A comprehensive security architecture for SDN. SDN enables the administrators to configure network resources very quickly and to adjust network-wide traffic flow to meet changing needs dynamically. However, there are some challenges for implementing a full-scale carrier SDN. One of the most important challenges is SDN security, which is beginning to receive attention. With new SDN architecture, some security threats are common to traditional networking, but the profile of these threats (including their likelihood and impact and hence their overall risk level) changes. Moreover, there are some new security challenges such as bypassing predefined mandatory policies by overwriting flow entries and data eavesdropping by inserting fraudulent flow entries. This paper is to design open-flow specific security solutions and propose a comprehensive security architecture to provide security services such as enforcing mandatory network policy correctly and receiving network policy securely for SDN in order to solve these common security issues and new security challenges. It can also help the developers to implement security functions to provide security services when developing the SDN controller.

Question: What is the security architecture for SDN?


Approximate Gaussian Elimination. Approximate Gaussian Elimination

Question: What is approximate Gaussian elimination?


System design of the internet of things for residential smart grid. The Internet of Things envisions integration, coordination, communication, and collaboration of real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this article studies the design of a large-scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a back-end server, a unified home gateway (UHG) at the end users, and a user interface for mobile devices. We discuss the features of such an IoT system to support a large-scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using XMPP and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.

Question: What is the design of a large-scale IoT system for residential smart grid?


A Software Gamification Model for Cross-Cultural Software Development Teams. Gamification is the use of game design elements in non-game context platforms such as services and marketing to motivate people to participate in planned activities to increase engagement and loyalty to achieve goals.  Gamification has been applied to academic fields including software engineering in recent years. Some studies show that gamification can motivate engineers in Software Engineering (SE) if applied appropriately. However, most gamification implementations are lacking well-defined frameworks or models.  This paper develops a software gamification model (SGM) with a well-defined framework that provides a robust process for implementing gamification for SE. This model also contains elements from applied psychology, Social Software Engineering (SSE), and the Capability Maturity Model (CMM). Having those elements ensures that the model has a solid foundation in gamification and SE disciplines. The model is further tailored for cross-cultural software development teams (CCSDT).  CCSDT have been popular in recent years due to the fact that software development has become a global business. However, as more and more CCSDT are formed, many challenges and issues have been raised in those cross-cultural environments stemming from miscommunication, misunderstanding, cultural differences and conflicts. This paper uses the SGM to help resolve the issues.

Question: What is a software gamification model?


An Initial Investigation of Test Driven Development in Industry. Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. In our research, we ran a set of structured experiments with 24 professional pair programmers. One group developed code using TDD while the other a waterfall-like approach. Both groups developed a small Java program. We found that the TDD developers produced higher quality code, which passed 18% more functional black box test cases. However, TDD developer pairs took 16% more time for development. A moderate correlation between time spent and the resulting quality was established upon analysis. It is conjectured that the resulting high quality of code written using the TDD practice may be due to the granularity of TDD, which may encourage more frequent and tighter verification and validation. Lastly, the programmers which followed a waterfall-like process often did not write the required automated test cases after completing their code, which might be indicative of the tendency among practitioners toward inadequate testing. This observation supports that TDD has the potential of increasing the level of testing in the industry as testing as an integral part of code development.

Question: What is the difference between TDD and waterfall?


The IHS Transformations Based Image Fusion. The IHS sharpening technique is one of the most commonly used techniques for sharpening. Different transformations have been developed to transfer a color image from the RGB space to the IHS space. Through literature, it appears that, various scientists proposed alternative IHS transformations and many papers have reported good results whereas others show bad ones as will as not those obtained which the formula of IHS transformation were used. In addition to that, many papers show different formulas of transformation matrix such as IHS transformation. This leads to confusion what is the exact formula of the IHS transformation?. Therefore, the main purpose of this work is to explore different IHS transformation techniques and experiment it as IHS based image fusion. The image fusion performance was evaluated, in this study, using various methods to estimate the quality and degree of information improvement of a fused image quantitatively.

Question: What is the IHS transformation?


The Role of Adult Attachment Styles in Psychopathology and Psychotherapy Outcomes. Attachment theory provides a model for understanding how the attachment styles formed in infancy systematically affect subsequent psychological functioning across the life span. Attachment styles provide the cognitive schemas, or working models, through which individuals perceive and relate to their worlds. In turn, these schemas predispose the development of psychopathologies and influence outcomes when people undergo psychotherapy. After reviewing recent empirical findings, the authors conclude that an understanding of attachment theory facilitates the conceptualization of clients' problems and the selection of appropriate interventions. Accordingly, attachment styles should be assessed as a standard part of treatment planning. Furthermore, the authors propose that attachment styles should be assessed as individual difference variables in psychotherapy outcome research because adult attachment styles dictate how people perceive and respond to their environments and, therefore, how clients respond differentially to various treatments.

Question: What is the role of attachment styles in psychotherapy?


NEXMark – A Benchmark for Queries over Data Streams DRAFT. A lot of research has focused recently on executing queries over data streams. This recent attention is due to the large number of data streams available, and the desire to get answers to queries on these streams in real time. There are many sources of data streams: environmental sensor networks, network routing traces, financial transactions and cell phone call records. Many systems are currently under development to execute queries over data streams [BW01, CCC02, MF02, NDM00, SH98]. Further, many ideas have been presented to improve the execution of queries over data streams [ABB02, MSHR02, TMSF02]. It is important to be able to measure the effectiveness of this work. To this end, we present the Niagara Extension to XMark benchmark (NEXMark). This is a work in progress. We are circulating it now for feedback. We have three goals: To define a benchmark, to provide stream generators, and to define metrics for measuring queries over continuous data streams. The XMark benchmark [SWK01] is designed to measure the performance of XML repositories. The benchmark provides a data generator that models the state of an auction in XML format, and various queries over the generated data. An abbreviated schema for XMark is shown in Figure 1.

Question: What is the abbreviation for the benchmark?


