Dilemmas in a General Theory of Planning *. The search for scientific bases for confronting problems of social policy is bound to fail, because of the nature of these problems. They are "wicked" problems, whereas science has developed to deal with "tame" problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about "optinaal solutions" to social probIems unless severe qualifications are imposed first. Even worse, there are no "solutions" in the sense of definitive and objective answers. George Bernard Shaw diagnosed the case several years ago; in more recent times popular protest may have already become a social movement . Shaw averred that "every profession is a conspiracy against the laity." The contemporary publics are responding as though they have made the same discovery. Few o f the modern professionals seem to be immune f rom the popular a t t a c k whether they be social workers, educators, housers, public health officials, policemen, city planners, highway engineers or physicians. Our restive clients have been telling us that they don ' t like the educational programs that schoolmen have been offering, the redevelopment projects urban renewal agencies have been proposing, the lawenforcement styles o f the police, the administrative behavior o f the welfare agencies, the locations o f the highways, and so on. In the courts, the streets, and the political campaigns, we've been hearing ever-louder public protests against the professions' diagnoses o f the clients' problems, against professionally designed governmental programs, against professionally certified standards for the public services. It does seem odd that this attack should be coming just when professionals in * This is a modification of a paper presented t

Question: What is the difference between a "tame" and a "wicked" problem?


Analyzing and Comparing the Protection Quality of Security Enhanced Operating Systems. Host compromise is a serious computer security problem today. To better protect hosts, several Mandatory Access Control systems, such as Security Enhanced Linux (SELinux) and AppArmor, have been introduced. In this paper we propose an approach to analyze and compare the quality of protection offered by these different MAC systems. We introduce the notion of vulnerability surfaces under attack scenarios as the measurement of protection quality, and implement a tool called VulSAN for computing such vulnerability surfaces. In VulSAN, we encode security policies, system states, and system rules using logic programs. Given an attack scenario, VulSAN computes a host attack graph and the vulnerability surface. We apply our approach to compare SELinux and AppArmor policies in several Linux distributions and discuss the results. Our tool can also be used by Linux system administrators as a system hardening tool. Because of its ability to analyze SELinux as well as AppArmor policies, it can be used for most enterprise Linux distributions and home user distributions.

Question: How to compare the quality of protection offered by different security enhanced operating systems?


Clinical Information Extraction via Convolutional Neural Network. We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) con-volutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.

Question: How does the convolutional neural network work?


Perceived parental social support and academic achievement: an attachment theory perspective.. The study tested the extent to which parental social support predicted college grade point average among undergraduate students. A sample of 418 undergraduates completed the Social Provisions Scale--Parent Form (C.E. Cutrona, 1989) and measures of family conflict and achievement orientation. American College Testing Assessment Program college entrance exam scores (ACT; American College Testing Program, 1986) and grade point average were obtained from the university registrar. Parental social support, especially reassurance of worth, predicted college grade point average when controlling for academic aptitude (ACT scores), family achievement orientation, and family conflict. Support from parents, but not from friends or romantic partners, significantly predicted grade point average. Results are interpreted in the context of adult attachment theory.

Question: What is the difference between parental social support and parental attachment?


Analysis and Design of an Input-Series Two-Transistor Forward Converter For High-Input Voltage Multiple-Output Applications. In this paper, an input-series two-transistor forward converter is proposed and investigated, which is aimed at high-input voltage multiple-output applications. In this converter, all of the switches are operating synchronously, and the input voltage sharing (IVS) of each series-module is achieved automatically by the coupling of primary windings of the common forward integrated transformer. The active IVS processes are analyzed based on the model of the forward integrated transformer. Through the influence analysis when the mismatches in various series-modules are considered, design principles of the key parameters in each series-module are discussed to suppress the input voltage difference. Finally, a 96-W laboratory-made prototype composed of two forward series-modules is built, and the feasibility of the proposed method and the theoretical analysis are verified by the experimental results.

Question: How to design a converter for high-input voltage multiple-output applications?


Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar. We describe a chart realization algorithm for Combinatory Categorial Grammar (CCG), and show how it can be used to efficiently realize a wide range of coordination phenomena, including argument cluster coordination and gapping. The algorithm incorporates three novel methods for improving the efficiency of chart realization: (i) using rules to chunk the input logical form into sub-problems to be solved independently prior to further combination; (ii) pruning edges from the chart based on the n-gram score of the edge's string, in comparison to other edges with equivalent categories; and (iii) formulating the search as a best-first anytime algorithm, using n-gram scores to sort the edges on the agenda. The algorithm has been implemented as an extension to the OpenCCG open source CCG parser, and initial performance tests indicate that the realizer is fast enough for practical use in natural language dialogue systems.

Question: What is the name of the algorithm?


Color texture classification by integrative Co-occurrence matrices. Integrative Co-occurrence matrices are introduced as novel features for color texture classi"cation. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information pro"t of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classi"cation experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classi"cation results up to 20% and 32% for the "rst and second baseline, respectively. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Question: What is the difference between the color and the intensity texture?


The Mobile Sensing Platform: An Embedded Activity Recognition System. Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

Question: What is the difference between a smart environment and a smart home?


Asymmetrical impact of trustworthiness attributes on trust, perceived value and purchase intention: a conceptual framework for cross-cultural study on consumer perception of online auction. Lack of trust has been frequently cited to be one of the key factors that discourage customers from participating in e-commerce, while cultural differences affect the formation of trust. As such, this cross-cultural study aims at establishing a conceptual framework to investigate the primary antecedents of online trust and the mechanism that converts consumer trust into perceived value and purchase intention in online auction. Two elements of our model bear mention here. First, the authors divide the consumer trust into two facets, seller and intermediary trust, and intend to assess their reciprocal relationships. Second, the authors intend to examine the asymmetrical impact of the negative and positive trustworthiness attributes on trust, and the interrelationship among trust, perceived value and purchase intention across cultural groups.

Question: What is the asymmetrical impact of trustworthiness attributes on trust?


Variance Reduction Methods II 1 Importance Sampling. For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of n, however, would almost inevitably result in an estimate, θ̂n = 0, and an approximate confidence interval [L,U ] = [0, 0]! So the naive approach does not work. We could try to use the variance reduction techniques we have seen in the course so far, but they would provide little, if any, help.

Question: How to solve this problem?


Data mining models for student careers. This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade. 2015 Elsevier Ltd. All rights reserved.

Question: What is the ideal career of a student?


Intelligent criminal identification system. In many countries the amount of crime incidents that is reported per day is increasing dramatically. Concerning about Sri Lanka, The department of Police is the major organization of preventing crimes. In general, Sri Lankan police stations utilize paper-based information storing systems and they don't employ computer based applications up to a great extent. Due to this utilization of paper-based systems police officers have to spend a lot of time as well as man power to analyze existing crime information and to identify suspects for crime incidents. So the requirement of an efficient way for crime investigation has arisen. Data mining practices is one aspect of crime investigation, for which numerous technique are available. This paper highlights the use of data mining techniques, clustering and classification for effective investigation of crimes. Further the paper aims to identify suspects by analyzing existing evidences in situations where any witness or forensic clues are not present.

