributions framework (Scott et al., 2013), with three conclusions. First, one can optimise balanced error and AUC without knowledge of the corruption parameters. Second, given estimates of the corruption parameters, one can minimise a range of classification risks. Third, one can estimate corruption parameters via a class-probability estimator (e.g. kernel logistic regression) trained solely on corrupted data. Experiments on label noise tasks corroborate our analysis. 1. Learning from corrupted binary labels In many practical scenarios involving learning from binary labels, one observes samples whose labels are corrupted versions of the actual ground truth. For example, in learning from class-conditional label noise (CCN learning), the labels are flipped with some constant probability (Angluin & Laird, 1988). In positive and unlabelled learning (PU learning), we have access to some positive samples, but in lieu of negative samples only have a pool of samples whose label is unknown (Denis, 1998). More generally, suppose there is a notional clean distribution D over instances and labels. We say a problem involves learning from corrupted Proceedings of the 32 International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copyright 2015 by the author(s). binary labels if we observe training samples drawn from some corrupted distribution Dcorr such that the observed labels do not represent those we would observe under D. A funda

Question: What is the difference between a corrupted binary label and a corrupted positive and unlabelled data?


A tourism destination recommender system using users' sentiment and temporal dynamics. With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.

Question: What is the tourism destination recommender system?