Visualising the structure of architectural open spaces based on shape analysis. Visualisation and interpretation of architectural spaces are intellectually challenging and multifaceted exercises with fairly wide ranging applications and implications. In the context of urban planning, they are most commonly undertaken to evaluate the usage of the architectural space to ensure efficient navigation and accessibility [1]. These exercises clearly assume a certain influence of the built structures on the human cognition. However, what aspect of architectural space affects the human behaviour still remains an open debate. In this respect, it is closely similar to an exercise to identify the unknown visual variables in information visualization. Since a quantitative analysis of the architectural geometric structure on a large scale will be a daunting computational task, the open space bounded by the built structures is studied instead [2]. The study of architectural open spaces essentially involves the computation of the visibility polygon or isovist (space visible all around a viewpoint, see Figure 1a) from a viewpoint and calculating various shape measures of the visibility polygon. The isovist computation involves drawing rays

Question: What is the isovist?


On multiple foreground cosegmentation. In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur cross the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In particular, our approach is flexible enough to integrate any advanced region classifiers for foreground modeling, and our region assignment employs a combinatorial auction framework that enjoys several intuitively good properties such as optimality guarantee and linear complexity. We show the superior performance of our method in both segmentation quality and scalability in comparison with other state-of-the-art techniques on a newly introduced FlickrMFC dataset and the standard ImageNet dataset.

Question: What is the difference between foreground and background?


Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution. Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.

Question: How to recover corrupted data in sensor networks?


Data Mining and Opinion Mining: A Tool in Educational Context. The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.

Question: What is the objective of this research?


Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception. This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.

Question: What is the difference between IoT and IoT?


Harlequin fetus.. We report a case of harlequin fetus born to the consanguineous parents. She had the typical skin manifestations of thick armour like scales with fissures, complete ectropion and eclabium, atrophic and crumpled ears and swollen extremities with gangrenous digits. Supportive treatment was given but the neonate died on the 4th day.

Question: What is harlequin fetus?


Auditory Saliency Using Natural Statistics. In contrast to the wealth of saliency models in the vision literature, there is a relative paucity of models exploring auditory saliency. In this work, we integrate the approaches of (Kayser, Petkov, Lippert, & Logothetis, 2005) and (Zhang, Tong, Marks, Shan, & Cottrell, 2008) and propose a model of auditory saliency. The model combines the statistics of natural soundscapes and the recent past of the input signal to predict the saliency of an auditory stimulus in the frequency domain. To evaluate the model output, a simple behavioral experiment was performed. Results show the auditory saliency maps calculated by the model to be in excellent accord with human judgments of saliency.

Question: What is the difference between the visual and auditory saliency models?


SEQUEL: A Structured English Query Language. In this paper we present the data manipulation facility for a structured English query language (SEQUEL) which can be used for accessing data in an integrated relational data base. Without resorting to the concepts of bound variables and quantifiers SEQUEL identifies a set of simple operations on tabular structures, which can be shown to be of equivalent power to the first order predicate calculus. A SEQUEL user is presented with a consistent set of keyword English templates which reflect how people use tables to obtain information. Moreover, the SEQUEL user is able to compose these basic templates in a structured manner in order to form more complex queries. SEQUEL is intended as a data base sublanguage for both the professional programmer and the more infrequent data base user.

Question: What is a structured English query language?


Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues. This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.

Question: How do you ground new words on the physical world?


Interactive bookshelf surface for in situ book searching and storing support. We propose an interactive bookshelf surface to augment a human ability for in situ book searching and storing. In book searching support, when a user touches the edge of the bookshelf, the cover image of a stored book located above the touched position is projected directly onto the book spine. As a result, the user can search for a desired book by sliding his (or her) finger across the shelf edge. In book storing support, when a user brings a book close to the bookshelf, the place where the book should be stored is visually highlighted by a projection light. This paper also presents sensing technologies to achieve the above mentioned interactive techniques. In addition, by considering the properties of the human visual system, we propose a simple visual effect to reduce the legibility degradation of the projected image contents by the complex textures and geometric irregularities of the spines. We confirmed the feasibility of the system and the effectiveness of the proposed interaction techniques through user studies.

Question: What is the bookshelf surface for in situ book searching and storing?


Anaerobic biotechnology for industrial wastewater treatment.. Microbiological formation of methane has been occurring naturally for ages in such diverse habitats as marshes, rice paddies, benthic deposits, deep ocean trenches, hot springs, trees, cattle, pigs, iguanas, termites, and human beings (Mah and Smith, 1981; Steggerda and Dimmick, 1966; Prins, 1979; Balch et al., 1979). In the past decade, interest in anaerobic biotechnology has grown considerably, both in the harnessing of the process for industrial wastewater treatment and in the bioconversion of crop-grown biomass to methane (Sheridan, 1982; Chynoweth and Srivastava, 1980).

Question: What is anaerobic biotechnology?


Featureless Visual Processing for SLAM in Changing Outdoor Environments. Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

Question: What is the difference between a low cost camera and a consumer camera?


Cannabidiol--recent advances.. The aim of this review is to present some of the recent publications on cannabidiol (CBD; 2), a major non-psychoactive constituent of Cannabis, and to give a general overview. Special emphasis is laid on biochemical and pharmacological advances, and on novel mechanisms recently put forward, to shed light on some of the pharmacological effects that can possibly be rationalized through these mechanisms. The plethora of positive pharmacological effects observed with CBD make this compound a highly attractive therapeutic entity.

Question: What is the pharmacological effects of cannabidiol?


Effectiveness of Credit Management System on Loan Performance : Empirical Evidence from Micro Finance Sector in Kenya. Microfinance institutions in Kenya experience high levels of non-performing loans. This trend threatens viability and sustainability of MFIs and hinders the achievement of their goals. This study was aimed at assessing the effectiveness of credit management systems on loan performance of microfinance institutions. Specifically we sought to establish the effect of credit terms, client appraisal, credit risk control measures and credit collection policies on loan performance. We adopted a descriptive research design. The respondents were the credit officers of the MFIs in Meru town. Collection policy was found to have a higher effect on loan repayment with =12.74, P=0.000 at 5% significance level. Further research is recommended on the effectiveness of credit referencing on loan performance of MFIs. This study is informative in terms of public policy adjustments and firm level competences required for better operation of MFIs and it also contributes to credit management literature.