Question: What is the purpose of data mining?


Breaking the News: First Impressions Matter on Online News. A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.

Question: What is the sentiment polarity of the headline?


A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring. The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO₂, CO, SO₂, NO₂, O₃, Cl₂, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.

Question: What is the impact of air quality on health and on life comfort?


Earpod: eyes-free menu selection using touch input and reactive audio feedback. We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.

Question: How does earPod compare to other menu techniques?


Bitcoin's Academic Pedigree. The concept of cryptocurrencies is built from forgotten ideas in research literature.

Question: What is the academic pedigree of bitcoin?


Wideband Millimeter-Wave Surface Micromachined Tapered Slot Antenna. A millimeter-wave surface micromachined tapered slot antenna (TSA) fed by an air-filled rectangular coaxial line is proposed. Detailed parametric study is conducted to determine the effects of the TSA's key structural features on its VSWR and gain. Selected exponential taper with determined growth rate and corrugation length resulted in an antenna with VSWR <; 2.5, gain > 2.75 dBi, and stable patterns over a 43-140-GHz range. The TSA is fabricated, and good correlation between modeling and W-band measurements confirms its wideband performance.

Question: What is a millimeter-wave surface micromachined tapered slot antenna?


Efficient 3D LIDAR based loop closing using deep neural network. Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.

Question: What is the KITTI dataset?


Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample. A mailed questionnaire was used to obtain comparative risk judgments for 32 different hazards from a random sample of 296 individuals living in central New Jersey. The results demonstrate that an optimistic bias about susceptibility to harm-a tendency to claim that one is less at risk than one's peers—is not limited to any particular age, sex, educational, or occupational group. It was found that an optimistic bias is often introduced when people extrapolate from their past experience to estimate their future vulnerability. Thus, the hazards most likely to elicit unrealistic optimism are those associated with the belief (often incorrect) that if the problem has not yet appeared, it is unlikely to occur in the future. Optimistic biases also increase with the perceived preventability of a hazard and decrease with perceived frequency and personal experience. Other data presented illustrate the inconsistent relationships between personal risk judgments and objective risk factors.

Question: What is the difference between a community-wide sample and a random sample?


How Learning Works: Seven Research-Based Principles for Smart Teaching. In this volume, the authors introduce seven general principles of learning, distilled from the research literature as well as from twenty-seven years of experience working one-on-one with college faculty. They have drawn on research from a breadth of perspectives (cognitive, developmental, and social psychology; educational research; anthropology; demographics; and organizational behavior) to identify a set of key principles underlying learningfrom how effective organization enhances retrieval and use of information to what impacts motivation. These principles provide instructors with an understanding of student learning that can help them see why certain teaching approaches are or are not supporting student learning, generate or refine teaching approaches and strategies that more effectively foster student learning in specific contexts, and transfer and apply these principles to new courses.

Question: What is the difference between a research-based principle and a research-based principle?


The Acquisition of Reading Comprehension Skill. How do people acquire skill at comprehending what they read? That is the simple question to which we shall try to make a tentative answer. To begin, we have to acknowledge some complexities about the concept of reading comprehension and what it means to develop it.

Question: How do people acquire skill at comprehending what they read?


1 ACO Algorithms for the Traveling Salesman Problemy. Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect communication of a colony of agents, called (artificial) ants, based on pheromone trails (pheromones are also used by real ants for communication). The (artificial) pheromone trails are a kind of distributed numeric information which is modified by the ants to reflect their experience while solving a particular problem. Recently, the Ant Colony Optimization (ACO) metaheuristic has been proposed which provides a unifying framework for most applications of ant algorithms [15, 16] to combinatorial optimization problems. In particular, all the ant algorithms applied to the TSP fit perfectly into the ACO meta-heuristic and, therefore, we will call these algorithms also ACO algorithms. The first ACO algorithm, called Ant System (AS) [18, 14, 19], has been applied to the Traveling Salesman Problem (TSP). Starting from Ant System, several improvements of the basic algorithm have been proposed [21, 22, 17, 51, 53, 7]. Typically, these improved algorithms have been tested again on the TSP. All these improved versions of AS have in common a stronger exploita-

Question: What is the difference between the ACO algorithms and the ant algorithms?


Extreme-angle broadband metamaterial lens.. For centuries, the conventional approach to lens design has been to grind the surfaces of a uniform material in such a manner as to sculpt the paths that rays of light follow as they transit through the interfaces. Refractive lenses formed by this procedure of bending the surfaces can be of extremely high quality, but are nevertheless limited by geometrical and wave aberrations that are inherent to the manner in which light refracts at the interface between two materials. Conceptually, a more natural--but usually less convenient--approach to lens design would be to vary the refractive index throughout an entire volume of space. In this manner, far greater control can be achieved over the ray trajectories. Here, we demonstrate how powerful emerging techniques in the field of transformation optics can be used to harness the flexibility of gradient index materials for imaging applications. In particular we design and experimentally demonstrate a lens that is broadband (more than a full decade bandwidth), has a field-of-view approaching 180 degrees and zero f-number. Measurements on a metamaterial implementation of the lens illustrate the practicality of transformation optics to achieve a new class of optical devices.

Question: What is a metamaterial?


2D-to-3D image conversion by learning depth from examples. Among 2D-to-3D image conversion methods, those involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. In this paper, we adopt the radically different approach of "learning" the 3D scene structure. We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the k corresponding depth fields and align the fused depth with the 2D query. Unlike in our original work, we validate the simplified algorithm quantitatively on a Kinect-captured image+depth dataset against the Make3D algorithm. While far from perfect, the presented results demonstrate that online repositories of 3D content can be used for effective 2D-to-3D image conversion.

Question: How to convert a 2D image to a 3D image?


Enhancing an Interactive Recommendation System with Review-based Information Filtering. Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.

Question: How to extract meaningful facets from social media?


Wavelet Neural Networks and their application in the study of dynamical systems. The main aim of this dissertation is to study the topic of wavelet neural networks and see how they are useful for dynamical systems applications such as predicting chaotic time series and nonlinear noise reduction. To do this, the theory of wavelets has been studied in the first chapter, with the emphasis being on discrete wavelets. The theory of neural networks and its current applications in the modelling of dynamical systems has been shown in the second chapter. This provides sufficient background theory to be able to model and study wavelet neural networks. In the final chapter a wavelet neural network is implemented and shown to accurately estimate the dynamics of a chaotic system, enabling prediction and enhancing methods already available in nonlinear noise reduction.

Question: What is a wavelet neural network?


Grade Prediction with Temporal Course-wise Influence. There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent "knowledge" space. The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.