Question: What is the effect of credit referencing on loan performance of microfinance institutions?


A low cost ultra-wideband pulse transceiver. A low cost pulse transceiver has been developed for measuring the electrical properties of materials. The transceiver generates an ultra wide band (UWB) pulse as well as samples the received pulse. The pulse generator has been designed using a silicon-germanium (SiGe) analog comparator which is a possible alternative to step recovery diodes. The pulse is received by an extended time sampling circuit. The sampling circuit presented is an alternative design that does not require the use of a broadband balun.

Question: What is an ultra wideband pulse transceiver?


Parameterisation of a stochastic model for human face identification. Stochastic modelling of non-stationary vector timeseries based on HMMs has been very successful for speech applications [5]. Recently it has been applied to a range of image recognition problems [7, 9]. Previously reported work [6] has investigated the use of HMMs to model human faces for identi cation purposes. Faces can be intuitively divided into regions such as the mouth, eyes, nose, etc., and these regions can be associated with the states of an HMM. The identi cation performance of a top-bottom HMM compares favourably with some of the well-known algorithms, for example eigenfaces as detailed in [8]. However, the HMM parameterisation in the work presented so far was arrived at by subjective intuition. This paper presents experimental results which show how identi cation rates vary with HMM parameters, and which indicate the most sensible choice of parameters. The paper is organised as follows: section 2 gives an overview of the HMM-based approach; section 3 details the training and recognition processes; section 4 describes the experimental setup; section 5 presents the identi cation results; section 6 concludes the paper.

Question: How to parameterise a stochastic model for human face identification?


Analytical Solution of Air-Gap Field in Permanent-Magnet Motors Taking Into Account the Effect of Pole Transition Over Slots. We present an analytical method to study magnetic fields in permanent-magnet brushless motors, taking into consideration the effect of stator slotting. Our attention concentrates particularly on the instantaneous field distribution in the slot regions where the magnet pole transition passes over the slot opening. The accuracy in the flux density vector distribution in such regions plays a critical role in the prediction of the magnetic forces, i.e., the cogging torque and unbalanced magnetic pull. However, the currently available analytical solutions for calculating air-gap fields in permanent magnet motors can estimate only the distribution of the flux density component in the radial direction. Magnetic field and forces computed by the new analytical method agree well with those obtained by the finite-element method. The analytical method provides a useful tool for design and optimization of permanent-magnet motors.

Question: How to calculate the magnetic field in a permanent magnet motor?


Knowledge sharing behavior of physicians in hospitals. Sharing knowledge of Physicians within hospitals can realize potential gains enormously and is critical to be successful and survive in competitive environments. There is a need for empirical research to identify the factors that determine physician's behavior to share knowledge. This study investigates the factors that determine the physician's individual knowledge sharing behavior in his/her department. The purpose of this study is to examine empirically the physicians' knowledge sharing behavior The research models under investigation are the Theory of Reasoned Action (TRA) model and the Theory of Planned Behavior (TPBI model. These models are empirically examined and compared, using the survey results on physicians' knowledge sharing behavior collected from 286 physicians practicing in 28 departments in 13 tertiary hospitals in Korea. TPB model exhibited a good fit with the data and appeared to be superior to TRA in explaining physicians' intentions to share knowledge. Amended TPB model provided an important improvement In fit over that of original TPB model. In amended TPB model, subjective norms were found to have the strongest total effects on behavioral intentions to share knowledge of physicians through direct and indirect path by attitude. Attitude was found to be the second important factor influencing physicians' intentions. Perceived behavioral control was also found to have effect on the intentions to share knowledge though it was weaker than that of subjective norms or attitude. The implications for physician's knowledge sharing activities are discussed

Question: What is the difference between the theory of reasoned action and the theory of planned behavior?


Flexible Container-Based Computing Platform on Cloud for Scientific Workflows. Cloud computing is expected to be a promising solution for scientific computing. In this paper, we propose a flexible container-based computing platform to run scientific workflows on cloud. We integrate Galaxy, a popular biology workflow system, with four famous container cluster systems. Preliminary evaluation shows that container cluster systems introduce negligible performance overhead for data intensive scientific workflows, meanwhile, they are able to solve tool installation problem, guarantee reproducibility and improve resource utilization. Moreover, we implement four ways of using Docker, the most popular container tool, for our platform. Docker in Docker and Sibling Docker, which run everything within containers, both help scientists easily deploy our platform on any clouds in a few minutes.

Question: What is a container-based computing platform?


Introduction of speech log-spectral priors into dereverberation based on Itakura-Saito distance minimization. It has recently been shown that a multi-channel linear prediction can effectively achieve blind speech dereverberation based on maximum-likelihood (ML) estimation. This approach can estimate and cancel unknown reverberation processes from only a few seconds of observation. However, one problem with this approach is that speech distortion may increase if we iterate the dereverberation more than once based on Itakura-Saito (IS) distance minimization to further reduce the reverberation. To overcome this problem, we introduce speech log-spectral priors into this approach, and reformulate it based on maximum a posteriori (MAP) estimation. Two types of priors are introduced, a Gaussian mixture model (GMM) of speech log spectra, and a GMM of speech mel-frequency cepstral coefficients. In the formulation, we also propose a new versatile technique to integrate such log-spectral priors with the IS distance minimization in a computationally efficient manner. Preliminary experiments show the effectiveness of the proposed approach.

Question: How to integrate speech log-spectral priors into dereverberation based on Itakura-Saito distance minimization?


VMTP: a transport protocol for the next generation of communication systems. The Versatile Message Transaction Protocol (VMTP) is a transport-level protocol designed to support remote procedure call, multicast and real-time communication. The protocol is optimized for efficient page-level network file access in particular. In this paper, we describe the significant aspects of the VMTP design, including the VMTP treatment of sessions, addressing, duplicate suppression, flow control and retransmissions plus its provision for multicast. The VMTP design reflects a change in the use of computer communication as well as a change in the underlying hardware base for the next generation of communication systems. It also challenges certain established notions in the design of protocols.

Question: What is the next generation of communication systems?


Tooth survival following non-surgical root canal treatment: a systematic review of the literature.. AIMS To investigate (i) the effect of study characteristics on reported tooth survival after root canal treatment (RCTx) and (ii) the effect of clinical factors on the proportion of root filled teeth surviving after RCTx.   METHODOLOGY Longitudinal human clinical studies investigating tooth survival after RCTx which were published up to the end of 2007 were identified electronically (MEDLINE and Cochrane database 1966-2007 December, week 4). In addition, four journals (Dental Traumatology, International Endodontic Journal, Journal of Endodontics, Oral Surgery Oral Medicine Oral Pathology Oral Radiology & Endodontics), bibliographies of all relevant articles and review articles were hand searched. Two reviewers (Y-LN, KG) assessed and selected the studies based on specified inclusion criteria and extracted the data onto a pre-designed proforma, independently. The criteria were as follows: (i) clinical study on RCTx; (ii) stratified analysis of primary and secondary RCTx available; (iii) sample size given and larger than 10; (iv) at least 6-month postoperative review; (v) success based on survival of tooth; and (vi) proportion of teeth surviving after treatment given or could be calculated from the raw data. Three strands of evidence or analyses were used to triangulate a consensus view. The reported findings from individual studies, including those excluded for quantitative analysis, were utilized for the intuitive synthesis, which constituted the first strand of evidence. Secondly, the pooled weighted proportion of teeth surviving and thirdly the combined effects of potential prognostic factors were estimated using the fixed and random effects meta-analyses on studies fulfilling all the inclusion criteria.   RESULTS Of the 31 articles identified, 14 studies published between 1993 and 2007 were included. The majority of studies were retrospective (n = 10) and only four

Question: What is the effect of study characteristics on reported tooth survival after root canal treatment?


DO INVESTMENT-CASH FLOW SENSITIVITIES PROVIDE USEFUL MEASURES OF FINANCING CONSTRAINTS?*. No. This paper investigates the relationship between financing constraints and investment-cash flow sensitivities by analyzing the firms identified by Fazzari, Hubbard, and Petersen as having unusually high investment-cash flow sensitivities. We find that firms that appear less financially constrained exhibit significantly greater sensitivities than firms that appear more financially constrained. We find this pattern for the entire sample period, subperiods, and individual years. These results (and simple theoretical arguments) suggest that higher sensitivities cannot be interpreted as evidence that firms are more financially constrained. These findings call into question the interpretation of most previous research that uses this methodology.

Question: What is the relationship between financing constraints and investment-cash flow sensitivities?


Feature selection based on rough sets and particle swarm optimization. We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.

Question: What is the difference between rough sets and rough sets?


Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions. Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.

Question: How to track the global power peak under partially shaded conditions?


An ontology for clinical questions about the contents of patient notes. OBJECTIVE Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes.   METHODS There are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions.   RESULTS Through the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution.   CONCLUSIONS In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by t

Question: What is the ontology for clinical questions about the contents of patient notes?


Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation. Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.

Question: What is the difference between node, node-link, and node-link-group diagrams?


Detecting Code Smells in Python Programs. As a traditional dynamic language, Python is increasingly used in various software engineering tasks. However, due to its flexibility and dynamism, Python is a particularly challenging language to write code in and maintain. Consequently, Python programs contain code smells which indicate potential comprehension and maintenance problems. With the aim of supporting refactoring strategies to enhance maintainability, this paper describes how to detect code smells in Python programs. We introduce 11 Python smells and describe the detection strategy. We also implement a smell detection tool named Pysmell and use it to identify code smells in five real world Python systems. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. It reveals that Large Class and Large Method are most prevalent. Our experiment also implies Python programs may be suffering code smells further.

Question: How to detect code smells in python programs?


Fixed-point algorithms for learning determinantal point processes. Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.

Question: What is a determinantal point process?


An Edge Driven Wavelet Frame Model for Image Restoration. Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.

Question: What is an edge driven wavelet frame model?


Query optimization using column statistics in hive. Hive is a data warehousing solution on top of the Hadoop MapReduce framework that has been designed to handle large amounts of data and store them in tables like a relational database management system or a conventional data warehouse while using the parallelization and batch processing functionalities of the Hadoop MapReduce framework to speed up the execution of queries. Data inserted into Hive is stored in the Hadoop FileSystem (HDFS), which is part of the Hadoop MapReduce framework. To make the data accessible to the user, Hive uses a query language similar to SQL, which is called HiveQL. When a query is issued in HiveQL, it is translated by a parser into a query execution plan that is optimized and then turned into a series of map and reduce iterations. These iterations are then executed on the data stored in the HDFS, writing the output to a file.  The goal of this work is to to develop an approach for improving the performance of the HiveQL queries executed in the Hive framework. For that purpose, we introduce an extension to the Hive MetaStore which stores metadata that has been extracted on the column level of the user database. These column level statistics are then used for example in combination with join ordering algorithms which are adapted to the specific needs of the Hadoop MapReduce environment to improve the overall performance of the HiveQL query execution.

Question: What is the difference between a relational database and a data warehouse?