Question: What is the next term grade prediction?


The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. SEMAINE has created a large audiovisual database as a part of an iterative approach to building Sensitive Artificial Listener (SAL) agents that can engage a person in a sustained, emotionally colored conversation. Data used to build the agents came from interactions between users and an "operator" simulating a SAL agent, in different configurations: Solid SAL (designed so that operators displayed an appropriate nonverbal behavior) and Semi-automatic SAL (designed so that users' experience approximated interacting with a machine). We then recorded user interactions with the developed system, Automatic SAL, comparing the most communicatively competent version to versions with reduced nonverbal skills. High quality recording was provided by five high-resolution, high-framerate cameras, and four microphones, recorded synchronously. Recordings total 150 participants, for a total of 959 conversations with individual SAL characters, lasting approximately 5 minutes each. Solid SAL recordings are transcribed and extensively annotated: 6-8 raters per clip traced five affective dimensions and 27 associated categories. Other scenarios are labeled on the same pattern, but less fully. Additional information includes FACS annotation on selected extracts, identification of laughs, nods, and shakes, and measures of user engagement with the automatic system. The material is available through a web-accessible database.

Question: What is the SEMAINE database?


Ultrasound-Assisted Evaluation ofthe Airway in Clinical AnesthesiaPractice: Past, Present and Future. Introduction: The incidence of difficulties encountered in perioperative airway management has been reported to range from 1% to 4%. In patients with head and neck cancers, the incidence can be dramatically higher. Because of high quality of imaging, non-invasiveness and relatively low cost, ultrasonography has been utilized as a valuable adjunct to the clinical assessment of the airway. A review of the literature was conducted with the objective of summarizing the available evidence concerning the use of ultrasound (US) for assessment of the airway, with special emphasis on head and neck cancers. Methods and Materials: A systematic search of the literature in the MEDLINE database was performed. A total of 42 manuscripts from 329 searched articles were included in this review. Results: Ultrasonography was found to give high-resolution images of the anatomic structures of the upper airway comparable to computed tomography and magnetic resonance imaging. Several ultrasonographic parameters (soft tissue thickness at level of hyoid bone, epiglottis and vocal cords, visibility of hyoid bone in sublingual ultrasound, hyomental distance in head-extended position and hyomental distance ratio) were found to be independent predictors of difficult laryngoscopy in obese and non-obese patients. In conjunction with elastosonography, it also provided valuable information regarding tumors, infiltration, and edema as well as fibrosis of the head and neck. Conclusion: Ultrasound-assisted evaluation of the difficult airway offers many important advantages. The ready availability of US machines in anesthesiology departments, familiarity of anesthesia providers with USguided procedures and the portability of US machines allow real-time, point-of-care assessment. It will undoubtedly become more popular and will greatly contribute to improve perioperative patient safety.

Question: What is the incidence of difficult laryngoscopy in head and neck cancers?


User-centered security: stepping up to the grand challenge. User-centered security has been identified as a grand challenge in information security and assurance. It is on the brink of becoming an established subdomain of both security and human/computer interface (HCI) research, and an influence on the product development lifecycle. Both security and HCI rely on the reality of interactions with users to prove the utility and validity of their work. As practitioners and researchers in those areas, we still face major issues when applying even the most foundational tools used in either of these fields across both of them. This essay discusses the systemic roadblocks at the social, technical, and pragmatic levels that user-centered security must overcome to make substantial breakthroughs. Expert evaluation and user testing are producing effective usable security today. Principles such as safe staging, enumerating usability failure risks, integrated security, transparent security and reliance on trustworthy authorities can also form the basis of improved systems

Question: What is user-centered security?


A Survey of Text Summarization Extractive Techniques. Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.

Question: What is a survey of text summarization extractive techniques?


Classification and Regression by randomForest. Recently there has been a lot of interest in "ensemble learning" — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.

Question: What is the difference between classification and regression?


The art of appeal in electronic commerce: Understanding the impact of product and website quality on online purchases. Purpose – This study advances product appeal and website appeal as focal psychological mechanisms that can be invoked by business-to-consumer (B2C) e-commerce sites to mitigate problems of information asymmetry via signaling to bolster consumers' purchase intention under the influence of trust. Design/methodology/approach – Survey approach was employed to validate our research model. Findings – Website appeal partially mediates the positive effect of product appeal on consumers' purchase intention. Trust in e-commerce sites not only increases purchase intention directly, but it also reinforces the positive relationship between website appeal and purchase intention while attenuating the positive relationship between product appeal and purchase intention. Service content quality, search delivery quality, and enjoyment are confirmed as positive antecedents of website appeal whereas diagnosticity and justifiability are established as positive antecedents of product appeal. Research implications – This study not only delineates product and website appeal as complementary drivers of consumer purchase on e-commerce sites, but it also derives five signals that aid in bolstering both product and website appeal. Trust is revealed to exert a moderating influence on the impact of product and website appeal on purchase intention. Practical implications – Practitioners should prioritize their resource allocation to enhance qualities most pertinent to product and website appeal. E-commerce sites should offer product-oriented functionalities to facilitate product diagnosticity and reassure consumers of their purchase decisions. Originality/value – This study distinguishes between product and website appeal as well as between their respective antecedents. It also uncovers how trust can alter the effects of both website and product appeal on consumers' purchase inten

Question: What is the difference between product and website appeal?


Asynchronous Large-Scale Graph Processing Made Easy. Scaling large iterative graph processing applications through parallel computing is a very important problem. Several graph processing frameworks have been proposed that insulate developers from low-level details of parallel programming. Most of these frameworks are based on the bulk synchronous parallel (BSP) model in order to simplify application development. However, in the BSP model, vertices are processed in fixed rounds, which often leads to slow convergence. Asynchronous executions can significantly accelerate convergence by intelligently ordering vertex updates and incorporating the most recent updates. Unfortunately, asynchronous models do not provide the programming simplicity and scalability advantages of the BSP model. In this paper, we combine the easy programmability of the BSP model with the high performance of asynchronous execution. We have designed GRACE, a new graph programming platform that separates application logic from execution policies. GRACE provides a synchronous iterative graph programming model for users to easily implement, test, and debug their applications. It also contains a carefully designed and implemented parallel execution engine for both synchronous and user-specified built-in asynchronous execution policies. Our experiments show that asynchronous execution in GRACE can yield convergence rates comparable to fully asynchronous executions, while still achieving the near-linear scalability of a synchronous BSP system.

Question: What is the difference between a synchronous and asynchronous graph processing framework?


PyThinSearch: A Simple Web Search Engine. We describe a simple, functioning web search engine for indexing and searching online documents using python programming language. Python was chosen because it is an elegant language with simple syntax, is easy to learn and debug, and supports main operating systems almost evenly. The remarkable characteristics of this program are an adjustable search function that allows users to rank documents with several combinations of score functions and the focus on anchor text analysis as we provide four additional schemes to calculate scores based on anchor text. We also provide an additional ranking algorithm based on link addition process in network motivated by PageRank and HITS as an experimental tool. This algorithm is the original contribution of this paper.