A survey of performance measures to evaluate ego-lane estimation and a novel sensor-independent measure along with its applications. Lane estimation plays a central role for Driver Assistance Systems, therefore many approaches have been proposed to measure its performance. However, no commonly agreed metric exists. In this work, we first present a detailed survey of the current measures. Most of them apply pixel-level benchmarks on camera images and require a time-consuming and fault-prone labeling process. Moreover, these metrics cannot be used to assess other sources such as the detected guardrails, curbs or other vehicles. Therefore, we introduce an efficient and sensor-independent metric, which provides an objective and intuitive self-assessment for the entire road estimation process at multiple levels: individual detectors, lane estimation itself, and the target applications (e.g., lane keeping system). Our metric does not require a high labeling effort and can be used both online and offline. By selecting the evaluated points in specific distances, it can be applied to any road model representation. By comparing in 2D vehicle coordinate system, two possibilities exist to generate the ground-truth: the human-driven path or the expensive alternative with DGPS and detailed maps. This paper applies both methods and reveals that the human-driven path also qualifies for this task and it is applicable to scenarios without GPS signal, e.g., tunnel. Although the lateral offset between reference and detection is widely used in the majority of works, this paper shows that another criterion, the angle deviation, is more appropriate. Finally, we compare our metric with other state-of-the-art metrics using real data recordings from different scenarios.

Question: What is the self-assessment of ego-lane estimation?


Optimal Power Flow by Black Hole Optimization Algorithm. In this paper, a black hole optimization algorithm (BH) is utilized to solve the optimal power flow problem considering the generation fuel cost, reduction of voltage deviation and improvement of voltage stability as an objective functions. The black hole algorithm simulate the black hole phenomenon which relay on tow operations, the star absorption and star sucking. The IEEE 30-Bus and IEEE 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures.

Question: What is the black hole optimization algorithm?


CLOUDS: A Decision Tree Classifier for Large Datasets. Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifters, while maintaining the quality of the generated trees in terms of accuracy and tree size. We provide experimental results with a number of real and synthetic data~ets.

Question: What is a decision tree classifier?


Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study. The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.

Question: What is the difference between a genetics-based machine learning algorithm and a classical machine learning algorithm?


Understanding Malvertising Through Ad-Injecting Browser Extensions. Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.

Question: What is malvertising?


Scaling Human-Object Interaction Recognition Through Zero-Shot Learning. Recognizing human object interactions (HOI) is an important part of distinguishing the rich variety of human action in the visual world. While recent progress has been made in improving HOI recognition in the fully supervised setting, the space of possible human-object interactions is large and it is impractical to obtain labeled training data for all interactions of interest. In this work, we tackle the challenge of scaling HOI recognition to the long tail of categories through a zero-shot learning approach. We introduce a factorized model for HOI detection that disentangles reasoning on verbs and objects, and at test-time can therefore produce detections for novel verb-object pairs. We present experiments on the recently introduced large-scale HICODET dataset, and show that our model is able to both perform comparably to state-of-the-art in fully-supervised HOI detection, while simultaneously achieving effective zeroshot detection of new HOI categories.

Question: How do you scale human-object interaction recognition?


Design of a 100 W, 500000 rpm permanent-magnet generator for mesoscale gas turbines. Mesoscale gas turbine generator systems are a promising solution for high energy and power density portable devices. This paper focuses on the design of a 100 W, 500000 rpm generator suitable for use with a gas turbine. The design procedure selects the suitable machine type and bearing technology, and determines the electromagnetic characteristics. The losses caused by the high frequency operation are minimized by optimizing the winding and the stator core material. The final design is a permanent-magnet machine with a volume of 3.5 cm/sup 3/ and experimental measurements from a test bench are presented.

Question: What is the volume of a permanent-magnet generator?


A Singular Value Thresholding Algorithm for Matrix Completion. This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem and arises in many important applications as in the task of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices {Xk ,Y k}, and at each step mainly performs a soft-thresholding operation on the singular values of the matrix Y k. There are two remarkable features making this attractive for low-rank matrix completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {Xk} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal storage space and keep the computational cost of each iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence of iterates converges. On the practical side, we provide numerical examples in which 1, 000 × 1, 000 matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate that our approach is amenable to very large scale problems by recovering matrices of rank about 10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are connected with the recent literature on linearized Bregman iterations for 1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagran

Question: What is the singular value thresholding algorithm?


1 USES OF TECHNOLOGY THAT SUPPORT PUPILS WITH SPECIAL EDUCATIONAL NEEDS.

Question: What is the difference between a special educational need and a special educational requirement?


Multi-target tracking scheme using a track management table for automotive radar systems. In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.

Question: What is a track management table for automotive radars?


Learning from positive and unlabeled examples. In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications. © 2005 Elsevier B.V. All rights reserved.

Question: What is the difference between positive and unlabeled examples?


A hybrid approach for database intrusion detection at transaction and inter-transaction levels. Nowadays, information plays an important role in the organizations. Sensitive information is often stored within the database. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. Therefore, the existence of Intrusion Detection Systems in the database is a necessity. In this paper, we propose a type of intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level). For this purpose, we propose a detection method at transaction level, which is based on describing the expected transactions within the database applications. Then at inter-transaction level, we propose a detection method that is based on anomaly detection and uses data mining to find dependency and sequence rules. The advantage of this system compared to the previous database intrusion detection systems is that it can detect malicious behaviors in both transaction and inter-transaction levels. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative errors. In order to evaluate the accuracy of the proposed system, some experiments have been done. The experimental evaluation results show high accuracy and effectiveness of the proposed system.

Question: What is a hybrid method?


Receiver Design for a Bionic Nervous System: Modeling the Dendritic Processing Power. Intrabody nanonetworks for nervous system monitoring are envisioned as a key application of the Internet of Nano-Things (IoNT) paradigm, with the aim of developing radically new medical diagnosis and treatment techniques. Indeed, very recently, bionic devices have been implanted inside a living human brain as innovative treatment for drug-resistant epilepsy. In this context, this paper proposes a systems-theoretic communication model to capture the actual behavior of biological neurons. Specifically, biological neurons exhibit physical extension due to their projections called dendrites, which propagate the electrochemical stimulation received via synapses to the soma. Experimental evidences show that the dendrites exhibit two main features: 1) the compartmentalization at the level of the dendritic branches of the neuronal processes and 2) the location-dependent preference for different frequencies. Stemming from these experimental evidences, we propose to model the dendritic tree as a spatiotemporal filter bank, where each filter models the behavior in both space and time of a dendritic branch. Each filter is fully characterized along with the overall neuronal response. Furthermore, sufficient conditions on the incoming stimulus for inducing a null-neuronal response are derived. The conducted theoretical analysis shows that: 1) the neuronal information is encoded in the stimulus temporal pattern, i.e., it is possible to select the neuron to affect by changing the stimulus frequency content; in this sense, the communication among neurons is frequency-selective and 2) the spatial distribution of the dendrites affects the neuronal response; in this sense, the communication among neurons is spatial-selective. The theoretical analysis is validated through a real neuron morphology.