Question: What is the difference between pyThinSearch and other web search engines?


Very Small Ultra-Wide-Band MMIC Magic T and Applications to Combiners and Dividers. An FET-sized 1-18 GHz monolithic active magic T (1 W hybrid) is proposed. It unifies two different dividers, electrically isolated from each other, in a novel GaAs FET electrode configuration, viz. the LUFET concept. Its characteristics and experiment results are presented. Applications of the magic T to miniature wide-band RF signal processing. . .

Question: What is a magic T?


Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals. In this study, we investigated blood-flow-related magnetic-resonance (MR) signal changes and the time course underlying short-term motor learning of the dominant right hand in ten piano players (PPs) and 23 non-musicians (NMs), using a complex finger-tapping task. The activation patterns were analyzed for selected regions of interest (ROIs) within the two examined groups and were related to the subjects' performance. A functional learning profile, based on the regional blood-oxygenation-level-dependent (BOLD) signal changes, was assessed in both groups. All subjects achieved significant increases in tapping frequency during the training session of 35 min in the scanner. PPs, however, performed significantly better than NMs and showed increasing activation in the contralateral primary motor cortex throughout motor learning in the scanner. At the same time, involvement of secondary motor areas, such as bilateral supplementary motor area, premotor, and cerebellar areas, diminished relative to the NMs throughout the training session. Extended activation of primary and secondary motor areas in the initial training stage (7–14 min) and rapid attenuation were the main functional patterns underlying short-term learning in the NM group; attenuation was particularly marked in the primary motor cortices as compared with the PPs. When tapping of the rehearsed sequence was performed with the left hand, transfer effects of motor learning were evident in both groups. Involvement of all relevant motor components was smaller than after initial training with the right hand. Ipsilateral premotor and primary motor contributions, however, showed slight increases of activation, indicating that dominant cortices influence complex sequence learning of the non-dominant hand. In summary, the involvement of primary and secondary motor cortices in motor learning is dependent on

Question: How does the brain learn to play the piano?


The Finite Element Method with Anisotropic Mesh Grading for Elliptic Problems in Domains with Corners and Edges. This paper is concerned with a specific finite element strategy for solving elliptic boundary value problems in domains with corners and edges. First, the anisotropic singular behaviour of the solution is described. Then the finite element method with anisotropic, graded meshes and piecewise linear shape functions is investigated for such problems; the schemes exhibit optimal convergence rates with decreasing mesh size. For the proof, new local interpolation error estimates for functions from anisotropically weighted spaces are derived. Finally, a numerical experiment is described, that shows a good agreement of the calculated approximation orders with the theoretically predicted ones. ( 1998 B. G. Teubner Stuttgart—John Wiley & Sons Ltd.

Question: What is the finite element method?


1 Sex , Syntax , and Semantics. Many languages have a grammatical gender system whereby all nouns are assigned a gender (most commonly feminine, masculine, or neuter). Two studies examined whether (1) the assignment of genders to nouns is truly arbitrary (as has been claimed), and (2) whether the grammatical genders assigned to nouns have semantic consequences. In the first study, English speakers' intuitions about the genders of animals (but not artifacts) were found to correlate with the grammatical genders assigned to the names of these objects in Spanish and German. These findings suggest that the assignment of genders to nouns is not entirely arbitrary but may to some extent reflect the perceived masculine or feminine properties of the nouns' referents. Results of the second study suggested that people's ideas about the genders of objects are strongly influenced by the grammatical genders assigned to these objects in their native language. Spanish and German speakers' memory for object--name pairs (e.g., apple--Patricia) was better for pairs where the gender of the proper name was congruent with the grammatical gender of the object name (in their native language), than when the two genders were incongruent. This was true even though both groups performed the task in English. These results suggest that grammatical gender may not be as arbitrary or as purely grammatical as was previously

Question: What is the grammatical gender of the word apple?


A real-time face tracker. We present a real-time face tracker in this paper The system has achieved a rate of 30% frameshecond using an HP-9000 workstation with a framegrabber and a Canon VC-CI camera. It can track a person 'sface while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, we present a stochastic model to characterize skin-color distributions of human faces. The information provided by the model is sufJicient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model e's used to estimate image motion and to predict search window. Third, a camera model is used toprediet and to compensate for camera motion. The system can be applied to tele-conferencing and many HCI applications including lip-reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.

Question: How does the system work?


Design and Experimental Results of a 300-kHz Synthetic Aperture Sonar Optimized for Shallow-Water Operations. The design of a shallow-water synthetic aperture sonar (SAS) requires an understanding of key system and environmental issues. The main factors that limit SAS performance are as follows: micronavigation accuracy, where micronavigation is defined as the problem of estimating the acoustic path lengths to allow the focusing of the aperture; multipath effects; and target view angle changes. All of them can degrade shadow classification performance. Micronavigation accuracy is successfully addressed by the gyrostabilized displaced phase center antenna technique, which combines data-driven motion estimates with external attitude sensors. Multipath effects in shallow water are effectively countered by narrow vertical beams. Shadow blur induced by view angle changes is mitigated by increasing the center frequency, to reduce the SAS integration length while still maintaining the desired resolution and by designing the system with a minimum grazing angle of about 6deg to reduce shadow length. The combination of these factors led to the choice of a 300-kHz center frequency and of a multipath mitigation scheme that uses multiple vertical beams. Experimental results obtained with a sonar incorporating these features have produced SAS images with 1.6 cm times 5 cm resolution in range times cross-range and shadow contrast in excess of 5 dB, at ranges of up to 170 m in 20 m of water.

Question: What is the resolution of a sonar?


A low-Cost, Open-Source, BCI- VR Game Control Development Environment Prototype for Game Based Neurorehabilitation. In this paper we present a low-cost and open-source brain-computer interface (BCI) virtual-reality (VR) Game Control Development Environment prototype, which we demonstrate using real-time signal processing of Electroencephalography (EEG) event-related desynchronization and synchronization changes (ERD/ERS) within the Precentral Gyrus (Motor Cortex), allowing a user to control a 3D object within a Virtual Reality Environment. This BCI-VR system prototype was functionally tested on multiple participants and demonstrated live before an audience during the 2017 'Hack the Brain' at the Dublin Science Gallery. The availability of such an open-source, effective, BCI-VR Game Control Development Environment, at a level acceptable for industry experimentation, has the potential to open up this field to a much wider range of researchers and games developers and to assist the investigation of gaming experiences which both incorporate the specific control features available through BCI as a core element of the game play and the potential for its use in neurorehabilitation.

Question: What is a brain-computer interface?


REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING. This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of "noticing"; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners' language is a product of their history of usage in communicative interaction.