Question: How does the dendritic tree affect the neuronal response?


A Proximity-Aware Hierarchical Clustering of Faces. In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clus- ters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging un- constrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.

Question: How does the proposed algorithm work?


A Finger-Vein Based Cancellable Bio-cryptosystem. Irrevocability is one major issue in existing bio-cryptosystems. In this paper, we proposed a cancellable bio-cryptosystem by taking the full advantage of cancellable and non-invertible properties of bio-hashing biometrics. Specifically, two transformed templates are generated by using the bio-hashing algorithm and applied into two different secure sketches, fuzzy commitment sketch and fuzzy vault sketch, respectively. These two secure sketches can be fused in two different ways: AND fusion and OR fusion, so as to emphasis either on the recognition accuracy or the security level of the system. Experimental results and security analysis show the validity of the proposed scheme.

Question: What is a cancellable bio-cryptosystem?


Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control. In this paper, we treat nonlinear active noise control (NANC) with a linear secondary path (LSP) and with a nonlinear secondary path (NSP) in a unified structure by introducing a new virtual secondary path filter concept and using a general function expansion nonlinear filter. We discover that using the filtered-error structure results in greatly reducing the computational complexity of NANC. As a result, we extend the available filtered-error-based algorithms to solve NANC/LSP problems and, furthermore, develop our adjoint filtered-error-based algorithms for NANC/NSP. This family of algorithms is computationally efficient and possesses a simple structure. We also find that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP. Finally, we use the statistical properties of the virtual secondary path and the robustness of our proposed methods to further reduce the computational complexity and simplify the implementation structure of NANC/NSP when the NSP satisfies certain conditions. Computational complexity and simulation results are given to confirm the efficiency and effectiveness of all of our proposed methods

Question: What is the computational complexity of NANC/NSP?


An approach for offensive text detection and prevention in Social Networks. Social Network has become a place where people from every corner of the world has established a virtual civilization. In this virtual community, people used to share their views, express their feelings, photos, videos, blogs, etc. Social Networking Sites like Facebook, Twitters, etc. has given a platform to share innumerable contents with just a click of a button. However, there is no restriction applied by them for the uploaded content. These uploaded content may contains abusive words, explicit images which may be unsuitable for social platforms. As such there is no defined mechanism for restricting offensive contents from publishing on social sites. To solve this problem we have used our proposed approach. In our approach we are developing a social network prototype for implementing our approach for automatic filtering of offensive content in social network. Many popular social networking sites today don't have proper mechanism for restricting offensive contents. They use reporting methods in which user report if the content is abuse. This requires substantial human efforts and time. In this paper, we applied pattern matching algorithm for offensive keyword detection from social networking comments and prevent it from publishing on social platform. Apart from conventional method of reporting abusive contents by users our approach does not requires any human intervention and thus restrict offensive words by detecting and preventing it automatically.

Question: How to detect offensive words in social network?


Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform stateof-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.

Question: What is the one-class collaborative filtering?


On the Representation and Estimation of. This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.

Question: How to estimate the relationship between two frames?


Machine Reading. Over the last two decades or so, Natural Language Processing (NLP) has developed powerful methods for low-level syntactic and semantic text processing tasks such as parsing, semantic role labeling, and text categorization. Over the same period, the fields of machine learning and probabilistic reasoning have yielded important breakthroughs as well. It is now time to investigate how to leverage these advances to understand text.

Question: What is machine reading?


A Faster Scrabble Move Generation Algorithm. Appel and Jacobson1 presented a fast algorithm for generating every possible move in a given position in the game of Scrabble using a DAWG, a finite automaton derived from the trie of a large lexicon. This paper presents a faster algorithm that uses a GADDAG, a finite automaton that avoids the non-deterministic prefix generation of the DAWG algorithm by encoding a bidirectional path starting from each letter of each word in the lexicon. For a typical lexicon, the GADDAG is nearly five times larger than the DAWG, but generates moves more than twice as fast. This time/space trade-off is justified not only by the decreasing cost of computer memory, but also by the extensive use of move-generation in the analysis of board positions used by Gordon in the probabilistic search for the most appropriate play in a given position within realistic time constraints.

Question: What is a finite automaton?


Failure detection and consensus in the crash-recovery model. Summary. We study the problems of failure detection and consensus in asynchronous systems in which processes may crash and recover, and links may lose messages. We first propose new failure detectors that are particularly suitable to the crash-recovery model. We next determine under what conditions stable storage is necessary to solve consensus in this model. Using the new failure detectors, we give two consensus algorithms that match these conditions: one requires stable storage and the other does not. Both algorithms tolerate link failures and are particularly efficient in the runs that are most likely in practice – those with no failures or failure detector mistakes. In such runs, consensus is achieved within $3 \delta$ time and with 4 n messages, where $\delta$ is the maximum message delay and n is the number of processes in the system.

Question: What is the consensus algorithm that tolerates link failures?


HDRF: Stream-Based Partitioning for Power-Law Graphs. Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graph-computing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance. However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the real-world: their highly skewed power-law degree distributions. In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and real-world graphs and show that it outperforms all existing algorithms in partitioning quality.

Question: What is the name of the algorithm?


Using Phase Instead of Optical Flow for Action Recognition. Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phaseinformation extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.