Question: What is the difference between explicit and implicit memory?


Regularized Off-Policy TD-Learning. We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity. The algorithmic framework underlying ROTD integrates two key ideas: off-policy convergent gradient TD methods, such as TDC, and a convex-concave saddle-point formulation of non-smooth convex optimization, which enables first-order solvers and feature selection using online convex regularization. A detailed theoretical and experimental analysis of RO-TD is presented. A variety of experiments are presented to illustrate the off-policy convergence, sparse feature selection capability and low computational cost of the RO-TD algorithm.

Question: What is the off-policy convergence of TD-learning?


ROUTERLESS NETWORKS-ON-CHIP. Traditional bus-based interconnects are simple and easy to implement, but the scalability is greatly limited. While router-based networks-on-chip (NoCs) offer superior scalability, they also incur significant power and area overhead due to complex router structures. In this paper, we explore a new class of on-chip networks, referred to as Routerless NoCs, where routers are completely eliminated. We propose a novel design that utilizes on-chip wiring resources smartly to achieve comparable hop count and scalability as router-based NoCs. Several effective techniques are also proposed that significantly reduce the resource requirement to avoid new network abnormalities in routerless NoC designs. Evaluation results show that, compared with a conventional mesh, the proposed routerless NoC achieves 9.5X reduction in power, 7.2X reduction in area, 2.5X reduction in zero-load packet latency, and 1.7X increase in throughput. Compared with a state-of-the-art low-cost NoC design, the proposed approach achieves 7.7X reduction in power, 3.3X reduction in area, 1.3X reduction in zero-load packet latency, and 1.6X increase

Question: What is the scalability of a routerless network?


A generalized multilevel inverter topology with self voltage balancing. Multilevel power converters that provide more than two levels of voltage to achieve smoother and less distorted ac-to-dc, dc-to-ac, and dc-to-dc power conversion, have attracted many contributors. This paper presents a generalized multilevel inverter (converter) topology with self voltage balancing. The existing multilevel inverters such as diode-clamped and capacitor-clamped multilevel inverters can be derived from the generalized inverter topology. Moreover, the generalized multilevel inverter topology provides a true multilevel structure that can balance each dc voltage level automatically without any assistance from other circuits, thus, in principle, providing a complete and true multilevel topology that embraces the existing multilevel inverters. From this generalized multilevel inverter topology, several new multilevel inverter structures can be derived. Some application examples of the generalized multilevel converter will be given.

Question: What is a generalized multilevel inverter topology?


FULLY AUTOMATIC STEREO-TO-MULTIVIEW CONVERSION IN AUTOSTEREOSCOPIC DISPLAYS. Watching 3D without glasses will be the future of 3D home entertainment. However, current stereoscopic 3D content is not suited to drive autostereoscopic displays and a 3D format conversion is required. In this paper a system is presented which is capable of high quality stereo-tomultiview conversion in real-time. This allows playback of 3D Blu-ray or any other stereoscopic 3D video content without prior adaptation on most existing autostereoscopic displays in real-time. The algorithms described include image rectification, fast and reliable disparity estimation, adaptive post-processing and up-sampling of disparity maps, rendering of virtual views and interweaving these views into a format suitable for autostereoscopic displays.

Question: How to convert 3D video to autostereoscopic video?


Dynamic Movement Primitives – A Framework for Motor Control in Humans and Humanoid Robotics. Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dynamically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control in robotics and biology.

Question: What is a dynamic movement primitive?


Evolution of Trajectories: A Novel Representation for Deep Action Recognition. Achieving high classification accuracy remains a challenge for human action recognition approaches that are based on convolutional neural networks (CNN). CNN-based action recognition methods on resource-constrained edge systems are currently unable to train on whole videos due to infeasible computational and memory requirements. On the other hand, approaches that utilize video-level supervision with sparse-sampling designs run the risk of learning local features shared with multiple similar classes. Additionally, features captured by motion estimation algorithms for temporal stream CNN's are already reduced in dimension, increasing the possibility of label mismatch in methods that rely on short-term features. To address the aforementioned points, we design a novel temporal representation to capture a 1) long-term interval of motion and 2) integrate the trajectory of motion captured therein. We compare our approach with current motion representations and demonstrate its efficacy for examples containing local features with high inter-class similarity. We implement our representation as part of two and three-stream CNN's and conduct experiments on one of the popular and most difficult action recognition datasets: HMDB51. Our results show that, for methods employing sparse-sampling designs, our approach surpasses the current state-of-the-art approaches achieving 71.76% on HMDB51.

Question: What is the current state-of-the-art for action recognition?


Irregularities in the Distribution of Primes and Twin Primes By. The maxima and minima of sL(x)) — n(x), iR(x)) — n(x), and sL2(x)) — n2(x) in various intervals up to x = 8 x 10 are tabulated. Here n(x) and n2(x) are respectively the number of primes and twin primes not exceeding x, L(x) is the logarithmic integral, R(x) is Riemann's approximation to ir(x), and L2(x) is the Hardy-Littlewood approximation to ti"2(;c). The computation of the sum of inverses of twin primes less than 8 x 10 gives a probable value 1.9021604 ± 5 x 10~7 for Brun's constant. 1. Approximations to nix). Let P= {2, 3, 5, • • • } be the set of primes, and let 7r(x) be the number of primes not exceeding x. Two well-known approximations to 7t(x) for x > 1 are the logarithmic integral: rx dt (1.1) L{x) = \v ; J o log t O-2) =T + log(logx)+Í: ^> k=\ k-k and Riemann's approximation: (1.3) R{x)=Z^L{xi'k) k=i k (1.4) =1 + ¿Jtó_ fz. k\kl{k + 1) Note that (1.1) differs by 1(2) = 1.04516378... from the frequently used approximation f2 dt/log t. We are interested in the errors O-5) r.{x) = (L(x)> n{x) and (1 6) r2(x) = (Rix)) n{x), where <y> denotes the integer closest to y {i.e., the integer part of (y + W)). k Received July 5, 1974. AMS (MOS) subject classifications (1970). Primary 10-04, 10H25, 10H15; Secondary 10A25, 10A40, 10H05, 65A05, 65B05.

Question: What is the error of the approximation to the number of primes?


E-commerce web page classification based on automatic content extraction. Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each product are classified into its category by human. It is very hard to define their categories in automatic manner when the data is very large. In this paper, we propose the method for classifying E-commerce web pages based on their product types. Firstly, we apply the proposed automatic content extraction to extract the contents of E-commerce web pages. Then, we apply the automatic key word extraction to select words from these extracted contents for generating the feature vectors that represent the E-commerce web pages. Finally, we apply the machine learning technique for classifying the E-commerce web pages based on their product types. The experimental results signify that our proposed method can classify the E-commerce web pages in automatic fashion.

Question: What is the main objective of C2C websites?


A NOVEL OF RECONFIGURABLE PLANAR ANTENNA ARRAY ( RPAA ) WITH BEAM STEERING CONTROL. A new antenna structure is formed by combining the concept of reconfigurable planar antenna array (RPAA) with the parasitic elements to produce beam steering patterns. The antenna has been integrated with the PIN diode switches that enable the beam to be steered in the desired direction. This has been done by changing the switch state to either on or off mode. In this work, a number of parasitic elements have been applied to the antenna, namely reflectors and directors. They are placed in between the driven elements, which is aimed to improve the beam steering angle. With such configuration, the main beam radiated by the array can be tilted due to the effect of mutual coupling between the driven elements and parasitic elements (reflectors and director). The unique property of this antenna design is that instead of fabricating all together in the same plane, the antenna's feeding network is separated from the antenna radiating elements (the patches) by an air gap distance. This allows reducing the spurious effects from the feeding line. The optimization results for the resonant Corresponding author: M. T. Ali (mizi732002@yahoo.com).

Question: What is the novel of reconfigurable planar antenna array?


An Efficient and Secure Automotive Wireless Software Update Framework. Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.

Question: What is the name of the framework?


A survey on comparison of electric motor types and drives used for electric vehicles. In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.

Question: What is the most appropriate motor for electric vehicles?


Selective Pooling Vector for Fine-Grained Recognition. We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.

Question: What is the difference between the super vector and the selective pooling vector?


T-S Fuzzy Path Controller Design for the Omnidirectional Mobile Robot. This paper focuses on the design of T-S fuzzy path controller for improving the high-speed tracking accuracy of a four-wheel omnidirectional mobile robot (ODMR). Usually, when an ODMR moves in high speed, the adverse effects induced by Coriolis force significantly deteriorate the tracking performances. In this paper, a T-S fuzzy control designed with both the state feedback and path tracking error is proposed to meet the design specifications. Moreover, a new approach of kinematics inversion considering the nonlinear term of coordinate transform is proposed to simplify the analysis of ODMR motion. Finally, an example is provided to compare the present T-S fuzzy path controller with the well-tuned PID controller. The simulation results indicate that the proposed approach provides much better tracking performances as the speed of ODMR increases

Question: How to design a fuzzy controller for a robot?


MusicMixer: computer-aided DJ system based on an automatic song mixing. In this paper, we present MusicMixer, a computer-aided DJ system that helps DJs, specifically with song mixing. MusicMixer continuously mixes and plays songs using an automatic music mixing method that employs audio similarity calculations. By calculating similarities between song sections that can be naturally mixed, MusicMixer enables seamless song transitions. Though song mixing is the most fundamental and important factor in DJ performance, it is difficult for untrained people to seamlessly connect songs. MusicMixer realizes automatic song mixing using an audio signal processing approach; therefore, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system, enabling effective DJ song mixing and lowering entry barriers for the inexperienced. We also propose personalization for song suggestions using a preference memorization function of MusicMixer.

Question: What is a computer-aided DJ system?


Recognizing human activity in smart home using deep learning algorithm. There is a topic of interest for many researchers about human activities recognition in recent years. In this paper, we propose the research to recognize human activities by deep learning (DL) algorithm. We collected the data from participants performing activities in order to evaluate the human activities recognition results. After pre-training the deep network, the fine-tuning process begins. Compared with Hidden Markov Model (HMM) and naïve Bayes Classifier (NBC), the experiment results show that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.

Question: What is the difference between deep learning and other algorithms?


FURTHER ANALYSIS OF THE HIPPOCAMPAL AMNESIC SYNDROME : 14-YEAR FOLLOW-UP STUDY OF H . M . *. -The report attempts to delineatecertain residual learning capacities of H.M., a young man who became amnesic in 1953 following a bilateral removal in the hippocampal zone. In addition to being able to acquire new motor skills (CoRKIN [2]), this patient shows some evidence of perceptual learning. He also achieves some retention of very simple visual and tactual mazes in which the sequence of required turns is short enough to fit into his immediate memory span; even then, the rate of acquisition is extremely slow. These vestigial abilities, which have their occasional parallels in the patient's everyday life, are assessed against the background of his continuing profound amnesia for most on-going events, an amnesia that persists in spite of above-average intelligence and superior performance on many perceptual tasks. THE PRESENT repor t has three aims. In the first place, it describes the persis tent features of a severe amnesic syndrome acquired 14 years ago, fol lowing bi la tera l mesial t empora l lobec tomy (ScoVILLE [28]). Secondly, the repor t a t tempts to give further substance to our previous ly held belief tha t the pa t ien t ' s perceptua l and intel lectual capacit ies remain intact , as mani fes ted by no rma l or super ior pe r tb rmance on a fair ly wide range o f exper imental tasks. Thirdly , we are explor ing the na ture o f the m e m o r y detect in some detai l by trying to discover which learning tasks the pat ient can master , as c o m p a r e d with those on which he always fails. I N T E R V A L H I S T O R Y Since the onset of his amnes ia in 1953, we have twice had the oppor tun i ty o f br inging this pa t ien t under intensive observat ion. In 1962, he spent one week at the Mont rea l Neuro log ica l Inst i tute, and most results o f these and earl ier examinat ions have a l ready been repor ted (CoRK~N [1]; MILNER [16, 18, 19]). Extensive testin

Question: What is the difference between a memory and a memory test?


Consideration of 10kW in-wheel type axial-gap motor using ferrite permanent magnets. In general, the in-wheel type permanent magnet synchronous motor (PMSM) for electric city commuters uses powerful rare earth permanent magnets (PMs). However, the employment of the rare earth PMs should be reduced due to high prices. Therefore, it is important to develop an in-wheel PMSM that does not use the rare earth PMs. We proposed 5kW in-wheel type axial gap motor that can generate high torque density and has features such as a coreless rotor structure with ferrite PMs and a reduction gearbox in the inner side of the stator. And we have presented details of experimental results on a prototype. So, in this paper, in order to achieve further higher power output, we considered a 10 kW in-wheel motor with the proposed structure. The motor with 10-kW output was examined by means of using 3D-FEM and experimental results in order to attain the further higher power as a rare earth free motor.

Question: What is the rare earth free motor?