Question: What is the difference between optical flow and phase?


Hand Gesture Recognition With Multiscale Weighted Histogram of Contour Direction Normalization for Wearable Applications. This paper proposes a static hand gesture recognition method with low computation and memory consumption for wearable applications. The hand contour is chosen as the hand gesture feature and support vector machine is used to classify the feature. A multiscale weighted histogram of contour direction-based direction normalization is proposed to ensure good recognition performance. In order to improve efficiency, the proposed histogram only counts the direction of the contour point to focus on the most significant hand feature in the first-person view of wearable devices. Based on the hand's anatomy, the proposed histogram is weighted by considering each contour point's position and direction jointly using the direction-angle map, to ensure robustness. Experimental results show that the proposed method can give a recognition accuracy of 97.1% with a frame rate of 30 fps on a PC.

Question: How to recognize hand gestures with wearable devices?


Optimal Mass Transport for Registration and Warping. Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass transport. This mass transport method has a number of important characteristics. First, it is parameter free. Moreover, it utilizes all of the grayscale data in both images, places the two images on equal footing and is symmetrical: the optimal mapping from image A to image B being the inverse of the optimal mapping from B to A. The method does not require that landmarks be specified, and the minimizer of the distance functional involved is unique; there are no other local minimizers. Finally, optimal transport naturally takes into account changes in density that result from changes in area or volume. Although the optimal transport method is certainly not appropriate for all registration and warping problems, this mass preservation property makes the Monge–Kantorovich approach quite useful for an interesting class of warping problems, as we show in this paper. Our method for finding the registration mapping is based on a partial differential equation approach to the minimization of the L 2 Kantorovich–Wasserstein or "Earth Mover's Distance" under a mass preservation constraint. We show how this approach leads to practical algorithms, and demonstrate our method with a number of examples, including those from the medical field. We also extend this method to take into account changes in intensity, and show that it is well suited for applications such as image morphing.

Question: Optimal mass transport for registration and warping


An Introduction to Systems Biology: Design Principles of Biological Circuits. Bernhard Ø. Palsson Cambridge U. Press, New York, 2006. $75.00 (322 pp.). ISBN 978-0-521-85903-5 Reviewed by Nigel Goldenfeld For some of us physicists, the recent explosion of interest in biology by our colleagues is something of a mystery— perhaps even a betrayal. Biology seems to offer none of the aesthetic elegance and beauty that is the hallmark of such cherished creations as the general theory of relativity and the standard model of strong, weak, and electromagnetic interactions. Neither does biology provide the breathtaking precision with which physical experiment and theory can agree in such phenomena as quantum electrodynamics or Josephson effects. And don't even think about summarizing all of biology on a T-shirt; biology textbooks, unlike those for physics, generally describe reactions and structures in monstrous detail, without an equation in sight. So why the allure of the subject among physicists? And what meaningful contributions can physical scientists make to the field? One answer is the emergence of a new science of biological circuits, or systems biology, which has become advanced enough, both scientifically and promotionally, that textbooks are warranted. Uri Alon's An Introduction to Systems Biology: Design Principles of Biological Circuits and Bernhard Ø. Palsson's Systems Biology: Properties of Reconstructed Networks are two recent offerings by leaders in this new field. These texts deserve serious attention from any quantitative scientist or physicist who hopes to learn about modern biology. Both books are well written; each has a different focus. Thus they could be used together to create an accessible course for beginners, with the instructor's choice of topics from each book. The books are a welcome departure from the typical biology textbook. First, they emphasize quantitative modeling rather than nomenclature; second, they concentrate on essentials of the model

Question: What is a systems biology textbook?


Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment. In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry- and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.

Question: How to classify point clouds into urban objects?


Approximation algorithms for data placement in arbitrary networks. We study approximation algorithms for placing replicated data in arbitrary networks. Consider a network of nodes with individual storage capacities and a metric communication cost function, in which each node periodically issues a request for an object drawn from a collection of uniform-length objects. We consider the problem of placing copies of the objects among the nodes such that the average access cost is minimized. Our main result is a polynomial-time constant-factor approximation algorithm for this placement problem. Our algorithm is based on a careful rounding of a linear programming relaxation of the problem. We also show that the data placement problem is MAXSNP-hard. We extend our approximation result to a generalization of the data placement problem that models additional costs such as the cost of realizing the placement. We also show that when object lengths are non-uniform, a constant-factor approximation is achievable if the capacity at each node in the approximate solution is allowed to exceed that in the optimal solution by the length of the largest object.

Question: What is the approximation ratio of the algorithm?


Comparison of Discovery Service Architectures for the Internet of Things. In the emerging Internet of Things rich data on real-world objects and events will be generated in vast amounts and stored in widely distributed databases. In truly global and dynamic application scenarios, intermediate brokers are needed to find these data, even if the exact location and form of storage are initially unknown to the requester. Discovery Services are aimed to fill this gap: they respond to requests for data on specific objects with a list of corresponding data providers. In this paper, we frame functional requirements for Discovery Services, and perform an overview and analysis of five established approaches for implementing Discovery Services that are taken from literature and industrial practice. In order to compare their characteristics, we develop a quality framework based on literature review and an ISO standard for software quality.

Question: What is the difference between discovery services and discovery brokers?


Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pressure. It is shown that abstract invariant features can be translated to detailed reconstructions when invariant features are allowed to modulate the strength of the lateral connection. Three dAE structures with modulated and additive lateral connections, and without lateral connections were compared in experiments using real-world images. The experiments verify that adding modulated lateral connections to the model 1) improves the accuracy of the probability model for inputs, as measured by denoising performance; 2) results in representations whose degree of invariance grows faster towards the higher layers; and 3) supports the formation of diverse invariant poolings.

Question: How does the denoising autoencoder learn invariant representations of natural images?


Facial recognition using histogram of gradients and support vector machines. Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.

Question: How to recognize a face?