Insulin detemir versus insulin glargine for type 2 diabetes mellitus.. BACKGROUND Chronically elevated blood glucose levels are associated with significant morbidity and mortality. Many diabetes patients will eventually require insulin treatment to maintain good glycaemic control. There are still uncertainties about the optimal insulin treatment regimens for type 2 diabetes, but the long-acting insulin analogues seem beneficial. Several reviews have compared either insulin detemir or insulin glargine to NPH insulin, but research directly comparing both insulin analogues is limited.   OBJECTIVES To assess the effects of insulin detemir and insulin glargine compared with each other in the treatment of type 2 diabetes mellitus.   SEARCH STRATEGY We searched MEDLINE, EMBASE, The Cochrane Library, online registries of ongoing trials and abstract books. Date of last search was January 2011.   SELECTION CRITERIA All randomised controlled trials comparing insulin detemir with insulin glargine with a duration of 12 weeks or longer were included.   DATA COLLECTION AND ANALYSIS Two authors independently selected the studies and extracted the data. Pooling of studies by means of random-effects meta-analysis was performed.   MAIN RESULTS This review examined four trials lasting 24 to 52 weeks involving 2250 people randomised to either insulin detemir or glargine. Overall, risk of bias of the evaluated studies was high. Insulin glargine was dosed once-daily in the evening. Insulin detemir was initiated once-daily in the evening with the option of an additional dose in the morning in three studies and initiated twice-daily in one study. Of randomised patients 13.6% to 57.2% were injecting insulin detemir twice-daily at the end of trial.Glycaemic control, measured by glycosylated haemoglobin A1c (HbA1c) and HbA1c equal to or less than 7% with or without hypoglycaemia, did not differ statistically significantly between treatment groups.The results showed no significant differences i

Question: What is the difference between insulin detemir and insulin glargine?


Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. reinforcement learning and approximate dynamic programming for feedback control are a good way to achieve details about operating certainproducts. Many products that you buy can be obtained using instruction manuals. These user guides are clearlybuilt to give step-by-step information about how you ought to go ahead in operating certain equipments. Ahandbook is really a user's guide to operating the equipments. Should you loose your best guide or even the productwould not provide an instructions, you can easily obtain one on the net. You can search for the manual of yourchoice online. Here, it is possible to work with google to browse through the available user guide and find the mainone you'll need. On the net, you'll be able to discover the manual that you might want with great ease andsimplicity

Question: What is the difference between a user's guide and a manual?


IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks. Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks.

Question: What is the influence maximization problem?


Modeling temporal effects of human mobile behavior on location-based social networks. The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.

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


Leveraging the schema in latent factor models for knowledge graph completion. We focus on the problem of predicting missing links in large Knowledge Graphs (KGs), so to discover new facts. Over the last years, latent factor models for link prediction have been receiving an increasing interest: they achieve state-of-the-art accuracy in link prediction tasks, while scaling to very large KGs. However, KGs are often endowed with additional schema knowledge, describing entity classes, their sub-class relationships, and the domain and range of each predicate: the schema is actually not used by latent factor models proposed in the literature. In this work, we propose an unified method for leveraging additional schema knowledge in latent factor models, with the aim of learning more accurate link prediction models. Our experimental evaluations show the effectiveness of the proposed method on several KGs.

Question: What is the difference between latent factor models and latent semantic models?


On Modeling and Predicting Individual Paper Citation Count over Time. Evaluating a scientist's past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited papers is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.

Question: How to predict the citation count of a paper?


H-Ras transfers from B to T cells via tunneling nanotubes. Lymphocytes form cell–cell connections by various mechanisms, including intercellular networks through actin-supported long-range plasma membrane (PM) extensions, termed tunneling nanotubes (TNTs). In this study, we tested in vitro whether TNTs form between human antigen-presenting B cells and T cells following cell contact and whether they enable the transfer of PM-associated proteins, such as green fluorescent protein (GFP)-tagged H-Ras (GFP-H-Ras). To address this question, we employed advanced techniques, including cell trapping by optical tweezers and live-cell imaging by 4D spinning-disk confocal microscopy. First, we showed that TNTs can form after optically trapped conjugated B and T cells are being pulled apart. Next, we determined by measuring fluorescence recovery after photobleaching that GFP-H-Ras diffuses freely in the membrane of TNTs that form spontaneously between B and T cells during coculturing. Importantly, by 4D time-lapse imaging, we showed that GFP-H-Ras-enriched PM patches accumulate at the junction between TNTs and the T-cell body and subsequently transfer to the T-cell surface. Furthermore, the PM patches adopted by T cells were enriched for another B-cell-derived transmembrane receptor, CD86. As predicted, the capacity of GFP-H-Ras to transfer between B and T cells, during coculturing, was dependent on its normal post-transcriptional lipidation and consequent PM anchorage. In summary, our data indicate that TNTs connecting B and T cells provide a hitherto undescribed route for the transfer of PM patches containing, for example, H-Ras from B to T cells.

Question: How does H-Ras transfer between B and T cells?


Implementing database operations using SIMD instructions. Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to accelerate the performance of multimedia applications. SIMD instructions provide new opportunities for database engine design and implementation. We study various kinds of operations in a database context, and show how the inner loop of the operations can be accelerated using SIMD instructions. The use of SIMD instructions has two immediate performance benefits: It allows a degree of parallelism, so that many operands can be processed at once. It also often leads to the elimination of conditional branch instructions, reducing branch mispredictions.We consider the most important database operations, including sequential scans, aggregation, index operations, and joins. We present techniques for implementing these using SIMD instructions. We show that there are significant benefits in redesigning traditional query processing algorithms so that they can make better use of SIMD technology. Our study shows that using a SIMD parallelism of four, the CPU time for the new algorithms is from 10% to more than four times less than for the traditional algorithms. Superlinear speedups are obtained as a result of the elimination of branch misprediction effects.

Question: What is the difference between a database and a database engine?


Cybersecurity Through Secure Software Development. Reports about serious vulnerabilities in critical IT components have triggered increased focus on cybersecurity worldwide. Among the man y initiatives to strengthen cybersecurity it is common to see the establishment and stre gthening of CERTs and other centers for cybersecurity. On the other hand , strengthening education in IT security and applying methods for secure systems d velopment are methods that receive much less attention. In this paper we expla in how the lack of focus on security in IT education programs worldwide is a signifi cant contributor to security vulnerabilities, and we propose an agile method for s ecure software design that requires team members to have received adequa te security education and training.

Question: How can we strengthen cybersecurity?


Retargeting Semantically-Rich Photos. Semantically-rich photos contain a rich variety of semantic objects (e.g., pedestrians and bicycles). Retargeting these photos is a challenging task since each semantic object has fixed geometric characteristics. Shrinking these objects simultaneously during retargeting is prone to distortion. In this paper, we propose to retarget semantically-rich photos by detecting photo semantics from image tags, which are predicted by a multi-label SVM. The key technique is a generative model termed latent stability discovery (LSD). It can robustly localize various semantic objects in a photo by making use of the predicted noisy image tags. Based on LSD, a feature fusion algorithm is proposed to detect salient regions at both the low-level and high-level. These salient regions are linked into a path sequentially to simulate human visual perception . Finally, we learn the prior distribution of such paths from aesthetically pleasing training photos. The prior enforces the path of a retargeted photo to be maximally similar to those from the training photos. In the experiment, we collect 217 1600 ×1200 photos, each containing over seven salient objects. Comprehensive user studies demonstrate the competitiveness of our method.

Question: How to retarget semantically-rich photos?


Color exploitation in hog-based traffic sign detection. We study traffic sign detection on a challenging large-scale real-world dataset of panoramic images. The core processing is based on the Histogram of Oriented Gradients (HOG) algorithm which is extended by incorporating color information in the feature vector. The choice of the color space has a large influence on the performance, where we have found that the CIELab and YCbCr color spaces give the best results. The use of color significantly improves the detection performance. We compare the performance of a specific and HOG algorithm, and show that HOG outperforms the specific algorithm by up to tens of percents in most cases. In addition, we propose a new iterative SVM training paradigm to deal with the large variation in background appearance. This reduces memory consumption and increases utilization of background information.

Question: What is the difference between a specific and HOG algorithm?


MapReduce network enabled algorithms for classification based on association rules.

Question: What is map reduce?


It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation. Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.

Question: How does the full face method compare to the state of the art?


Filtered channel features for pedestrian detection. This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.

Question: What is the best pedestrian detector?


Towards Portable Learning Analytics Dashboards. This paper proposes a novel approach to build and deploy learning analytics dashboards in multiple learning environments. Existing learning dashboards are barely portable: once deployed on a learning platform, it requires considerable effort to deploy the dashboard elsewhere. We suggest constructing dashboards from lightweight web applications, namely widgets. Our approach allows to port dashboards with no additional cost between learning environments that implement open specifications (Open Social and Activity Streams) for data access and use widget APIs. We propose to facilitate reuse by sharing the dashboards and widgets via a centralized analytics repository.

Question: What is a learning analytics dashboard?


Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).

Question: What is the tree-structured long short-term memory network?


Lightweight Cryptography for FPGAs. The advent of new low-power Field Programmable Gate Arrays (FPGA) for battery powered devices opens a host of new applications to FPGAs. In order to provide security on resource constrained devices lightweight cryptographic algorithms have been developed. However, there has not been much research on porting these algorithms to FPGAs. In this paper we propose lightweight cryptography for FPGAs by introducing block cipher independent optimization techniques for Xilinx Spartan3 FPGAs and applying them to the lightweight cryptographic algorithms HIGHT and Present. Our implementations are the first reported of these block ciphers on FPGAs. Furthermore, they are the smallest block cipher implementations on FPGAs using only 117 and 91 slices respectively, which makes them comparable in size to stream cipher implementations. Both are less than half the size of the AES implementation by Chodowiec and Gaj without using block RAMs. Present's throughput over area ratio of 240 Kbps/slice is similar to that of AES, however, HIGHT outperforms them by far with 720 Kbps/slice.

Question: What is the smallest block cipher implementation on FPGAs?


Experience Prototyping. In this paper, we describe "Experience Prototyping" as a form of prototyping that enables design team members, users and clients to gain first-hand appreciation of existing or future conditions through active engagement with prototypes. We use examples from commercial design projects to illustrate the value of such prototypes in three critical design activities: understanding existing experiences, exploring design ideas and in communicating design concepts.

Question: What is experience prototyping?


Extension of Soft-Switching Region of Dual-Active-Bridge Converter by a Tunable Resonant Tank. Hard-switching-induced switching loss can contribute significantly to the power loss of an isolated bidirectional dual-active-bridge (DAB) dc–dc converter operating at high frequency. An <inline-formula> <tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on a switch-controlled-inductor (SCI) is proposed to mitigate the loss arising from hard switching under wide-range variations in output voltage and current. Zero-voltage switching is achieved at the primary side (high voltage), while at the secondary side (low voltage), zero-current switching is preferred to reduce excessive ringing due to circulating current and switching loss. In order to achieve reduced conduction loss, a nominal operating point is chosen where the root-mean-square resonant tank current is the minimum. To validate the proposed topology and modulation scheme, an <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on SCI operating at 100 kHz is designed to interface a 400-V dc bus to a supercapacitor-based energy storage. Simulation and experimental results validate the effectiveness of the proposed topology for charging/discharging a supercapacitor with an output voltage variation of between 10 and 48 V and a maximum rated power of 480 W. A maximum efficiency of 94.6% is achieved using the proposed topology and modulation scheme.

Question: How to design a series resonant DAB converter?


Fast texture synthesis using tree-structured vector quantization. Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

Question: How to generate a texture with a specific pattern?


Epidemic Algorithms for Replicated Database Maintenance. When a database is replicated at many sites, maintaining mutual consistency among the sites in the face of updates is a significant problem. This paper describes several randomized algorithms for distributing updates and driving the replicas toward consistency. The algorithms are very simple and require few guarantees from the underlying communication system, yet they ensure that the effect of every update is eventually reflected in all replicas. The cost and performance of the algorithms are tuned by choosing appropriate distributions in the randomization step. The algorithms are closely analogous to epidemics, and the epidemiology literature aids in understanding their behavior. One of the algorithms has been implemented in the Clearinghouse servers of the Xerox Corporate Internet, solving long-standing problems of high traffic and database inconsistency. An earlier version of this paper appeared in the Proceedings of the Sixth Annual ACM Symposium on Principles of Distributed Computing, Vancouver, August 1987, pages 1-12. CR

Question: What is the name of the paper?


Overview of ImageCLEFcaption 2017 - Image Caption Prediction and Concept Detection for Biomedical Images. This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from the biomedical literature. Two subtasks were proposed to the participants: a concept detection task and caption prediction task, both using only images as input. The two subtasks tackle the problem of providing image interpretation by extracting concepts and predicting a caption based on the visual information of an image alone. A dataset of 184,000 figure-caption pairs from the biomedical open access literature (PubMed Central) are provided as a testbed with the majority of them as trainign data and then 10,000 as validation and 10,000 as test data. Across two tasks, 11 participating groups submitted 71 runs. While the domain remains challenging and the data highly heterogeneous, we can note some surprisingly good results of the difficult task with a quality that could be beneficial for health applications by better exploiting the visual content of biomedical figures.

Question: What is the size of the dataset?


Hiding Behind the Shoulders of Giants: Abusing Crawlers for Indirect Web Attacks. It could be argued that without search engines, the web would have never grown to the size that it has today. To achieve maximum coverage and provide relevant results, search engines employ large armies of autonomous crawlers that continuously scour the web, following links, indexing content, and collecting features that are then used to calculate the ranking of each page. In this paper, we describe how autonomous crawlers can be abused by attackers to exploit vulnerabilities on thirdparty websites while hiding the true origin of the attacks. Moreover, we show how certain vulnerabilities on websites that are currently deemed unimportant, can be abused in a way that would allow an attacker to arbitrarily boost the rankings of malicious websites in the search results of popular search engines. Motivated by the potentials of these vulnerabilities, we propose a series of preventive and defensive countermeasures that website owners and search engines can adopt to minimize, or altogether eliminate, the effects of crawler-abusing attacks.

Question: How can crawlers be abused to attack websites?


A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

Question: What is a particle filter?