A New Aggregate Local Mobility (ALM) Clustering Algorithm for VANETs. We present a beacon-based clustering algorithm aimed at prolonging the cluster lifetime in VANETs. We use a new aggregate local mobility criterion to decide upon cluster re-organisation. The scheme incorporates a contention method to avoid triggering frequent re-organisations when two clusterheads encounter each other for a short period of time. Simulation results show a significant improvement of cluster lifetime and reduced node state/role changes compared to previous popular clustering algorithms.

Question: What is a beacon-based clustering algorithm?


Positive psychology : Past , present , and ( possible ) future. What is positive psychology? Where has it come from? Where is it going? These are the questions we address in this article. In defining positive psychology, we distinguish between the meta-psychological level, where the aim of positive psychology is to redress the imbalance in psychology research and practice, and the pragmatic level, which is concerned with what positive psychologists do, in terms of their research, practice, and areas of interest. These distinctions in how we understand positive psychology are then used to shape conceptions of possible futures for positive psychology. In conclusion, we identify several pertinent issues for the consideration of positive psychology as it moves forward. These include the need to synthesize the positive and negative, build on its historical antecedents, integrate across levels of analysis, build constituency with powerful stakeholders, and be aware of the implications of description versus prescription.

Question: What is positive psychology?


Exploring Context with Deep Structured Models for Semantic Segmentation. We propose an approach for exploiting contextual information in semantic image segmentation, and particularly investigate the use of patch-patch context and patch-background context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets.

Question: What is the canadian military?


The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.. Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.

Question: What is the difference between physical and mental health?


A real-coded genetic algorithm for training recurrent neural networks. The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.

Question: What is a genetic algorithm?


PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems. Collaborative Filtering (CF), a well-known approach in producing recommender systems, has achieved wide use and excellent performance not only in research but also in industry. However, problems related to cold start and data sparsity have caused CF to attract an increasing amount of attention in efforts to solve these problems. Traditional approaches adopt side information to extract effective latent factors but still have some room for growth. Due to the strong characteristic of feature extraction in deep learning, many researchers have employed it with CF to extract effective representations and to enhance its performance in rating prediction. Based on this previous work, we propose a probabilistic model that combines a stacked denoising autoencoder and a convolutional neural network together with auxiliary side information (i.e, both from users and items) to extract users and items' latent factors, respectively. Extensive experiments for four datasets demonstrate that our proposed model outperforms other traditional approaches and deep learning models making it state of the art.

Question: What is the probabilistic model of hybrid deep collaborative filtering for recommender systems?


SEM-PPA: A semantical pattern and preference-aware service mining method for personalized point of interest recommendation. Point-of-Interest (POI) recommendation has received increasing attention in Location-based Social Networks (LBSNs). It involves user behavior analysis, movement pattern model and trajectory sequence prediction, in order to recommend personalized services to target user. Existing POI recommendation methods are confronted with three problems: (1) they only consider the location information of users' check-ins, which causes data sparsity; (2) they fail to consider the order of users' visited locations, which is valuable to reflect the interest or preference of users; (3) users cannot be recommended the suitable services when they move into the new place. To address the above issues, we propose a semantical pattern and preference-aware service mining method called SEM-PPA to make full use of the semantic information of locations for personalized POI recommendation. In SEM-PPA, we firstly propose a novel algorithm to classify the locations into different types for location identification; then we construct the user model for each user from four aspects, which are location trajectory, semantic trajectory, location popularity and user familiarity; in addition, a potential friends discovery algorithm based on movement pattern is proposed. Finally, we conduct extensive experiments to evaluate the recommendation accuracy and recommendation effectiveness on two real-life datasets from GeoLife and Beijing POI. Experimental results show that SEM-PPA can achieve better recommendation performance in particular for sparse data and recommendation accuracy in comparison with other methods.

Question: What is the difference between a semantical pattern and a preference-aware service mining method?


Wireless Hand Gesture Recognition Based on Continuous-Wave Doppler Radar Sensors. Short-range continuous-wave Doppler radar sensors have been mainly used for noncontact detection of various motions. In this paper, we investigate the feasibility to implement the function of a remote mouse, an input device of a computer, by recognizing human gestures based on a dual-channel Doppler radar sensor. Direct conversion architecture, symmetric subcarrier modulation, and bandpass sampling techniques are used to obtain a cost-effective solution. An arcsine algorithm and a motion imaging algorithm are proposed to linearly reconstruct the hand and finger motions in a 2-D plane from the demodulated Doppler phase shifts. Experimental results verified the effectiveness of the proposed architecture and algorithms. Different from the frequency-domain "micro-Doppler" approach, the proposed remote gesture recognition based on linear motion reconstruction is able to recognize definitive signatures for the corresponding motions, exhibiting promising potential in practical applications of human-computer interaction.

Question: How to recognize gestures based on a dual-channel Doppler radar sensor?


NewsVallum: Semantics-Aware Text and Image Processing for Fake News Detection system. As a consequence of the social revolution we faced on the Web, news and information we daily enjoy may come from different and diverse sources which are not necessarily the traditional ones such as newspapers, either in their paper or online version, television, radio, etc. Everyone on the Web is allowed to produce and share news which can soon become viral if they follow the new media channels represented by social networks. This freedom in producing and sharing news comes with a counter-effect: the proliferation of fake news. Unfortunately, they can be very effective and may influence people and, more generally, the public opinion. We propose a combined approach of natural language and image processing that takes into account the semantics encoded within both text and images coming with news together with contextual information that may help in the classification of a news as fake or not.

Question: What is a fake news?


Source Code Analysis: A Road Map. The automated and semi-automated analysis of source code has remained a topic of intense research for more than thirty years. During this period, algorithms and techniques for source-code analysis have changed, sometimes dramatically. The abilities of the tools that implement them have also expanded to meet new and diverse challenges. This paper surveys current work on source-code analysis. It also provides a road map for future work over the next five-year period and speculates on the development of source-code analysis applications, techniques, and challenges over the next 10, 20, and 50 years.

Question: What is source code analysis?